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Streptococcus agalactiae is a common human commensal and a major life-threatening pathogen in neonates . Adherence to host epithelial cells is the first critical step of the infectious process . Pili have been observed on the surface of several gram-positive bacteria including S . agalactiae . We previously characterized the pilus-encoding operon gbs1479-1474 in strain NEM316 . This pilus is composed of three structural subunit proteins: Gbs1478 ( PilA ) , Gbs1477 ( PilB ) , and Gbs1474 ( PilC ) , and its assembly involves two class C sortases ( SrtC3 and SrtC4 ) . PilB , the bona fide pilin , is the major component; PilA , the pilus associated adhesin , and PilC , are both accessory proteins incorporated into the pilus backbone . We first addressed the role of the housekeeping sortase A in pilus biogenesis and showed that it is essential for the covalent anchoring of the pilus fiber to the peptidoglycan . We next aimed at understanding the role of the pilus fiber in bacterial adherence and at resolving the paradox of an adhesive but dispensable pilus . Combining immunoblotting and electron microscopy analyses , we showed that the PilB fiber is essential for efficient PilA display on the surface of the capsulated strain NEM316 . We then demonstrated that pilus integrity becomes critical for adherence to respiratory epithelial cells under flow-conditions mimicking an in vivo situation and revealing the limitations of the commonly used static adherence model . Interestingly , PilA exhibits a von Willebrand adhesion domain ( VWA ) found in many extracellular eucaryotic proteins . We show here that the VWA domain of PilA is essential for its adhesive function , demonstrating for the first time the functionality of a prokaryotic VWA homolog . Furthermore , the auto aggregative phenotype of NEM316 observed in standing liquid culture was strongly reduced in all three individual pilus mutants . S . agalactiae strain NEM316 was able to form biofilm in microtiter plate and , strikingly , the PilA and PilB mutants were strongly impaired in biofilm formation . Surprisingly , the VWA domain involved in adherence to epithelial cells was not required for biofilm formation .
Group B Streptococcus ( GBS , Streptococcus agalactiae ) is a common colonizer of the gastro-intestinal and urogenital tracts of up to 40% of healthy individuals [1] . However , in certain circumstances , GBS can become a life-threatening pathogen causing invasive infections in human neonates [2] , [3] . Epidemiological studies have documented how commonly GBS are transmitted from “carrier” mothers to newborn infants [4] . The clinical symptoms of acute GBS disease are pneumonia , septicemia , and meningitis . The lung is the portal of entry in neonatal GBS infections although it possesses a sophisticated array of innate immune mechanisms for defense against infection: mechanical barriers and mucociliary clearance , antimicrobial factors in the airway lining fluid , and resident alveolar macrophages . Thus , adherence to the host pulmonary epithelium is the first step in GBS pathogenesis and experimental studies involving static binding assays indicate that the molecular interactions of S . agalactiae with host cells are complex , involving a variety of surface adhesion molecules [5] , [6] , [7] . Bacterial pili have recently been recognized in several gram-positive bacteria ( for reviews see [8]–[12] ) . In contrast to gram-negative bacteria , gram-positive bacteria assemble pili by a distinct mechanism involving a transpeptidase called sortase . Sortase was first discovered in Staphylococcus aureus and is mostly known for catalyzing the covalent anchoring of LPXTG-containing proteins to the peptidoglycan [13] . Analysis of bacterial genomes revealed a plethora of sortases in almost all gram-positive species with frequently more than one sortase gene per genome [14] . Our previous bioinformatic analysis of sixty-one sortases from complete gram-positive genomes suggested the existence of 4 distinct classes of sortases named A , B , C , and D involved in different functions [15] . The class A sortase is the ubiquitous housekeeping enzyme that anchors LPXTG proteins to the cell wall . The class B , C , and D sortases are specifically involved in iron acquisition , pilus assembly and developmental processes including sporulation [15]–[17] . Sortase-mediated pilus assembly was first demonstrated in Corynebacterium diphtheriae [18] , [19] and these pioneer studies revealed the existence of 3 conserved genetic elements found within the major pilin subunit and necessary for pilus formation: i ) the pilin motif ( WxxxVxVYPK ) ; ii ) the E-box domain ( YxLxETxAPxGY ) ; and iii ) the cell wall sorting signal ( LPxTG followed by a hydrophobic domain and a positively charged tail ) . The current model for pilus assembly is as follows: the major subunit is assembled into a pilus by a cis-encoded sortase that catalyzes the covalent attachment between the conserved pilin motif lysine residue of one subunit with the conserved threonyl residue LPxTG motif of another subunit . In addition , one or more accessory subunits are incorporated into the pilus by an unknown mechanism , but requiring pilus-specific sortase as well as the E-box domain within the major pilin subunit . Then , during a final step , the pilus fiber is covalently linked to the peptidoglycan by either the pilus-specific sortase or the housekeeping sortase . This mechanism of pilus assembly catalyzed by class C sortases has now been demonstrated in several gram-positive pathogens using similar genetic and biochemical analyses [20]–[27] . We previously carried out a detailed structural and functional analysis of the pilus locus gbs1479-1474 ( also referred to as PI-2A [27] in GBS strain NEM316 [22] ) . This locus encodes a pilus composed of three structural subunit proteins Gbs1478 ( PilA ) , Gbs1477 ( PilB ) , and Gbs1474 ( PilC ) whose assembly involves two class C sortases ( SrtC3 and SrtC4 ) . PilB , the bona fide pilin , is the major component; PilC is a minor associated component mainly localized at the base of the pilus; and PilA is the pilus associated adhesin located at intervals along the pilus backbone . We previously showed that PilA mediates adherence of GBS NEM316 to the pulmonary epithelial cell line A549 independently of pilus formation [22] . The apparently paradoxical situation of a pilus that carries the adhesive property and yet is dispensable for binding was reported previously in the Escherichia coli Pap pilus model system [28] and more recently in the pneumococcal pilus [29] . We postulated that , in the absence of pilus , PilA behaves as a classical LPXTG-containing adhesin anchored to the cell wall by the housekeeping class A sortase SrtA to mediate adherence to cultured epithelial cells . Bacteria often exist within natural systems in an entirely different form ( sessile ) from those grown in laboratory conditions ( planktonic ) . Sessile bacteria appear to be protected in hostile environments by growing as colonies embedded in an extracellular matrix of carbohydrate or exopolysaccharide called biofilm . The pattern of biofilm development involves bacterial attachment to a solid surface , the formation of microcolonies , and their differentiation into exopolysaccharide-encased communities to form a mature biofilm . Many gram-negative pathogens use their pili to promote attachment and aggregation to host cells , that eventually develop into mature biofilm resulting in host tissues colonization [30] . Pilus contribution in biofilm formation was recently shown in gram-positive bacteria such as E . faecalis , S . pyogenes ( GAS ) , and S . pneumoniae [11] , [26] , [31] , [32] . In this work , we investigated the roles of the housekeeping sortase A in pilus assembly in GBS and that of the pilus structure to resolve the paradox of a pilus dispensable in adherence assays although containing an adhesin subunit . We characterized the functional role of the von Willebrand adhesion domain found in the PilA adhesin . We adapted a biofilm formation assay for GBS and thus uncovered an essential role of GBS pilus in this process .
Our previous functional characterization of the pilus locus in S . agalactiae [22] raised the question of the role of the housekeeping class A sortase ( SrtA ) in pilus biosynthesis but did not answer it since transcription of the PI-2A pilus locus was dramatically reduced in the srtA mutant of strain NEM316 [22] . This mutant was made by insertion of a promoterless aphA-3 kanamycin cassette within srtA by allelic replacement to generate a strain that synthesizes a truncated SrtA protein deleted of its carboxylic half ( i . e . , 127 out of 248 amino acids ) including the catalytic TLXTC sequence [33] . Complementation of the srtA mutant with the wild-type gene inserted ectopically on NEM316 chromosome did not result in wild-type levels of pilus expression ( data not shown ) , although restoring the correct localization of two model LPXTG proteins , Alp2 and ScpB [33] . To characterize the role of SrtA in pilus synthesis , we therefore constructed a catalytic mutant of SrtA by in frame-modification of the TLXTC signature sequence encompassing the critical cysteyl residue ( TLVTCTDPE to TAAAPGRAE replacement in the catalytic site ) . This new mutant named SrtA* exhibited phenotypes similar to those of the previously characterized SrtA− mutant ( Figure 1A–1C ) . It is unable to anchor the classical LPXTG protein Alp2 on the bacterial surface as shown by immunofluorescence ( Figure 1A ) or by Western blotting ( Figure 1B , left panel ) . The ScpB protein was found in larger amounts in the supernatants of the SrtA− and SrtA* mutants compared to the wild-type strain NEM316 ( Figure 1B , right panel ) . As expected , the binding to human fibronectin- and fibrinogen-coated plates was similarly affected in both mutant strains ( Figure 1C ) . Of note , the surface properties of SrtA− and SrtA* were macroscopically different from that of the parental strain: they bound less to polypropylene- , MaxiSorp- , or glass-matrices and their pellets obtained after centrifugation were smooth ( data not shown ) . We then tested expression levels of the major pilin subunit PilB in the SrtA* mutant by immunoblotting on whole bacteria . As shown in Figure 1D , the level of PilB in the SrtA* mutant was similar to that found in the wild-type strain . To unravel the role of the various sortases in the pilus assembly process , we monitored pilus polymerization in the various GBS sortases mutants by immunoblotting using specific anti-PilB polyclonal antibody ( Figure 2 ) . S . agalactiae isogenic strains were grown to the same optical density ( OD600≈2 ) and the cultures were separated into three fractions ( medium , cell-wall , and membrane ) that were electrophoresed on 4–12% gradient SDS-PAGE and probed with the PilB antiserum ( upper panel ) . The same fractions were also probed with an antiserum raised against the secreted protein Bsp [34] used as an internal loading control ( lower panel ) . Pilus polymers are readily detected in the various fractions of the wild-type strain but , as previously reported [22] , their polymerization requires either SrtC3 or SrtC4 ( Figure 2 ) . PilB monomer could be detected in the culture medium fractions as a band of about 80 kDa , the lower band at 60 kDa being a degradation product . As previously shown , pili are not expressed in the SrtA− mutant ( Figure 2 ) . In the SrtA* mutant , the pilus polymers are only found in the membrane and medium fractions , but not in the cell wall fraction . This result demonstrates that the housekeeping class A sortase is not necessary for pilus polymerization but is absolutely required for anchoring the pilus to the cell wall . The PI-2A pilus of S . agalactiae is composed of three structural subunits PilA ( Gbs1478 ) , PilB ( Gbs1477 ) , and PilC ( Gbs1474 ) . PilB is the major constituent of the pilus fiber; PilC is a minor associated component mainly localized at the base of the pilus; and PilA is the pilus-associated adhesin located at intervals along the pilus backbone [22] , [27] . Immunogold electron microscopy revealed abundant surface staining and pilus structures extending largely beyond the capsule in strain NEM316 ( Figure 3A ) . Using the previously characterized mouse monoclonal antibody S9 directed against the type III capsular polysaccharide [35] , we carried out a triple-labeling experiment to detect simultaneously the PilB pilin , the PilA-associated adhesin , and the capsule . Wild-type ( WT ) and isogenic mutant bacteria were stained with: i ) mouse mAb S9 followed by 5 nm gold-labeled IgG; ii ) with rabbit pAb anti-PilB followed by 10 nm gold-labeled IgG , and iii ) with rabbit pAb anti PilA followed by 20 nm gold-labeled IgG . The mAb S9 decorates the external layer of the capsule [36] and its thickness in strain NEM316 was estimated to be ≈50 nm on ultra-thin sections by transmission electron microscopy ( Figure S1 ) . In the absence of the PilB backbone pilin , the PilA adhesin is found at the cell surface without detectable pili ( Figure 3A , bottom panel ) . As expected , the absence of the PilA accessory protein did not prevent pilus formation and in this mutant pili are even longer than in wild-type strain ( Figure 3A , bottom panel , and Figure S2 ) . Strikingly , in the absence of the PilC ancillary protein , pili are longer but also more extended ( Figure 3A , bottom panel ) . Of note , a significant amount of pili produced by ΔpilA and ΔpilC mutants were released in the culture medium compared to the parental strain ( Figure S2 ) . These immuno electron micrographs were subjected to quantitative analysis and the results are shown in Table 1 . Pili were shown to be longer in both ΔpilA and ΔpilC compared to the wild-type strain . In addition , immunofluorescence analyses clearly shows that pili are not only longer but also thicker in the ΔpilA and ΔpilC ( Figure S2 ) . Immunoblotting analysis on whole bacteria confirmed the specificity of all four antisera ( PilA , PilB , PilC , and S9 ) and showed that in the absence of the pilus backbone ( ΔpilB ) , PilA cannot be detected at the bacterial surface ( Figure 3B ) . PilA accessibility at the bacterial surface is also reduced in the ΔpilC mutant . Previous transcriptional and western blot analyses showed that deletion of pilB or pilC does not affect expression of pilA [22] . Altogether , these results reinforce the idea that pilus integrity is essential for efficient PilA display at the bacterial surface . We previously showed that PilA mediates adherence of S . agalactiae strain NEM316 to the human alveolar epithelial cell line A549 independently of pilus formation [22] . Indeed , the apiliated pilB mutant is as adherent as the wild-type strain to A549 cells ( Figure 4A ) and the role of the pilus fiber in bacterial adhesion therefore remains to be characterized . A major defence in the lung is constituted by the mucociliary clearance apparatus . Goblet and glandular cells beneath the epithelium produce mucus that lines the epithelial layer of the air conducting pathways . Mucus is moved through the conducting pathways as fast as 1 cm/min by bronchial epithelial cell cilia to the trachea and later towards the mouth . We reasoned that surface display of PilA adhesin could be important in more stringent adherent conditions , e . g . , in the presence of liquid fluid mimicking the mucociliary movement in the lung . A major limitation of the standard adhesion model is that it neglects the local fluid mechanic environment encountered in the organism . We therefore examined the role of the various pilus components under defined shear stress condition by analyzing pilA , pilB , and pilC mutants . Adherent human alveolar epithelial cells ( A549 ) were grown on glass slides and placed in a laminar flow chamber observed under an inverted microscope ( for experimental details see [37] ) . S . agalactiae labeled with fluorescent 5-chloromethylfluorescein diacetate ( CMFDA ) was introduced in the chamber under a controlled flow . Before introduction of bacteria little or no fluorescence was detected . A low shear stress value ( 0 . 04 dynes/cm2 ) mimicking the mucus flow in the lung was selected . We showed that all three pilus mutants were significantly decreased for adherence as much as the srtA* mutant under low shear stress ( Figure 4B and Figure S3 ) . The structural component of the pilus is therefore necessary for efficient adhesion in the presence of a shear stress reproducing the conditions encountered by the bacteria in the lung . In silico analysis of GBS PilA adhesin revealed the presence of a von Willebrand factor type A domain ( VWA found at amino acids 228 to 585 ) located upstream from the putative pilin motif ( YPK ) . This VWA domain is flanked by two Cna-B type domain found in a S . aureus collagen-binding surface protein ( Figure 5A ) . However , the Cna-B regions do not mediate collagen binding but forms a stalk that presents the ligand binding domain away from the bacterial surface [38] . VWA domains in extracellular eukaryotic proteins mediate adhesion via metal ion-dependent adhesion sites ( MIDAS ) . Binding of Mn2+ and Mg2+ to the MIDAS region in eukaryotic proteins have been demonstrated by crystallographic structures . Divalent cations were shown to stabilize the α1β1 integrin I domain [39] . Of note , the critical serine and aspartate residues known to interact with divalent cations are conserved in the VWA domain of PilA ( Figure 5A ) . Many homologues have been identified in bacterial genomes but their role have not been characterized [40] . Multiple sequence alignments of prokaryotic and eukaryotic VWA-domains is shown in Figure S4 . We sought to determine whether the VWA domain of PilA was involved in PilA-mediated adherence . To test this hypothesis , we constructed a PilA mutant named ΔVWA in which the first 180 amino acids of the 358 amino acids VWA domain was replaced by a 9-aa residue-long hemagglutinin epitope tag ( HA tag ) allowing the detection of the mutant protein with specific anti-HA monoclonal antibody ( Figure S4 ) . Of note , the putative pilin motif YPK of PilA allowing its incorporation into the pilus fiber is located behind the VWA domain and left intact in the mutant ( Figure 5A ) . This new isogenic pilAΔVWA mutant displayed similar growth caracteristics in Todd-Hewitt broth compared to the parental strain at 37°C ( data not shown ) . Dot-blot analysis on whole bacteria using a commercial anti-HA antibody showed that the HA epitope is located at the bacterial surface of the ΔVWA HA-expressing bacteria . No signal could be detected in the parental strain confirming the specificity of the HA antibody ( Figure 5B ) . Interestingly , introduction of the srtC3C4 mutation in the ΔVWA strain to abrogate pilus polymerization caused the disappearance of the HA signal ( Figure 5B ) . This result strongly suggests that HA detection on the bacterial surface of ΔVWA mutant depends on its incorporation into the pilus fiber . Western-blot analysis of cell wall extracts from isogenic mutants showed the presence of the HA-tagged PilAΔVWA protein in the pilus polymers of the ΔVWA strain but this incorporation is abolished in the ΔVWA/SrtC3C4− mutant where a single 60-kDa protein , i . e . the predicted size of monomeric PilAΔVWA protein , is present ( Figure 5C ) . We also analyzed the interaction between HA-tagged PilA mutant and the major pilin subunit PilB using pull-down experiment with HA agarose beads and immunoblotting with anti-PilB antibody . As shown in Figure 5D , the HA-tagged PilAΔVWA physically interact with PilB polymer in cell wall extracts . No signal was detected with wild-type extracts as control for HA specificity ( data not shown ) . Immunolocalization of the PilAΔVWA-HA protein in the pilus fiber was demonstrated by scanning electron microscopy ( Figure 5E ) . Double-labeling experiments were performed on the parental ΔVWA and its isogenic SrtC3C4− mutant using rabbit anti-PilB polyclonal antibody followed by 10 nm gold-labeled IgG ( thin arrows ) and then with rat anti-HA monoclonal antibody followed by 20 nm gold-labeled IgG ( arrow heads ) . HA staining was detected at various locations including the base and the tip ( Figure 5E ) and was similar to that of PilA staining ( Figure 3A and [22] , [27] ) . No staining was detected in the absence of pilus polymerization in the ΔVWA/SrtC3C4− double mutant ( Figure 5E ) . Altogether these results indicate that the PilAΔVWA protein is produced , folded , and incorporated into the pilus fiber like the intact PilA protein . Finally , we examined the ability of the PilAΔVWA mutant to bind to human epithelial cells from alveolar ( A549 ) and intestinal ( TC7 ) origins . Standard adhesion assays showed that the ΔVWA mutant is strongly reduced for adherence to both A549 and TC7 cell lines compared with the parental strain NEM316 , to a level similar to that obtained with the pilA and the SrtA* mutants ( Figure 6 ) . Collectively , these results show that the VWA domain of PilA is essential for PilA adhesive property . We initially observed that all pilus mutant strains remained in suspension after an overnight culture in Todd-Hewitt broth whereas strain NEM316 sedimented at the bottom of the tube ( data not shown ) . This result suggested a role of the PI-2A pilus locus in bacterial aggregation and possibly in biofilm formation . We thus began to assay the ability of S . agalactiae to form biofilms on microtiter polystyrene plate as previously described [41] . In this assay , staining with 0 . 1% crystal violet ( CV ) for 15 min enables the visualization of attached , sessile cells after bacterial biofilms have formed in microtiter plate wells . Biofilm assays were carried out under various conditions to determine the optimum experimental conditions . Various media ( TH , THY , BHI , LB , RPMI 1640 ) , temperature ( 30°C and 37°C ) , and time points ( 24 to 48 h ) were used in preliminary experiments but only LB and RPMI 1640 media supplemented with 1% glucose at 37°C for 24 h produced uniform biofilms ( data not shown ) . In enriched media such as TH , THY , BHI bacteria grew better than in LB or RPMI media but failed to evenly adhere over the surface , instead forming pellets at the bottom of the well . It appears that a nutritionally rich environment does not favor S . agalactiae biofilm formation on polystyrene but that nutritionally limited environment increases sessile growth . We also compared the ability of S . agalactiae to form biofilms on different surfaces . Polystyrene surface was more suited than polyvinylchloride or glass surfaces on which S . agalactiae adhered poorly . Thus , the optimal conditions to see biofilm formation with strain NEM316 were as follows: overnight culture in Todd-Hewitt medium , dilution in LB medium supplemented with 1% glucose to obtain an initial OD 600 nm of 0 . 05 , inoculation of sterile polystyrene 96-well plate and growth at 37°C for 24 h . Biofilm formation of S . agalactiae strain NEM316 and its isogenic pilus mutants were assayed accordingly ( Figure 7 ) . We hypothesized that the sortase A mutants , unable to attach to the polystyrene surface , would be defective for biofilm formation as recently reported for S . gordonii srtA mutant [42] . Indeed , both S . agalactiae srtA− and srtA* mutants were unable to form biofilm ( Figure 7 ) . We showed that pilA and pilB mutants were as strongly impaired as the srtA mutants for biofilm formation . The pilC mutant that still forms pili was only slighly reduced for biofilm formation . Surprisingly , the pilAΔVWA mutant readily forms thicker biofilm , as compared to the parental strain NEM316 , although it is unable to adhere to epithelial cells ( Figure 6 and Figure 7 ) .
Our previous functional characterization of the pilus locus in S . agalactiae [22] raised two major questions that were addressed in the present work: i ) what is the role of the housekeeping class A sortase ( SrtA ) in pilus biosynthesis and ii ) what is the function of the pilus fiber itself . Indeed , understanding the apparent paradox of a pilus carrying the adhesive property but yet dispensable for adherence remains a major challenge of the field . We previously observed a down-regulation in transcription of the pilus genes in the srtA− mutant and therefore could not test the role of SrtA in pilus biogenesis [22] . In this report , a new srtA mutant ( srtA* ) displaying all characteristics of the srtA− mutant but expressing wild-type levels of PI-2A pilus was constructed . Since high molecular weight polymers of pili were seen in the srtA* mutant , it is clear that the housekeeping sortase A is not involved in the polymerization process . This is in direct contrast to the effects of deleting both pilus-associated sortases SrtC that abrogates the formation of pilus polymers ( Figure 2 ) . Shedding of pilus polymers in the culture medium of the srtA* mutant demonstrate a role of the housekeeping sortase A in the anchoring phase . A similar result was very recently reported in S . agalactiae strain 515 [43] . As previously shown in C . diphtheriae [44] and B . cereus [21] , these results support a two-stage model of pilus assembly where pilins are first polymerized by a pilus-specific sortase and the resulting fiber is then attached to the cell wall by the housekeeping sortase . In contrast , SrtA is dispensable for pilus assembly and localization to the cell wall in S . pneumoniae [45] . Interestingly , the three pneumococcal RrgA , RrgB , and RrgC proteins that assemble into the pilus each have a motif ( YPRTG , IPQTG , and VPDTG respectively ) that is divergent in the first amino acid position of the canonical LPxTG cell wall signature sequence ( CWSS ) recognized by the house-keeping sortase A which could account for differences in sortase specificity . S . agalactiae is a capsulated bacteria and the size of the capsule is subject to phase variation [46] . By immunogold labeling , we visualized the capsule by electron microscopy and showed that the pilus extends beyond the capsule and thus serve as carrier for surface located adhesive clusters of PilA . Thus , pili-associated adhesins , as opposed to those directly linked to the peptidoglycan , can overcome masking by the capsule as demonstrated by immunodetection of PilA on capsulated bacterial surface ( Figure 3B ) . In a capsulated strain , PilA is not detected in the absence of PilB or in the srtC3C4− mutant in which pilus polymerization is abrogated ( Figure 3B , data not shown ) . These results indicate that the pilus structure is necessary for optimal display at the bacterial surface of the PilA subunit that is necessary for adherence to epithelial cells . However , the fact that PilA remains a functional adhesin in the absence of a pilus fiber raises question on the role of this appendage . A similar situation was recently reported in Streptococcus pneumoniae in which RrgA , a minor pilus component , is central in pilus-mediated adherence and disease , even in the absence of polymeric pilus production [29] . As mentioned in this work , it is conceivable that the conventional in vitro adherence assays carried out with immortalized cells culture are not adapted to test the functional benefit provided by a pilus fiber . This was recently demonstrated for the pili of S . pyogenes that mediate specific adhesion to human tonsil and skin epithelial cells [47] . The authors showed that pili were not required for S . pyogenes adhesion to immortalized HEp-2 and A549 cell lines but were indispensable for adhesion to ex vivo tissues and primary human keratinocytes highlighting an important limitation of the currently used adhesion models . We reasoned that surface display of PilA adhesin could be important in more stringent conditions , as for example in the presence of liquid flow mimicking the mucociliary movement in the lung . A major limitation of the standard adhesion model is that it neglects the local fluid mechanic environment encountered in the organism . Using a laminar flow chamber system optimized to study the adhesion of Neisseria meningitidis under low shear stress conditions [37] , we were able to prove the benefit of the S . agalactiae pilus fiber for adherence to human pulmonary epithelial cells A549 ( Figure 4B ) , thus emphasizing the need of employing models that are more relevant to the infectious process when studying bacterial-host interactions . Closer examination of EM micrographs shows a large heterogeneity in pilus structures in the wild-type strain . The composition , the size , but also the diameter of the individual pili appears highly variable and , as described in S . pneumoniae , bundles of individual pili could be also observed ( data not shown ) . In agreement with our previous results [22] , pili are still formed in the pilA− mutant but were longer than those produced by the wild-type strain . This is most probably due to the increased transcription of pilB in the pilA− mutant where a 3-fold increase in pilB expression was measured by qRT-PCR [22] . Synthesis of longer pili by mutants overexpressing the major pilin subunit has been demonstrated in C . diphtheriae [48] . Strikingly , we also observed longer and largely extended pili in the pilC− mutant and a higher amount of PilB polymers were found shed in the culture medium of this mutant in agreement with a role of PilC as the pilus anchor [43] . Again , similar results were obtained recently in C . diphtheriae [49] , a bacterium where the prototype pilus contains a major pilin ( SpaA ) , a tip pilin ( SpaC ) , and a minor pilin ( SpaB ) . Immunoelectron microscopy revealed that when SpaB was absent , the SpaA fibers found in the culture medium and on the bacterial envelope are considerably longer than in the wild-type strain . Incorporation of the SpaB minor pilin in the shaft base serves as the terminal step in pilus polymerization and triggers the concomitant cell wall linkage by sortase A [49] . The von Willebrand Adhesion Domain ( VWA ) has been identified in several prokaryotic proteins but their function remain unknown [40] . We showed here that the VWA domain of PilA is essential for its adhesive function . S . agalactiae strain NEM316 possesses another pilus locus ( PI-1 ) that is not expressed [22] but displayed a genetic organization similar to that of the PI-2A locus . In particular , the putative pilus associated adhesin ( Gbs0632 ) also contains a central VWA domain surrounded by two Cna-B domains and , interestingly , this central domain structure was also found in the pneumococcal pilus-associated adhesin RrgA [29] and in the minor pilin SpaC of C . diphtheriae [50] . Extracellular matrix proteins constitute good ligand candidates for these adhesins and it was recently shown that RrgA interacts with human fibronectin , collagen I , and laminin [51] . However , sequence comparisons revealed that the VWA domain of RrgA shares 59% identity with that of Gbs0632 but only 37% with that of PilA , suggesting that RrgA and PilA have different VWA-binding ligands . In agreement with this idea of different receptor recognized by different VWA domain , SpaC was shown to promote specific adhesion to human pharyngeal cell line D562 [50] . Finally , we investigated the possibility that GBS pili could also play a role in bacterial-bacterial interactions , as shown for E . faecalis , S . pyogenes , and S . pneumoniae [26] , [31] . We demonstrated that all three individual pilus NEM316 mutants were impaired for bacterial aggregation in liquid culture . Importantly , S . agalactiae strain NEM316 was able to form biofilm in microtiter plates under certain culture conditions . We demonstrated that pili are key surface structures involved in biofilm formation and showed that both PilB and PilA , but not PilC , are essential in this process . Surprisingly , the VWA domain required for adherence to epithelial cells was found to be dispensable for biofilm formation on polystyrene plates . This result indicates that the VWA domain is not required for adherence to abiotic surfaces and suggests that it recognizes specific ligand on epithelial cells . These results revealed GBS pili possess dual and non-overlapping functions in participating in biofilm formation and adherence to host cells . Current work aiming at identifying the epithelial receptor of PilA is in progress . Establishing a link between biofilm formation and colonization is the next challenging question requiring the development of an appropriate animal model .
S . agalactiae NEM316 was responsible for a fatal septicemia and belongs to the capsular serotype III . The complete genome sequence of this strain has been determined [52] . Escherichia coli DH5α ( Gibco-BRL ) was used for cloning experiments . S . agalactiae was cultured in Todd-Hewitt ( TH ) broth or agar ( Difco Laboratories , Detroit , MI ) and E . coli in Luria-Bertani ( LB ) medium . Unless otherwise specified , antibiotics were used at the following concentrations: for E . coli - ampicillin , 100 µg/ml; erythromycin , 150 µg/ml; for S . agalactiae - erythromycin , 10 µg/ml; kanamycin , 1 , 000 µg/ml . S . agalactiae liquid cultures were grown at 37°C in standing filled flasks . Standard recombinant techniques were used for nucleic acid cloning and restriction analysis [53] . Plasmid DNA from E . coli was prepared by rapid alkaline lysis using the Qiaprep Spin Miniprep kit ( Qiagen ) . Genomic DNA from S . agalactiae was prepared using the DNeasy Blood and Tissue kit ( Qiagen ) . PCR was carried out with Ampli Taq Gold polymerase as described by the manufacturer ( Applied Biosystem ) . Amplification products were purified on Sephadex S-400 columns ( Pharmacia ) and sequenced with an ABI 310 automated DNA sequencer , using the ABI PRISM dye terminator cycle sequencing kit ( Applied Biosystems ) . In-frame replacement of the VWA by an HA tag domain in pilA ( gbs1478 ) ( O1–O2; O3–O4 ) and modification of the catalytic sequence signature of sortase A gbs0949 ( O5–O6; O7–O8 ) were constructed by using splicing-by-overlap-extension PCR as previously described [22] . Mutants were confirmed by PCR and sequence analysis . The sequences ( 5′ to 3′ ) of the primers were: O1 , ACCAATGAATTCGGGGAAAGTACCGTACCG; O2 , GGCGTAGTCGGGGACGTCGTAGGGGTACGGCTTTTGTTTGTCCACTGGTTTTAC; O3 , TACCCCTACGACGTCCCCGACTACGCCTTGGGTGCATCATATGAAAGCCAATTTGAA; O4 , GGATGAGGATCCTATCGGGGTATAATACTCAGG; O5 , TAAACGAATTCGCAATGCTTTCATAGC; O6 , GGCACGCCCGGGTGCTGCCGCAGTGAGTTGGCTCTTGCCAGGTGT; O7 , GCGGCAGCACCCGGGCGTGCCGAAGCCACAGAACGTATTATTGTG; and O8 , TCTTGGATCCAGTATAGTCATCGTAACGAATAGGC . The human cell lines A549 ( ATCC CCL-185 ) from an alveolar epithelial carcinoma and TC7 clone [54] established from the parental colon adenocarcinoma Caco-2 , were cultured in Quantum 286 Medium ( PAA ) . Cells were incubated in 10% CO2 at 37°C and were seeded at a density of 2 to 5×105 cells per well in 24-well tissue culture plates . Monolayers were used after 24–48 h of incubation . Bacterial cultures from overnight cultures OD600 of 2 ( approximately 6 108 CFU/ml ) were washed once in PBS and resuspended in DMEM . Cells were infected at a multiplicity of infection ( M . O . I ) of 10 bacteria per cell for 1 h at 37°C in 10% CO2 . The monolayers were then washed four to five times with PBS , and the cells were disrupted by the addition of 1 ml sterile deionized ice-cold water and repeated pipeting . Serial dilutions of the lysate were plated onto TH agar for count of viable bacteria . The percent of adherence was calculated as follows: ( CFU on plate count/CFU in original inoculum ) ×100 . Assays were performed in triplicate and were repeated at least three times . Adhesion under flow was performed as previously described [37] . Before the assay , bacteria were grown overnight in TH broth at 37°C , resuspended at OD600 = 0 . 3 , and labeled for 30 min with the fluorescent marker CMFDA ( Molecular Probes ) at 20 µM on ice . After several washes in PBS , fluorescent bacteria were resuspended in DMEM supplemented with 10% FBS . A549 cells grown on glass slides were placed in the parallel plate flow chamber ( 3 . 3 cm×0 . 6 cm×250 µm , Immunetics , MA , USA ) and sealed with vacuum . About 3×107 fluorescent bacteria were introduced in the laminar flow chamber containing the cells at 0 . 04 dynes/cm2 . Experiments were performed in DMEM supplemented with 2% serum and maintained at 37°C with a heated platform ( Minitub , Germany ) . Medium was introduced into the chamber using a syringe pump ( Vial Medical , Becton-Dickinson or Harvard Apparatus ) . Adhesion of bacteria was recorded using an Olympus CKX41 inverted microscope with a 20× objective , a Hamamatsu ORCA285 CCD camera and the Openlab darkroom software ( Improvision , UK ) . Cell-wall anchored proteins are insoluble in hot SDS unless the peptidoglycan had been first digested enzymatically with mutanolysin . In contrast , membrane anchored proteins are generally extractable in hot SDS without any prior treatment . The assay described by Garandeau et al . [55] was used to study the solubility of PilB polymers in NEM316 and sortases derivatives . The bacteria in 10 ml overnight culture were collected by centrifugation ( 6 , 000 rpm , 4°C , 10 min ) . Medium corresponds to the supernatant that was filter-sterilized and concentrated 10× by ultra filtration on Sartorius vivaspin 20 devices ( cut-off 10 kDa ) . The bacterial pellet was washed in phosphate-buffered saline ( PBS ) , centrifuged , and resuspended in 500 µl of 4% SDS - 0 . 5 M Tris-HCl pH 8 . The bacterial suspension was boiled for 10 min and then centrifuged at 10 , 000 rpm for 5 min . Membrane correspond to the SDS-extracted supernatant and cell-wall to the pellet . These different protein fractions were further analyzed by immunoblotting . For dot-blot analysis on whole bacteria , late-exponentially growing bacteria were washed in PBS and resuspended in adjusted volumes of PBS to get similar OD600 values . The bacteria were loaded on nitrocellulose membrane , dried up for 20 min at room temperature , and then blocked in PBS-milk 5% for 30 min . PilB was detected using a specific rabbit polyclonal antibody obtained previously [22] at 1∶2000 dilution and the HA epitope was detected using the rat monoclonal antibody ( 3F10 ) from Roche at 1∶1000 dilution . The secondary horseradish peroxidase ( HRP ) -coupled anti-rabbit secondary antibody ( Zymed ) was used at 1∶20000 dilution whereas the goat anti-mouse antibody was used at 1∶10000 dilution . Detection was performed using the Western pico chemiluminescence kit ( Pierce ) . Image capture and analysis were done on GeneGnome imaging system ( Syngene ) . For Western blotting analysis , proteins were boiled in Laemmli sample buffer , resolved on Tris-Glycine Criterion XT gradient gels 4–12% SDS-PAGE gels and transferred to nitrocellulose membrane ( Hybond-C , Amersham ) . Protein detection was performed as described above . Immunofluorescence staining of R28/Alp2 and PilB was performed as described [56] using specific rabbit polyclonal antibodies revealed with an anti-IgG coupled to Alexa 488 ( Molecular Probes , OR ) . Microscopic observations were done on a Nikon Eclipse E600 and images acquired with a Nikon Digital Camera DXM1200F . Bacteria ( 50 ml ) were grown in TH medium at 37°C for 18 hours and harvested for preparation of cell wall extracts . Bacteria were washed once in PBS and resuspended in the mutanolysin digestion buffer to get an OD600 of 100 ml−1 ( 50 mM Tris-HCl pH 7 . 3 , 20% sucrose and protease inhibitor cocktail ( Roche ) ) . Mutanolysin ( Sigma ) dissolved to 5000 U ml−1 in potassium buffer ( 10 mM pH 6 . 2 ) was then added to the bacterial suspension to give a final concentration of 200 U ml−1 . The digestion was performed for 2 h at 37°C under gentle rotation . After centrifuging at 12 000 g for 15 min at 4°C , supernatants corresponding to the cell wall fractions were transferred to clean tubes . 25 µl of EZview Red anti-HA affinity gel ( EZview , Sigma ) was added and the samples were rotated overnight at 4°C . Beads were washed five times in solubilization buffer ( 20 mM Tris-HCl , 137 mM NaCl , 0 , 25% NonidetP40 , 1 . 5 mM MagCl2 , 1 mM EDTA , 10 mM NaF ) and resuspended in 20 µl of 2× reducing sample buffer followed by boiling for 5 minutes . Samples were then analyzed by Western blot analysis . For scanning electron microscopy analysis , bacteria were applied to polylysine coated glass coverslips , and fixed with 0 . 1% glutaraldehyde/4% paraformaldehyde in 0 . 1 M Sorensen buffer ( pH 7 . 2 ) for 30 min . Fixed bacteria were incubated in PBS supplemented with 0 . 25% NH4Cl for 20 min then washed extensively with PBS . Samples were incubated in incubated in PBS/BSA 1% for 10 min . Following incubation for 30 min with the primary antibody , samples were washed and incubated for 10 min with the secondary antibody conjugated to colloidal gold . Preparations were washed with PBS and fixed in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer ( pH 7 . 2 ) overnight at 4°C , then washed three times for 5 min ( each time ) in 0 . 2 M cacodylate buffer , post-fixed for 1 h in 1% osmium in 0 . 2 M cacodylate buffer and rinsed with distilled water . Bacteria were dehydrated through a graded series of ethanol ( 25 , 50 , 75 , 95 and 100% ) followed by critical point drying with CO2 . Dried specimens were sputter coated twice with carbon , with a with a GUN ionic evaporator PEC 682 and were examined and photographed with a JEOL JSM 6700F field emission scanning electron microscope operating at 5 kV . Images were acquired from the YAG BSE detector . For transmission electron microscopy , samples were processed as above . After dehydratation in ethanol the samples were embedded in epoxy resina and 70 nm thin sections were prepared and examined using a JEOL JSM1010 microscope operating at 80 kV . For double and triple labeling experiments , the same procedure was applied using the following antibodies: the mouse monoclonal S9 anti-type III capsule ( 1/5 ) , the rat monoclonal anti HA antibody ( clone 3F9 from Roche at 1/100 ) , the rabbit polyclonal α−PilB ( 1/100 ) and the rabbit polyclonal α−PilA ( 1/10 ) . The secondary antibodies were goat anti mouse or goat anti rabbit conjugated to 20 nm- , 10 nm- or 5 nm gold beads . Bacterial attachment and surface growth on polystyrene microtiter plates were studied during growth of S . agalactiae in LB medium supplemented with 1% glucose . Overnight cultures grown in TH were used to inoculate LB glucose medium at OD600 0 . 1 , were vortexed briefly and 180 µl volumes were dispensed into 96-wells plate ( Costar 3799; Corning , Inc . , NY ) followed by incubation at 37°C for 24 h . The OD600 of each culture was measured to ensure that all cells had reached stationary phase with a similar OD600 , and the wells were washed twice in PBS and air-dried for 15 min . Biofilms were stained with 0 . 1% crystal violet for 30 min ( 100 µl per well ) and the wells were washed twice with PBS and air-dried . The stained biomass was resuspended for quantification in ethanol/acetone ( 80∶20 ) and A595 was measured . The assay was performed in quadruplet . | Streptococcus agalactiae ( Group B Streptococcus ) is a leading cause of sepsis ( blood infection ) and meningitis ( brain infection ) in newborns . Most bacterial pathogens have long filamentous structures known as pili or fimbriae , which are often involved in the initial adhesion of bacteria to host tissues but also in bacteria–bacteria interactions , resulting in biofilm formation . Our previous functional characterization of the pilus locus in S . agalactiae showed that it encodes a major pilin and two minor pilin subunits that are covalently polymerized by the action of two enzymes belonging to the sortase C family . One of the accessory pilins is responsible for the adhesive property of the pilus . However , this initial study raised two major questions that were addressed in the present work: i ) what anchors the pilus to the cell wall and ii ) what is the function of the pilus fiber itself . We showed that the pilus is essential for optimal display of the pilus-associated adhesin and overcomes the masking effect of the capsule . Pilus integrity was shown to be critical in adherence assays under flow conditions . We also report that GBS can form biofilms and that pili play an important role in this process . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] | 2009 | Dual Role for Pilus in Adherence to Epithelial Cells and Biofilm Formation in Streptococcus agalactiae |
In eukaryotes , the highly conserved U3 small nucleolar RNA ( snoRNA ) base-pairs to multiple sites in the pre-ribosomal RNA ( pre-rRNA ) to promote early cleavage and folding events . Binding of the U3 box A region to the pre-rRNA is mutually exclusive with folding of the central pseudoknot ( CPK ) , a universally conserved rRNA structure of the small ribosomal subunit essential for protein synthesis . Here , we report that the DEAH-box helicase Dhr1 ( Ecm16 ) is responsible for displacing U3 . An active site mutant of Dhr1 blocked release of U3 from the pre-ribosome , thereby trapping a pre-40S particle . This particle had not yet achieved its mature structure because it contained U3 , pre-rRNA , and a number of early-acting ribosome synthesis factors but noticeably lacked ribosomal proteins ( r-proteins ) that surround the CPK . Dhr1 was cross-linked in vivo to the pre-rRNA and to U3 sequences flanking regions that base-pair to the pre-rRNA including those that form the CPK . Point mutations in the box A region of U3 suppressed a cold-sensitive mutation of Dhr1 , strongly indicating that U3 is an in vivo substrate of Dhr1 . To support the conclusions derived from in vivo analysis we showed that Dhr1 unwinds U3-18S duplexes in vitro by using a mechanism reminiscent of DEAD box proteins .
Ribosome biogenesis is fundamental to cellular growth . In bacteria that have undergone extreme genome reduction , ribosomes are apparently assembled without the use of specialized assembly factors [1] , indicating that the information needed for the correct rRNA folding and protein assembly is intrinsic to the ribosomal components themselves . Similarly , functional bacterial ribosomes can be assembled from purified components in vitro [2 , 3] . Despite their general conservation of structure , eukaryotic ribosomes require a large number of protein and RNA trans-acting factors that assist in their assembly [4 , 5] . A central outstanding question in the field is how RNA-RNA and RNA-protein structural rearrangements , which mark the transition from one step to the next , are directed and regulated . Pre-ribosomal particles initially assemble on the nascent pre-ribosomal RNA ( pre-rRNA ) transcript , which undergoes cleavage to separate the pre-40S and pre-60S complexes . This critical event in ribosome biogenesis requires the U3 small nucleolar RNA ( snoRNA ) . U3 is highly conserved among eukaryotes and base-pairs with multiple sites of the pre-rRNA to coordinate early folding and cleavage events [6–10] . The U3-associated proteins Imp3 and Imp4 promote the U3-pre-rRNA interactions in vitro [11–13] , and are thought to serve a similar role in vivo . U3 binding to the 5′-external transcribed spacer ( 5′-ETS ) and 18S regions of the pre-rRNA is required for the cleavage events at sites A0 within the 5′-ETS , at A1 that generates the mature 5′ end of 18S and at site A2 , within internal transcribed spacer 1 ( ITS1 ) , which separates the earliest pre-40S and pre-60S particles [14 , 15] . Within the 18S rRNA , U3 binds to the sequence close to the 5′ end of the 18S rRNA that will form the 5′ side of the central pseudoknot ( CPK ) , a long range interaction that is a key architectural feature of the small ribosomal subunit ( SSU ) in all domains of life [15] . U3 also has the potential to base-pair to the sequence that will form the 3′ side of the CPK and is located more than 1 Kb away in the 18S rRNA ( Fig . 1 ) , although this interaction has not been experimentally verified . These U3 interactions are believed to both facilitate formation of the CPK and control the timing of this key maturation step . However , U3 must be unwound from the pre-rRNA for CPK formation to occur . Furthermore , in vivo and in vitro studies indicate that the spontaneous dissociation rate of U3–18S interactions in the absence of accessory factors is too slow to support the rates of ribosome assembly observed in vivo [12 , 13 , 16] , suggesting that a helicase is needed . Nineteen RNA helicases are involved in ribosome biogenesis in yeast , 17 of which are essential [17–19] . These helicases are classified as either DEAD or DEAH/RHA enzymes based on conserved sequence motifs . DEAD box proteins do not unwind duplexes in a processive fashion . Rather , ATP-dependent binding to short duplex regions results in duplex destabilization and strand separation . Thus , ATP hydrolysis is not needed for duplex unwinding , but it is required for rapid product release to recycle the enzyme for multiple substrate turnovers . Processivity has also not been observed in DEAH/RHA enzymes but they have been less studied in mechanistic detail [20 , 21] . Identifying in vivo substrates for the RNA helicases has generally been challenging , and specific substrates have not yet been identified for most of the pre-ribosomal helicases . Previous analyses suggested two candidate helicases for the removal of U3 snoRNA from the CPK region . The DEAH helicase Dhr1 ( Ecm16 ) was reported to be associated with U3 [22] , whereas depletion of the DEAD enzyme Has1 leads to retention of snoRNAs , including U3 , in pre-ribosomal particles [23] . Here , we provide genetic , in vivo cross-linking and biochemical evidence that Dhr1 is the helicase that directly displaces U3 from the pre-rRNA to permit formation of the CPK .
In a previous analysis of RNA helicases involved in SSU biogenesis in yeast , conserved motifs were systematically mutated to generate mutants defective in ATP binding and/or hydrolysis [18] . Over-expression of Dhr1 with a Lys420 to Ala mutation ( Dhr1K420A ) in the Walker A motif gave a dominant negative lethal phenotype , and inhibited pre-rRNA processing , primarily at sites A1 and A2 [18] . The dominant negative phenotype implies that this mutant efficiently competes with wild-type ( WT ) protein , possibly by binding unproductively to its substrate , but no specific RNA substrate was identified . We hypothesized that Dhr1 is involved in dissociation of U3 from the pre-rRNA and that the Dhr1K420A mutant might block dissociation of the U3 complex . To test this hypothesis , we ectopically expressed c-myc epitope-tagged , WT Dhr1 and the Dhr1K420A mutant proteins in cells in which genomic DHR1 gene was under control of the GAL1 promoter and could be rapidly depleted by growth in glucose . This PGAL1 HA-DHR1 strain was unable to grow on glucose-containing medium ( S1A Fig . ) and HA-tagged Dhr1 was depleted to levels below detection within 6 h of repression by glucose ( S1B Fig . ) . DHR1–13myc fully complemented loss of DHR1 , whereas dhr1K420A-13myc was unable to support growth ( S2A Fig . ) . To identify the function of Dhr1 , extracts from cells expressing Dhr1–13myc or Dhr1K420A-13myc were fractionated by sedimentation through sucrose density gradients . A strong 40S biogenesis defect in the mutant polysomes profile was evident from the loss of free 40S subunits and reciprocal increase in free 60S subunits in the dhr1K420A mutant compared to WT ( Fig . 2A and 2B ) . This reduction in free 40S was also evident from loss of Rps8 ( eS8 ) from the 40S fraction in the dhr1K420A gradient compared to WT ( Fig . 2C and 2D ) . Western blotting for Dhr1 revealed that WT Dhr1 was almost entirely at the top of the gradient ( Fig . 2C , lanes 2 and 3 ) , indicating that the interaction of Dhr1 with pre-ribosomes is either very transient or unstable following cell lysis . In contrast , Dhr1K420A sedimented at ∼45S ( Fig . 2D , lane 7 ) , indicative of stable association with pre-40S particles . The sedimentation at ∼45S was unexpected because Dhr1 was previously characterized as a factor that acts in the context of the 90S processome [15] . The altered Dhr1 sedimentation was accompanied by changes in the sedimentation of U3 ( Fig . 2E , lanes 8–10 in WT and lane 6 in mutant ) and its associated proteins Mpp10 and Imp4 , with a significant fraction of these small nucleolar ribonucleoprotein ( snoRNP ) components co-sedimenting with Dhr1K420A ( compare Fig . 2C lanes 9–11 with Fig . 2D , lane 7 ) . Comparison of pre-rRNAs present in the strains expressing Dhr1 and Dhr1K420A ( Fig . 3A , lanes 1–4 ) revealed the loss of the 27SA2 pre-rRNA , accompanied by accumulation of an aberrant 21S species . The 5′ end of 27SA2 is generated by cleavage at site A2 , whereas 21S is generated by cleavage at sites A1 and A3 in the absence of A2 cleavage . Notably , there was little accumulation of the 23S RNA , which is generated by A3 cleavage in the absence of cleavage at sites A0 , A1 , and A2 , and is commonly seen in 40S subunit biogenesis mutants . These findings show that expression of Dhr1K420A specifically impairs pre-rRNA cleavage at site A2 . A reduced level of 20S pre-rRNA was detected in the mutant ( Fig . 3A , lanes 3 and 4 ) , showing that inhibition of A2 cleavage was not complete . Pre-RNA species present in particles associated with Dhr1–13myc and Dhr1K420A-13myc were compared by immunoprecipitation ( Fig . 3A , lanes 5–8 ) . The mutant particle contained low levels of 35S and 32S pre-rRNAs , and was enriched for 21S and 20S pre-rRNAs ( Fig . 3A ) . 21S RNA represented 36% and 20S represented the remaining 64% of the combined 21S + 20S signal . We used mass spectrometry ( MS ) for a comprehensive analysis of the protein composition of the Dhr1K420A particle ( Fig . 3B; S1 Table ) . Epitope tagged ( 13xmyc ) and untagged Dhr1K420A particles were immunoprecipitated , digested with trypsin , and subjected to MS . The U3 snoRNA is specifically associated with the Mpp10 complex ( Mpp10 , Imp3 , and Imp4 ) and Rrp9 , as well the common box C/D snoRNA binding proteins Nop1 , Nop56 , Nop58 , and Snu13 . All were detected with the exception of Snu13 , which is very small . Among the early binding factors , independently assembled complexes have been defined and termed the UtpA , B , and C complexes and Rrp5 . The MS analysis detected seven of the eight subunits of Utp-A complex , all six components of the Utp-B complex , two Utp-C components , Rrp5 , and 19 of the 33 SSU ribosomal proteins ( r-proteins ) . Notably absent were the late assembling r-proteins , including Rps2 ( uS5 ) , Rps3 ( uS3 ) , and Rps23 ( uS12 ) . The absence of Rps2 , Rps3 , and enrichment of Imp4 and Mpp10 were confirmed by Western blotting ( S3 Fig . ) . Numerous additional 40S biogenesis factors , including the GTPase Bms1 and the putative A2 endonuclease Rcl1 [24] were also present , but late biogenesis factors , such as Ltv1 , were not detected . Surprisingly , nine of the 11 subunits of the nuclear exosome were identified together with its cofactor , the RNA helicase Mtr4 [25] . The presence of the nuclear exosome could reflect its activity in removing the cleaved fragments of the 5′-ETS or recognition of the particle as defective by the nuclear surveillance machinery . The protein and RNA composition of this particle indicates that it is arrested at an early stage in pre-40S assembly , in which U3 and the SSU-processome complex remain associated with the pre-rRNA [26] . Mapping the r-proteins present in the Dhr1K420A particle to the mature 40S structure revealed a remarkable absence of r-proteins surrounding the CPK ( Fig . 3C ) . In particular , Rps2 , the primary r-protein that binds to the CPK , and Rps23 were absent from this particle . Comparison with the bacterial in vivo assembly map [27] revealed that almost all of the primary and secondary binding r-proteins were present but tertiary binding proteins were absent . We propose that the absence of proteins surrounding the CPK allows flexibility in the ribosomal RNA structure to enable access of assembly factors , including Dhr1 and the U3 snoRNA , to the CPK . If Dhr1 is responsible for displacing the U3 from the pre-18S , then U3 should remain base-paired with 18S in the ∼45S particle that accumulates in Dhr1K420A expressing cells . To examine this possibility , we used chemical modification with dimethyl sulfate ( DMS ) , which modifies N1 of adenines and N3 of cytosines , and primer extension . When the pre-18S is base-paired with U3 , A1139 is predicted to be in a standard Watson-Crick A-U base pair and thus protected from DMS ( Fig . 4A top ) [7] . In contrast , in the mature CPK A1139 is involved in a non-Watson-Crick base triple [28] and N1 is susceptible to DMS modification ( Fig . 4A , bottom ) [29] . Thus , the DMS susceptibility of A1139 is diagnostic for whether the CPK has formed or remains base-paired with U3 . Extracts from cells expressing Dhr1 or Dhr1K420A were fractionated by sucrose density gradient sedimentation , and fractions corresponding to ∼45S were collected . We anticipated that in WT cells this fraction would primarily contain mature 40S particles whereas in the mutant , the limited pool of 40S would rapidly recycle into translating ribosomes and the stalled intermediate containing Dhr1K420A would accumulate . Pooled fractions were treated with DMS or mock and purified mature 40S subunits were treated for a control . Modification of A1139 was readily detected in mature 40S subunits ( Fig . 4B , compare lane 5 , no DMS , with lanes 6–8 ) and in 45S fractions from the WT gradient ( Fig . 4B , lanes 9–11 ) . However , A1139 modification was substantially reduced in the mutant ( Fig . 4B , lanes 12–14 ) . The particularly strong signal for A1139 in mature subunits probably reflects the hypersensitivity of this position to DMS , as reported for A915 of the bacterial ribosome ( analogous to yeast A1139 ) [30] . The relative decrease of A1139 modification was quantified and normalized to intensities of nearby peaks that were relatively constant between WT and mutant ( Fig . 4B , lane quantification , far right ) . Reactivity of A1139 to DMS was reduced 80% in the mutant compared to WT . This low reactivity indicates that A1139 remains base-paired with U3 in the Dhr1K420A particle . To identify direct RNA binding sites of Dhr1 , we performed UV cross-linking and analysis of cDNA ( CRAC ) experiments on strains expressing Dhr1 with a tripartite C-terminal tag , consisting of His6—tobacco etch virus protease ( TEV ) cleavage site—protein A ( Dhr1-HTP ) , and untagged Dhr1 as a negative control [31] ( Fig . 5 ) . UV cross-linking of Dhr1 in vivo yielded a strong RNA cross-linked species for Dhr1-HTP but not for the control ( Fig . 5A ) . As other RNA helicases have been shown to be required for the release of snoRNAs from the pre-rRNA , we compared the read density of Dhr1 across all snoRNAs in yeast . The snoRNAs have been divided into two large groups termed box C/D and box H/ACA ( Fig . 5B and 5C ) , on the basis of conserved sequence elements and common proteins . Dhr1 had a strong preference for cross-linking only to the box C/D U3 ( encoded by U3A and U3B , Fig . 5B ) ; no other snoRNA was significantly enriched . Within U3 most of the reads overlapped within the box A motif in the 5′ end of the RNA , with smaller numbers of hits at the 3′ terminal stem and box D ( Fig . 5D ) . In CRAC analyses , the locations of micro deletions or substitutions in the cDNAs indicate the precise sites of protein-RNA cross-linking . These contact sites could be to the helicase active site itself or to other surfaces of the protein that bind to RNA . Deletions in the Dhr1 reads were rare but U29 , C39 , G47 , and A48 were frequently substituted ( Fig . 5E ) , indicating that Dhr1 directly contacts these nucleotides in U3 . Notably , U29 is located in the box A motif directly downstream of the predicted U3 interaction with the 3′ side of the CPK while C39 , G47 , and A48 flank a U3 binding site for the 5′-ETS ( Figs 5E and 6C ) . We also investigated Dhr1 cross-links to pre-rRNA . Dhr1 cross-linking was detectable at many sites on pre-rRNA , but three peaks , in helices H11 , H23 , and H44 in the SSU , were reproducibly identified as strong cross-linking sites in two independent experiments ( S4A and S5 Figs ) . We mapped these sites onto the 18S rRNA based on the yeast ribosome crystal structure [28] . The cross-linking sites are located near the decoding center of the SSU ( at the conjunction of H44 , H45 , and H11 ) and in the platform area ( S4B Fig . ) , consistent with Dhr1 playing a role in formation of the functional center of the 40S subunit . RNA isolated from a control CRAC experiment with the parental strain yielded mainly 25S rRNA sequences that are common contaminants in many CRAC experiments ( S4C Fig . ) [32] . Collectively , the CRAC analysis positions Dhr1 on the U3-snoRNA adjacent to the U3–18S duplex with the pre-rRNA . We hypothesized that cold-sensitive mutations in Dhr1 might stall a reaction intermediate containing the U3–18S duplex . If this duplex is indeed the target of Dhr1 , then mutations in U3 that destabilize the duplex might suppress the dhr1 mutation . Cold-sensitive dhr1 mutants were therefore identified by screening cells expressing randomly mutagenized DHR1 . Yeast cells carrying dhr1-cs2 were strongly cold-sensitive ( S6A Fig . ) , displayed a strong 40S biogenesis defect ( S6B Fig . ) , and , like the K420A mutant , accumulated U3 at the position of ∼45S in a sucrose density gradient ( S6B Fig . ) . Sequencing dhr1-cs2 revealed multiple mutations: E330G , K399E , T422A , A557G , T633A , and K892E . We then randomly mutagenized SNR17A ( encoding U3A ) by highly mutagenic PCR . PCR product was recombined into a U3 expression vector in vivo in a dhr1-cs2 and conditional U3 mutant strain and transformants were screened for improved growth at low temperature . In cases where SNR17A contained multiple mutations , these were separated by subcloning . This screen identified five single point mutations that suppressed the cold-sensitivity of dhr1-cs2 ( Fig . 6A ) . Notably , all of the suppressing mutations mapped to the extreme 5′-end of U3 , from residues G1 to A28 ( Fig . 6B ) , despite the fact that the entire gene was mutagenized . A28G was the most frequently recovered mutation ( present in seven of 12 suppressing mutants ) , with the double mutation of G1A/A28G showing the strongest growth suppression . The position of these mutations was remarkably coincident with our CRAC data in which Dhr1 most strongly protected residues 22 to 55 of U3 immediately downstream of the suppressing mutations ( Fig . 6B ) . Moreover , the residue most frequently substituted in cDNAs from our CRAC analysis was U29 , adjacent to A28 , the residue most commonly mutated in our suppressor screen . Such genetic suppression of a helicase mutant by mutations in an RNA provides strong evidence that the RNA is an in vivo target of the helicase . The above data indicated that Dhr1 directly dissociates U3 from the pre-rRNA in vivo . To test this activity in vitro we expressed Dhr1 with a C-terminal His6 tag in Escherichia coli and purified the recombinant protein . DHR1-His6 fully complemented a dhr1 null mutant in yeast ( S2B Fig . ) , verifying that the tag did not interfere with its function . We first investigated whether Dhr1 shows RNA-stimulated ATPase activity , a hallmark of DEAH/RHA RNA helicases . Dhr1 displayed weak ATPase activity in the absence of added RNA . Addition of single stranded poly ( A ) RNA or U3 snoRNA stimulated this activity 6-fold and 5-fold , respectively ( Fig . 7A ) . To confirm that the observed activities could be ascribed to Dhr1 and not a copurifying contaminant , we generated two active site mutants . The Walker A Dhr1K420A mutant described above is expected to disrupt ATP binding and thus inhibit ATP hydrolysis because the side chain of this lysine is expected to contact the β phosphate of the ATP ( Fig . 7B ) . In contrast , the Dhr1D516A/E517A mutant is expected to disrupt ATP hydrolysis by removing the carboxylic acids in motif II ( Walker B box ) that bind the catalytic metal ion and activate the nucleophilic water molecule ( Fig . 7B ) . RNA-dependent stimulation of ATPase activity was not observed in either mutant ( Fig . 7A ) . Steady state kinetic parameters were determined ( Figs 7C and S7 and Material and Methods ) from the dependence of ATPase activity on input ATP concentration: Km of 106 μM and kcat of 13 min−1 . As expected for a mutant whose main defect is in ATP binding , the Km of Dhr1K420A increased by an order of magnitude , from 106 μM to 1 mM , but kcat decreased by only 4-fold , from 13 to 3 min−1 . As expected for the catalytic mutant Dhr1D516A/E517A , the Km of the Dhr1D516A/E517A mutant remained almost unchanged , 106 μM versus 136 μM , whereas kcat decreased by 7-fold , from 13 to 1 . 8 min−1 . We next tested the unwinding activity of Dhr1 on a U3-ETS2 duplex that mimics one of the three genetically verified U3-pre-rRNA duplexes ( Figs 8 and 9 ) [11–13] . The U3-ETS2 duplex comprises the 3′ hinge of U3 bound to nts 281 to 291 of the 5′-ETS of the pre-rRNA ( Fig . 8A ) . This duplex is required for subsequent U3-pre-rRNA interactions in vivo [7] and forms spontaneously and is stable in vitro [13] . U3-ETS2 duplex unwinding reactions were performed under pre-steady state conditions with an excess of enzyme over the duplex substrate . In addition , the duplex concentration was limiting to minimize duplex reformation after unwinding . Under these conditions , the U3-ETS2 duplex was efficiently unwound by Dhr1 in the presence of ATP but not in its absence or in the presence of ADP when the reaction was monitored at a single ( Fig . 8C , compare lanes 4 , 5 , and 6 ) or at multiple time points ( Fig . 8D ) . These data illustrate that duplex unwinding is ATP dependent . To examine whether ATP hydrolysis is required for unwinding , we carried out reactions under the pre-steady state conditions described above using the ATP binding mutant Dhr1K420A and the catalytically impaired mutant Dhr1D516A/E517A . In the presence of ATP , unwinding activity of Dhr1D516A/E517A was indistinguishable from that of WT Dhr1 ( Fig . 8C , compare lanes 5 and 10 , and Fig . 8D ) . In contrast , no unwinding activity was observed for Dhr1K420A in the presence of ATP or with Dhr1D516A/E517A in the presence of ADP . These results indicate that ATP binding but not hydrolysis is needed for duplex unwinding activity . To test whether product release requires ATP hydrolysis we measured duplex unwinding activity under steady state conditions in which duplex substrate was present in excess over enzyme . To circumvent the problem of the labeled substrate re-annealing , we used excess unlabeled ETS2 in a strand exchange regime [33] . Under these conditions , repeated release of unwound product by Dhr1 is necessary for efficient unwinding . Unwinding by WT Dhr1 was observed under steady state conditions in the presence of ATP . In contrast , neither mutant was active under these conditions in the presence of ATP ( Fig . 8F ) . Thus , two lines of evidence indicate that ATP hydrolysis by Dhr1 is required for product release from the enzyme but not for duplex unwinding . First , the catalytically impaired Dhr1D516A/E517A supported unwinding activity in the presence of ATP under pre-steady state but not steady state conditions . Second , Dhr1K420A , which is impaired in ATP binding , was inactive under both conditions . This requirement of ATP hydrolysis by Dhr1 for efficient enzyme recycling but not duplex unwinding suggests that Dhr1 shares a common mechanism with DEAD box proteins [34] . We also tested the ability of Dhr1 to unwind a second duplex , U3–18S , composed of box A’/A of U3 hybridized to nts 6–22 of 18S ( Fig . 9A ) . We previously developed an in vitro assay for formation of the U3–18S duplex [11–13] using a minimal system containing nts 4–50 of U3 , designated U3 MINI , and nts 6–22 of 18S rRNA to mimic the U3-18S duplex ( Fig . 9A ) . This duplex does not form spontaneously but forms rapidly in the presence of Imp3 , which unfolds stem-loop structures in the U3 and pre-rRNA to expose the sites of hybridization [12 , 13] . Our genetic results indicate that the U3–18S duplex mimics a bona fide Dhr1 substrate . As was observed with the U3-ETS2 duplex , unwinding of the U3–18S duplex was observed in the presence of ATP under pre-steady state conditions , but was not observed either in the absence of ATP or in the presence of ADP ( Fig . 9C , lanes 5–7 , and Fig . 9D ) . Moreover , unwinding activity was observed for the Dhr1D516A/E517A mutant in the presence of ATP , but not for Dhr1K420A with ATP ( Fig . 9D ) . These data suggest that unwinding requires ATP binding but is independent of ATP hydrolysis . Although we expect that rapid product release from the U3–18S substrate requires ATP hydrolysis , we were unable to observe unwinding by Dhr1 under steady state conditions with this substrate . The extent of unwinding of the U3–18S duplex by Dhr1 was less than that observed for the U3-ETS2 duplex reaction , presumably because unwinding by Dhr1 competes with strand annealing promoted by Imp3 , which is present in excess . Imp3 does not appear to contribute to duplex unwinding , irrespective of the presence of ATP in the reaction ( Fig . 9C , lane 4 ) . Moreover , Imp3 binding decreased the RNA-dependent ATP hydrolysis by Dhr1 ( Fig . 7A ) . Because the presence of Imp3 is necessary to maintain the U3–18S duplex [11] , we have not been able to separate RNA unwinding from RNP remodeling activity with this substrate . Thus , it is currently unclear whether the activity of Dhr1 removes Imp3 directly , destabilizes the U3–18S duplex , or both .
RNA helicases generally act on RNP complexes rather than on naked duplex RNA . This is also likely to be the case for Dhr1 , as formation of the U3–18S duplex requires additional proteins [11–13] and occurs in the context of the SSU processome , a highly complex assemblage of RNA and protein [35–37] . The genetic suppression of a dhr1 cold-sensitive mutant by mutations in the region of U3 that base-pairs with the 18S portion of the pre-rRNA provides compelling evidence that the U3–18S duplex is the in vivo substrate of Dhr1 . However , several of the suppressing mutations affect residues that are not predicted to be involved in base-paired interactions . These include nucleotide G1 , at the extreme 5′-end of U3 , C11 , in the bulge between box A’ and box A in the U3–18S duplex , and A28 , adjacent to the duplex predicted to form between box A and nts 1139–1142 of 18S . We suggest that mutations at these positions affect protein-RNA interactions within the pre-ribosome . Partial disruption of these interactions by mutations in U3 may partially overcome defects of a dhr1 mutant . Proteins that may stabilize U3-pre-rRNA interactions include the U3-associated proteins Imp3 and Imp4 . Imp3 is an RNA chaperone that unfolds the 5′-stem loop of U3 to allow its hybridization with 18S in vitro [11 , 13] . The specific function of Imp4 is not known , but it could work in concert with Imp3 to stabilize the short duplex formed between U3 box A and nts 1139–1143 of 18S , on the 3′ side of the CPK ( Figs 1 and 6C ) . We noted that residues C11 and A28 of the U3 contribute to the U3 box A/A’ stem structure that forms when this RNA is not engaged with the pre-rRNA . We considered that Dhr1 might disrupt a competing intramolecular U3 stem-structure , rather than the U3–18S duplex . However , mutation of CGU to UAC at position 36–38 in U3 , predicted to destabilize the base of the box A/A’ stem , did not suppress dhr1-cs mutant . ( S8 Fig . ) We showed that Dhr1 in vitro unwinding activity depends on ATP binding but not hydrolysis ( Figs 8 and 9 ) . In contrast , rapid product release and enzyme recycling requires ATP hydrolysis . This activity differs from the well-characterized viral DExH helicases that require ATP hydrolysis for processive strand displacement [20 , 38] . However , it is similar to the behavior of DEAD box proteins that can destabilize and unwind short duplexes prior to ATP hydrolysis [34] . To our knowledge Dhr1 is the first DEAH/RHA helicase for which the mechanistic steps associated with ATP binding and hydrolysis have been identified . We think it is likely that other DEAH/RHA enzymes utilize a similar mechanism with regards to ATP binding and hydrolysis . Dhr1 cross-linked to U3 at a position immediately 3′ of the U3–18S duplex , identified in our genetic analysis as an in vivo target of Dhr1 . Because Dhr1 does not appear to be a processive helicase , this raises the question of how Dhr1 acts on the adjacent U3–18S duplex . It should be noted that the CRAC cross-links are not restricted to active site residues within Dhr1 . Thus , Dhr1 could be tethered to U3 while disrupting a nearby duplex . Alternatively , Dhr1 may be recruited to the complex by protein interactions that allow it to go through cycles of binding , local duplex unwinding and dissociation , as is the case for DEAD box proteins . Using CRAC experiments , we identified Dhr1 binding sites in 18S rRNA with the highest density of reads in helix 11 . Strong cross-linking signals at U319 and U320 indicate direct contact to those positions . Reads also mapped to helices 23 and 44 ( Fig . 5G ) . In the fully folded mature subunit , RNA elements of the platform as well as the top of helix 44 would appear to block access of a bulky helicase to the region of the CPK . In support of this idea , the rRNA region surrounding the CPK was devoid of r-proteins in the Dhr1K420A particle ( Fig . 3C , left panel ) . In particular , the tertiary binding proteins Rps23 ( uS12 ) [27] , which lies below the decoding center in the mature subunit , and Rps2 ( uS5 ) , which coordinates the CPK , were absent ( S1 Table ) . We therefore envisage that in the U3-bound pre-ribosome , tertiary interactions have not yet been established to allow remodeling of the functional center of the SSU . Notably , intermediates containing an unfolded CPK have also been identified in the bacterial assembly pathway [39] . As Dhr1 acts in the context of the preribosome , perhaps one role of additional SSU processome factors is to hold the rRNA structure in a more open conformation to allow access of U3 snoRNA and enzymes including Dhr1 . Our CRAC data also revealed binding sites that for Dhr1 that are removed from the functional center , the most notable being helix 23 in the platform . This site overlaps the binding site for Rps14 . However , Rps14 was present in the Dhr1K420A particle , indicating that its loading was not blocked . Whether or not Dhr1 plays a role at sites other than in the region of the CPK remains to be determined . In addition , while our results demonstrate that Dhr1 is required for U3 unwinding , they do not exclude the possibility that additional factors participate in this process in vivo , potentially including another helicase , such as Has1 . The pre-40S particles accumulated in the Dhr1K420A mutant strain retain U3 and the SSU processome , but have largely undergone cleavage at sites A1 and A2 . The appearance of high levels of the 21S demonstrates cleavage at site A3 prior to A2 cleavage , strongly indicating delayed A2 processing , and this is supported by the loss of the 27SA2 pre-rRNA in the mutant . We predict that release of U3 normally precedes ( and very likely stimulates ) pre-rRNA cleavage at site A2 . However , in the absence of U3 release some level of pre-rRNA cleavage still occurs . More importantly , it seems clear that the loss of Dhr1 activity not only blocked release of U3 , but also of many r-protein factors of the SSU processome . The isolation of U3 suppressors of the Dhr1 mutants suggests that U3 dissociation by Dhr1 is a key step in triggering release of core components of the SSU Processome .
Strains and plasmids are listed in S2 and S3 Tables . AJY3324 was derived from the heterozygous diploid deletion collection ( Open Biosystems ) . AJY3335 and AJY3711 were made by integrating the KanMX6- PGAL1–3HA cassette from pFA6a-KanMX6-PGAL1–3HA [40] into the DHR1 locus of BY4742 and BY4741 , respectively . AJY3583 was made by individually amplifying the PGAL-SNR17A::URA3 and snr17b∆::LEU2 loci from YKW100 and integrating them into BY4741 . AJY3752 was a haploid spore clone from crossing AJY3335 with AJY3583 . pAJ2312 encoded Dhr1 with the C-terminal extension Leu-Glu-6xHis . Mutations in DHR1 were introduced by site-specific mutagenesis . DHR1 was randomly mutagenized by amplification with Taq DNA polymerase using oligonucleotides AJO1566 and AJO1567 . The PCR product was co-transformed with MscI digested pAJ2593 into AJY3711 . The transformants were selected on synthetic media with galactose as the sole carbon source and lacking uracil . Transformants were screened by replica plating to glucose-containing media lacking uracil at RT . Sequencing dhr1-cs2 revealed multiple mutations: E330G , K399E , T422A , A557G , T633A , and K892E . dhr1-cs2 was introduced into AJY3752 on a Clonat-resistance vector ( pAJ3095 ) . The entire insert containing SNR17A in pAJ2587 was amplified by PCR using M13 forward and reverse primers with Taq DNA polymerase in buffer containing MnCl2 and imbalanced nucleotides to increase the rate of mutagenesis . The PCR product was cotransformed with pAJ2587 , digested with SalI and NcoI to remove the entire SNR17A gene , into AJY3752 bearing pAJ3095 . Transformants were screened for improved growth at 20°C . Sucrose density gradient sedimentation was carried out as described previously [41] . Fractions were precipitated with 10% TCA and proteins were separated on 8% SDS-PAGE gels , transferred to a nitrocellulose membrane and subjected to Western blot analysis . For immunoprecipitations , 250 ml cultures were grown in leu- galactose media to an OD600 of 0 . 08 at 30°C , followed by the addition of 2% glucose and shifted to the appropriate temperature for 6 h . Cells were resuspended in 500 μl of IP buffer ( 100 mM NaCl , 50 mM Tris-HCl [pH 7 . 5] , 1 . 5 mM MgCl2 , 0 . 15% NP40 , 1 mM PMSF , 1 μg/ml leupeptin , 1 μg/ml pepstatin A ) , lysed by vortexing with glass beads , and clarified by centrifugation at 15 , 000g at 4°C . Immunoprecipitation with the TAP tag was performed by incubating extracts with IgG-Sepharose beads ( Amersham IgG Sepharose 6 Fast Flow ) for 2 h at 4°C , followed by TEV enzyme cleavage at 16°C for 2 h . The eluted proteins were precipitated by adding 10% TCA , resuspended in Laemmli buffer and separated on an 8% SDS-PAGE gel . Immunoprecipitation for the 13myc tag was performed by incubating extracts with monoclonal ( 9e10 ) anti-myc antibody ( Covance ) for 2 h at 4°C , followed by addition of Protein A-conjugated beads ( Milipore ) and an additional incubation for 1 h at 4°C . The beads were washed three times and proteins were eluted in Laemmli buffer . Western blotting was done using the indicated antibodies . Cross reaction of anti-Rpl30 antibody was used to detect Rps2 . All RNAs were prepared and Northern blotting was carried out as described previously [41] . Oligonucleotide probes are listed in S4 Table . Cultures of AJY3711 expressing WT Dhr1 ( pAJ2311 ) or Dhr1K420A ( pAJ3081 ) were grown at 30°C in selective medium containing galactose as the sole carbon source . Glucose was added to repress expression of genomic DHR1 and after 6 . 5 h of growth ( OD600 ∼0 . 3 ) , cycloheximide was added to 100 μg/ml final concentration . After an additional 10 min at 30°C cultures were poured over ice and harvested by centrifugation . Extracts were prepared in gradient buffer ( 20 mM HEPES•KOH [pH 7 . 6] , 50 mM KCL , and 10 mM MgCl2 ) containing 100 μg/ml cycloheximide , 1 mM PMSF , and 1 μM each leupeptin and pepstatin by vortexing with glass beads . Extracts were clarified by centrifugation at 15 , 000g and 25 A260 units were loaded onto 7%–47% sucrose gradients prepared in gradient buffer . Samples were centrifuged for 195 min at 40 , 000g in an SW40 rotor . Gradients were fractionated using an ISCO Model 640 . Fractions containing 40S were pooled ( 1 . 8 ml total ) . 300 μl aliquots were quickly warmed to RT and 12 μl of ethanol ( no DMS control ) or DMS diluted in ethanol to 8 . 3% or 4 . 2% ( v/v ) was added ( adapted from [42] ) . After 2 min reactions were quenched by the addition of 15 μl of BME and RNA was precipitated by the addition of 12 . 5 μg tRNA and 2 . 5 volumes of ethanol . Pellets were resuspended in LETS ( 100 mM LiCl , 10 mM EDTA , 10 mM Tris-HCl [pH 7 . 5] , 0 . 2% SDS ) and extracted twice with phenol/CHCl3 , once with CHCl3 , and RNA was precipitated with 10% volume of 5 M LiCl and three volumes of ethanol . Pellets were washed with 80% ethanol and RNA was resuspended in water . Reverse transcription was carried out as described [43] using oligonucleotide AJO1849 . pAJ2158 was used as a template for a DNA sequencing ladder . Samples were analyzed on an 8% denaturing polyacrylamide gel and imaged by phosphoimaging on a Typhoon FLA 9500 . Quantification was done using NIH ImageJ . CRAC was performed as previously described [31] . The sequencing data were processed using pyCRAC [44] . Briefly , adapter sequences were removed using flexbar [45] and reads were collapsed ( pyFastqDuplicateRemover ) to remove potential PCR duplicates . The resulting sequences were aligned to the yeast genome ( ENSEMBL , version EF . 59 ) using novoalign 2 . 07 ( www . novocraft . com ) . Histograms were generated using pyReadCounters and pyPileup . Imp3 was purified as described [13] . WT and mutant Dhr1 proteins were expressed from pAJ2312 , pAJ2396 , or pAJ3257 overnight at 15°C in BL21 Star ( DE3 ) ( Life Technologies ) cells supplemented with a vector , which coded for the rare tRNAArg , tRNAIle , and tRNALeu . Cells were washed once and resuspended with extraction buffer ( 50 mM Tris-HCl [pH 8 . 0] , 500 mM NaCl , 10% [v/v] glycerol , 5 mM BME , 7 units/ml RNase A and 10 units/ml RNase I ) . The extensive RNase treatment ensured the removal of tightly bound RNA . French press was used to lyse cells and cell extracts were clarified for 10 min at 10 , 000g followed by 30 min at 50 , 000g . Supernatant was loaded on a Ni-NTA resin ( Invitrogen ) and washed once with extraction buffer without RNase . The resin was then resuspended with 3 column volumes ( CV ) of extraction buffer and incubated 15 min . The resin was washed extensively with extraction buffer without RNase and protein was eluted with extraction buffer in which NaCl was replaced with 250 mM imidazole . Fractions containing Dhr1 were pooled , supplemented with 1 mM DTT , and applied to SP Hitrap column ( GE Healthcare Life Sciences ) . The column was washed with Buffer A ( 30 mM Tris [pH 8 . 0] , 5% [v/v] glycerol , 5 mM sodium acetate , and 1 mM DTT ) . Protein was eluted with a 21 CV gradient from 0% to 60% buffer B ( buffer A plus 1 M NaCl ) . Dhr1 containing fractions were pooled , dialyzed ( 30 mM Tris [pH 8 . 0] , 10% [v/v] glycerol , 5 mM sodium acetate , 150 mM NaCl , and 1 mM DTT ) , and concentrated to ∼5 μM . Aliquots were flash frozen and stored at −80°C ( S6 Fig . ) . Yield for WT and mutant Dhr1 was approximately 0 . 5 mg/liter . The yeast strain AJY3711 containing pAJ3090 ( dhr1K420A-TEV-13myc LEU2 CEN ) or pAJ3100 ( dhr1K420A LEU2 CEN ) was grown in SD Leu- containing 2% glucose for 6 h to deplete endogenouse Dhr1 . Extracts were prepared and immunoprecipitation was carried out as described under “Immunoprecipitation and Western Blotting” except Protein-G magnetic Beads ( Pierce ) were used . After binding for 2 h , beads were washed three times , resuspended in 100 μl of the extraction buffer , and TEV was added . Samples were incubated for 2 h at 16°C with gentle rotation . The supernatants were layered onto 100 μl sucrose cushions ( 50 mM Tris-HCl [pH 7 . 5] , 100 mM NaCl , 1 . 5 mM MgCl2 , 15% sucrose ) and centrifuged in a TLA100 rotor ( Beckman ) for 15 min at 70 , 000 rpm at 4°C . The pellet was resuspended in 100 mM Tris-HCl ( pH8 ) , 4% SDS . Samples were run into a 7% Mini-Protean TGX polyacrylamide gel ( BioRad ) for 5 min 100 V and stained with Imperial Protein stain ( Thermo Scientific ) . The protein-containing band was diced and prepared for in-gel digestion essentially as in [46] . After standard acetonitrile elution of digested peptides , the gel pieces were swelled in 6 M urea and eluted with 75% acetonitrile . The combined eluate volume was reduced by SpeedVac centrifugation and digested peptides purified on HyperSep C-18 SpinTips ( Thermo Scientific ) . Peptides were separated on a reverse-phase Zorbax C18 column ( Agilent ) using a 5%–38% acetonitrile gradient over 137 min and subjected to nanoelectrospray-ionization tandem mass spectrometry on an LTQ-Orbitrap ( Thermo Scientific ) with parameters as in [47] . Resulting spectra were searched against the UniProtKB YEAST Saccharomyces cerevisiae ( strain ATCC 204508 / S288c ) FASTA using Sequest HT in the Proteome Discoverer v1 . 4 software ( Thermo Scientific ) . High confidence peptide spectrum matches were filtered at <1% FDR using Percolator . Significance parameters were as in [47] . RNA substrates . All RNAs were as previously described [11–13] and were purified by gel electrophoresis and refolded except for the ETS2 and 18S oligomers . ATPase assay . All the reactions were performed at RT . Dhr1 was pre-incubated in reaction buffer ( 20 mM Tris [pH 8 . 0] , 40 mM KCl , 2 mM DTT ) , with either U3 snoRNA or poly ( A ) for 2 min and in some reactions Imp3 was added ( 0 . 5 μM final concentration ) . Reactions were initiated by rapid addition and mixing of equimolar mixture of ATP and MgCl2 with trace [γ-32P]ATP . Final reaction conditions were 1 mM ATP , 1 mM MgCl2 , 0 . 5 μM Dhr1 , 60 mM Tris ( pH 8 . 0 ) , 40 mM KCl , 15 mM NaCl , 4% ( v/v ) glycerol , 2 mM sodium acetate and 2 . 4 mM DTT . For reaction with RNA , final concentration was either 60 μM U3 snoRNA or 4 mg/ml poly ( A ) . Aliquots of the reaction mixtures were withdrawn at different time intervals and quenched by addition of 3 volumes of stop buffer ( 90% [v/v] formamide , 50 mM EDTA ) . To separate reaction products 0 . 8 μl of quenched sample was spotted on thin layer chromatography ( TLC ) plates , dried , and developed for 10 min with developing buffer ( 0 . 8 M acetic acid , 0 . 8 M LiCl ) . The TLC plate was then exposed on a Fuji imaging plate ( BAS 2024 ) , scanned by a Typhoon 9400 ( Amersham Biosciences , GE ) , and quantified using Image Quant TL 7 . 0 ( GE Healthcase Life Sciences ) . Normalized band intensity quantification was displayed using PRISM 6 ( GraphPad , Inc . ) . Kinetic parameters of Dhr1 were determined by fitting the initial ATPase hydrolysis activity dependence on ATP concentration to the Michaelis–Menten equation using PRISM 6 . Unwinding reactions . All unwinding reactions were performed at RT . Pre-steady state U3–18S reactions: to pre-form U3–18S duplex , U3 MINI was incubated with Imp3 for 10 min [12] . After addition of 32P-18S the reaction was further incubated for 30 min . The pre-formed U3–18S duplex was then incubated with Dhr1 for 5 min . Reactions were initiated by rapid addition and mixing of equimolar mixture of ATP and MgCl2 . Final concentrations were 1 mM ATP , 1 . 5 mM MgCl2 , 40 nM U3 MINI , ≤1 . 2 nM 32P 18S , 1 . 2 μM Imp3 , 0 . 5 μM Dhr1 , 60 mM Tris ( pH 8 . 0 ) , 40 mM KCl , 15 mM NaCl , 4% ( v/v ) glycerol , 2 mM sodium acetate , 50 mM urea , 2 . 4 mM DTT , 0 . 2 mg/ml BSA and 0 . 8 units/μl RNasin . Aliquots were withdrawn at different times and quenched on ice . Ice was used instead of a stop solution containing SDS because this detergent removes Imp3 , resulting in the release of 18S RNA . Pre-steady state U3-ETS2 reactions: to form the U3-ETS2 duplex , U3 was first incubated with 32P-ETS2 for 10 min followed by 10 min with buffer supplemented with MgCl2 . The pre-formed U3-ETS2 duplex was incubated with Dhr1 for 5 min and reactions were initiated by rapid addition of 1 mM mixture of ATP and MgCl2 . The final concentrations were 1 mM ATP , 1 . 5 mM MgCl2 , 1 nM U3 snoRNA , ≤0 . 3 nM 32P ETS2 , 0 . 5 μM Dhr1 , 25 mM Tris ( pH 8 . 0 ) , 40 mM KCl , 15 mM NaCl , 7% ( v/v ) glycerol , 2 . 4 mM DTT , 0 . 8 units/μl RNasin , and 0 . 2 mg/ml BSA . Reactions were sampled and quenched by addition of one-half volume of stop buffer ( 150 mM Tris [pH 8 . 0] , 0 . 3% [w/v] SDS , and 150 mM EDTA ) . All unwinding reactions in the strand exchange regime were performed at RT . Steady state U3-ETS2 reactions: the reaction procedure was essentially the same as for pre-steady state reactions described above , but with several differences . Instead of using U3 snoRNA , 5 μM U3 ( 1–76 ) was incubated with 0 . 5 μM ETS2 with trace 32P-ETS2 to form the U3-ETS2 duplex . Activity under these conditions was not observed with full length U3 snoRNA , presumably owing to non-specific binding of Dhr1 to the 3′ region of U3 that includes the K-turn motifs in box B/C and box C/D . After pre-incubating the duplex with 0 . 1 μM Dhr1 , reactions were initiated by rapid addition of 5 μM ETS2 and 6 mM mixture of ATP and MgCl2 . The remaining buffer conditions are the same as those in the pre-steady reactions . The amount of duplex and single stranded RNA for both of the above helicase reactions were resolved by EMSAs [12] . | Ribosomes are intricate assemblies of RNA and protein that are responsible for decoding a cell’s genetic information . Their assembly is a very rapid and dynamic process , requiring many ancillary factors in eukaryotic cells . One critical factor is the U3 snoRNA , which binds to the immature ribosomal RNA to direct early processing and folding of the RNA of the small subunit . Although U3 is essential to promote assembly , it must be actively removed to allow completion of RNA folding . Such RNA dynamics are often driven by RNA helicases , and here we use a broad range of experimental approaches to identify the RNA helicase Dhr1 as the enzyme responsible for removing U3 in yeast . A combination of techniques allows us to assess what goes wrong when Dhr1 is mutated , which parts of the RNA molecules the enzyme binds to , and how Dhr1 unwinds its substrates . | [
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] | [] | 2015 | The DEAH-box Helicase Dhr1 Dissociates U3 from the Pre-rRNA to Promote Formation of the Central Pseudoknot |
Venom recurrence or persistence in the circulation after antivenom treatment has been documented many times in viper envenoming . However , it has not been associated with clinical recurrence for many snakes , including Russell's viper ( Daboia spp . ) . We compare the recovery of coagulopathy to the recurrence or persistence of venom in patients with Russell's viper envenoming . The study included patients with Russell's viper ( D . russelii ) envenoming presenting over a 30 month period who had Russell's viper venom detected by enzyme immunoassay . Demographics , information on the snake bite , and clinical effects were collected for all patients . All patients had serum collected for venom specific enzyme immunoassay and citrate plasma to measure fibrinogen levels and prothrombin time ( international normalised ratio; INR ) . Patients with venom recurrence/persistence were compared to those with no detectable recurrence of venom . There were 55 patients with confirmed Russell's viper envenoming and coagulopathy with low fibrinogen concentrations: 31 with venom recurrence/persistence , and 24 with no venom detected post-antivenom . Fibrinogen concentrations increased and INR decreased after antivenom in both the recurrence and non-recurrence patients . Clinical features , laboratory parameters , antivenom dose and length of hospital were similar for both groups . Pre-antivenom venom concentrations were higher in patients with venom recurrence/persistence with a median venom concentration of 385 ng/mL ( 16–1521 ng/mL ) compared to 128 ng/mL ( 14–1492 ng/mL; p = 0 . 008 ) . Recurrence of Russell's viper venom was not associated with a recurrence of coagulopathy and length of hospital stay . Further work is required to determine if the detection of venom recurrence is due to the venom specific enzyme immunoassay detecting both venom-antivenom complexes as well as free venom .
Snake envenoming is a major public health issue in many resource poor countries in the rural tropics [1] . Understanding the underlying pathophysiology of snake envenoming and the effect of antivenom is essential to improving health outcomes . An important part of investigating snake envenoming is the detection and measurement of venom in human sera , which confirms the type of snake ( diagnosis ) as well as assessing the efficacy of antivenom . As such , venom concentrations are measured before and after antivenom and the absence of venom in blood post-antivenom has been interpreted to mean that sufficient antivenom has been administered and envenoming will resolve . This has been used as an important end-point in recent studies in Australia showing that one vial of antivenom is sufficient for the treatment of all Australian elapids [2] . The persistence or recurrence of venom after antivenom administration has been interpreted as insufficient antivenom being administered and there being ongoing envenoming . The phenomenon of persistent or recurrent venom in patients following antivenom administration has been reported many times for a number of snakes , including Russell's viper ( Daboia russelii and D . siamensis ) [3]–[8] , Malayan Pit viper ( Calloselasma rhodostoma ) [9] , [10] , Carpet viper ( Echis ocellatus ) [11] , ( Echis pyramidum ) [12] , Western Diamond back rattle snake ( Crotalus atrox ) [13]–[15] , and Lancehead vipers ( Bothrops species ) [16] . Most studies have been conducted in Russell's viper because of the importance of this snake in South Asia , and have reported recurrence rates ranging from 7 to 95% [3]–[8] , [17] . To date , the recurrence of venom detected by EIA in sera post-antivenom has been interpreted as a failure of the initial antivenom dose to be effective or sufficient . Most experts usually suggest that there is ongoing absorption of venom from the site of the bite to the systemic circulation due to the large dose of venom injected by vipers [4]–[6] . Although it is often assumed and stated that there is ongoing clinical envenoming or recurrent clinical envenoming associated with the detection of venom post-antivenom , this has never been conclusively proven . In the case of Russell's viper envenoming it is not clear whether there is a recurrence of coagulopathy with the recurrence of venom in the circulation . Russell's viper venom contains factor X and factor V activators which result in the activation of the clotting pathway manifesting as venom induced consumption coagulopathy ( VICC ) [18] , [19] . VICC in Russell's viper envenoming is characterised by a prolonged prothrombin time ( PT ) or international normalised ratio ( INR ) , decreased levels of fibrinogen , decreased levels of factor V , decreased levels of factor X , and elevated d-Dimer concentrations [6] , [20]–[22] . Once antivenom is administered there is a resolution of VICC with normalising of the clotting function times and replenishing of the clotting factors including a gradual increase in fibrinogen levels . We hypothesized that if sufficient antivenom had been administered , then there would be an improvement in clotting function despite persistent or recurrent venom being detected using EIA . The aim of this study was to compare the recovery of VICC in patients with and without venom recurrence/persistence . The recovery of VICC was assessed primarily by the recovery of fibrinogen levels over time .
Patients over 13 years of age who presented with Russell's viper envenoming and coagulopathy from January 2007 to July 2009 were included in the study . Cases were only included if Russell's viper venom was detected in the patients' serum with the Russell's viper venom-specific EIA . We included only patients who had citrate and serum samples collected before antivenom administration , and who had at least three samples collected up to 24 hours after antivenom . The median number of samples collected from the patients was 5 ( Range: 3 to 10 ) . The following data were collected from patients prospectively: age and sex , time of the snakebite , clinical effects ( local effects , coagulopathy , systemic bleeding [haematemesis , bleeding gums or haematuria] , neurotoxicity [ptosis , ophthalmoplegia] and non-specific systemic symptoms ) , antivenom treatment ( timing and dose ) and hospital length of stay . Additional blood samples were collected from all patients on admission and then for at least 24 hours after antivenom treatment . Blood was collected in citrated tubes for coagulation studies and in serum tubes for venom-specific EIA . All samples were immediately centrifuged , aliquotted and frozen at −20°C and then transferred to a −80°C freezer within 2 weeks of collection until the completion of the study . All patients received Indian polyvalent snake antivenom manufactured by VINS Bioproducts Limited ( batch number: ASV 42C/06 , 1030 ) or BHARAT ( batch number: 5346KD4 , LY 55/05 , LY 32/04 , A5307035 ) Serum and Vaccines Limited , India . Both are equine F ( ab′ ) 2 antivenoms . Russell's viper venom concentrations were measured in serum samples by sandwich EIA which has previously been described [23]–[26] . In brief , polyclonal IgG antibodies were raised against Russell's viper ( D . russelii ) venom in rabbits as previously described [27] . These were bound to the microplates as well as being conjugated to biotin for a sandwich EIA with the detecting agent streptavidin-horseradish peroxidase . All samples were measured in triplicate , and the averaged absorbance converted to a concentration by comparison with a standard curve based on serial dilutions of venom using a sigmoidal curve . Prothrombin times ( PT ) , international normalised ratio ( INR ) and fibrinogen concentrations were measured in platelet free citrated plasma samples . All assays were performed using standard coagulometric methods on the Behring Coagulation System ( Siemens , Marburg , Germany ) or Sysmex CA-1500 coagulation analyser ( Siemens , Marburg , Germany ) , respectively . Briefly , the PT was determined by mixing patient plasma and Innovin reagent ( Dade Behring Inc , USA ) and the time taken for fibrin clot formation was measured . The INR was calculated from the PT according to the recommended formula . The fibrinogen concentration was determined by mixing a 1∶10 dilution of patient plasma in Owrens Veronal Buffer , in a 2∶1 ratio with Dade Thrombin Reagent ( Siemens Healthcare Diagnostic Inc , USA ) and measuring the time to fibrin clot formation . The fibrinogen concentration was then determined from the time to clot formation according to a standard curve of serially diluted standard human plasma in g/L . Patients with venom detected after the administration of antivenom , whether after an initial decrease in venom concentrations ( recurrence ) or no initial decrease in venom concentration ( persistence ) , were defined as patients with venom recurrence/persistence . These patients were then compared to patients where venom was never detected after the administration of antivenom . Time to the recovery of fibrinogen concentration and INR , pre-antivenom venom concentrations , clinical effects ( coagulopathy [INR>1 . 5] , neurotoxicity [ptosis] , systemic bleeding and local envenoming ) , number of antivenom doses administered and length of hospital stay , were compared between the venom recurrence or persistent group and the group of non-recurrence patients . Continuous variables ( venom concentrations , time to fibrinogen and INR recovery , and length of hospital stay ) are reported as median values with interquartile ranges ( IQR ) and ranges . Continuous variables were compared with the Mann-Whitney test ( non-parametric ) . All analyses and graphics were done in GraphPad Prism version 6 for Windows , GraphPad Software , San Diego California USA , www . graphpad . com .
Russell's viper envenoming was confirmed in 173 patients by EIA , but only 55 patients were included in the analysis due to adequate numbers of blood samples . In 24 patients , venom was not detected in serum after the administration of antivenom ( Table 1; Fig . 1A ) . In 31 patients there was recurrence or persistence of Russell's viper venom after antivenom administration ( Table 1; Fig . 1B ) . All 55 patients developed VICC with an abnormal INR and low or undetectable fibrinogen in the pre-antivenom or admission blood sample . Clinical features , coagulopathic parameters , pre-antivenom venom concentrations , antivenom dose and length of hospital stay are shown in Table 1 . The severity of the coagulopathy as determined by the median highest INR and median lowest fibrinogen , was similar for patients with recurrence or persistence of venom versus patients without recurrence ( Table 1 ) . Pre-antivenom venom concentrations were significantly higher in patients with venom recurrence or persistence with a median venom concentration of 385 ng/mL ( 16 to 1521 ng/mL ) compared to patients without recurrence/persistence with a median venom concentration of 128 ng/mL ( 14 to 1492 ng/mL; p = 0 . 008; Fig . 2 ) . The median time to recovery of the fibrinogen concentration to 1 g/L was 11 . 5 hours ( 0 . 3 to 34 . 9 h ) in patients with venom recurrence/persistence compared to 12 . 3 hours ( 1 . 8 to 55 . 3 h ) in patients without venom recurrence which was not significantly different ( p = 0 . 75; Figs . 3A and 3B ) . Similarly , the median time to correct the INR to less than 2 was 11 . 8 hours ( 0 . 3 to 32 . 9 ) for patients with venom recurrence or persistence compared to 12 . 3 hours ( 5 . 8 to 55 . 3 h ) in patients without recurrence which was not statistically significant ( p = 0 . 21;Figs . 4A and 4B ) . Patients in both groups were treated with either VINS or BHARAT Indian polyvalent antivenoms and a median of 10 vials of antivenom was given in both groups . However , 14 patients in the recurrence group had multiple doses of antivenom and only two patients in the non-recurrence group had repeated doses of antivenom . Administration of the second dose of antivenom in these patients was based on a positive whole blood clotting test 20 minutes ( WBCT 20 ) . However , in ten out of 14 patients in the recurrence group who had a second dose of antivenom , the coagulopathy had already resolved with a normal INR and fibrinogen , not consistent with the positive WBCT 20 done at the time . The remaining two patients in the recurrence group , and two patients in the non-recurrence group who had additional antivenom , were in the recovery phase of the coagulopathy ( S1 Figure ) . The median length of hospital stay was 3 and 2 days in recurrence and non-recurrence groups , respectively , which was not statistically significant ( Table 1 ) .
This study shows that the measurement of venom post-antivenom , either seen as the persistence of venom or the recurrence of venom , was not associated with ongoing coagulopathy in Russell's viper bites . The time to recovery of fibrinogen , the time to recovery of INR , and length of hospital stay were similar in both the venom recurrence/persistence group and the non-recurrence group . However , pre-antivenom venom concentrations were higher in recurrence/persistence group compared to the non-recurrence group . Recurrence of coagulopathy associated with the Russell's viper venom recurrence has been described previously in single case reports [4] , [5] , [10] . All of these studies have used the WBCT 20 or methods measuring clot quality to provide evidence for recurrence of coagulopathy . In addition , the so-called venom recurrence was recognised many months after the patient was treated and discharged , when the venom assays were done . Ariaratnam et al . selectively describe a single case from a study of 35 patients where the WBCT 20 became abnormal after antivenom administration , thus prompting a second dose of antivenom . This was later found to coincide with the measurement of venom recurrence . However , venom recurrence occurred in the majority of the patients in their study and in 11 patients given only a single dose of 2 g antivenom ( PolongaTab ) , they state that all patients had recurrence and persistence of venom for a mean time of 72 hours , despite all having immediate reversal of systemic envenoming including coagulopathy [4] . A different interpretation of the data is that Ariaratnam et al show that recurrent venom antigenaemia is not associated with coagulopathy in the majority of their cases , similar to our study , and in one case only there was an abnormal WBCT 20 at the time of the recurrence . This single abnormal WBCT 20 was potentially an error and the investigators did not confirm coagulopathy with other clotting studies . Lack of sensitivity of WBCT 20 for the detection of Russell's viper coagulopathy has been recently demonstrated [28] , and is the probable explanation for the recurrence of coagulopathy in previous reports [4] , [10] as well as the 10 abnormal WBCT 20 that occurred in our study . The case reported by Theakston is a single case in which the reported venom concentrations were very low ( <15 ng/mL ) compared to ours and other studies measuring Russell's viper venom [3] , [4] , [6]–[8] , [29] . Ho et al report a number of cases but there is no correlation between the recurrence of venom and the recurrence of an abnormal WBCT 20 ( Table 3; Ho et al . ) , except in one patient . This again suggests that recurrent coagulopathy did not occur with the measurement of venom recurrence [10] . Recurrence of coagulopathy in Russell's viper envenoming has not been described in any of the studies that used formal laboratory coagulation studies to assess the coagulopathy including fibrinogen concentration or clotting factor assays [6] , [21] , [30] , [31] . Venom recurrence has been documented with other vipers [10]–[13] , [15] , [16] . In a number of cases this is similar to previous studies of Russell's viper where there is little evidence to support recurrence of clinical envenoming . However , for some snakes there appears to be recurrence of clinical and/or laboratory envenoming at the same time as there is venom recurrence , but in these cases the recurrence appears to occur days after the bite , unlike the 12 to 24 hours in our study [15] . This is demonstrated clearly in a phase II clinical trial by Boyer et al of American vipers where the venom recurrence occurs approximately one week after the administration of antivenom in patients given Fab antibodies compared to no recurrence in those given F ( ab′ ) 2 antibodies [15] . The recurrence reported in American viper envenoming appears to be different to that seen with reports from Asian and African vipers . It occurs much later and has only been recognised with the change from an F ( ab′ ) 2 antivenom to an Fab antivenom . The recurrence is thought to be due to the rapid clearance of Fab and ongoing persistence of venom . Persistent coagulopathy has been reported in North American Crotalid envenoming for many years [32]–[37] and is thought to be due to slow absorption of venom . Therefore , the mismatch of Fab antivenom pharmacokinetics with rapid elimination and the slow venom absorption are the likely reason for the recurrence of coagulopathy . The recent study by Boyer et al confirms this [15] . It has always been assumed that venom specific EIA only detects free venom . It reasonably follows that the presence of free venom detected after antivenom administration means that insufficient antivenom has been given . A recent study has shown that in fact the traditional venom specific EIA ( as originally developed by Theakston [38] ) can detect bound venom or venom-antivenom ( VAV ) complexes under certain conditions [39] . If there are high concentrations of antivenom such that venom molecules are surrounded by antivenom and there is excess free antivenom , no venom is detected by the traditional venom EIA . However , at lower concentrations of antivenom ( lower ratio of antivenom to venom ) , where every venom component is not completely surrounded by antivenom but attached to at least one antivenom molecule or antibody on average , the traditional venom EIA will detect bound venom or VAV complexes ( see figure 1 O'Leary and Isbister 2014 [39] ) . It was therefore proposed that the detection of venom in sera post-antivenom may not mean that insufficient antivenom has been administered , because the assay is detecting bound venom . This is not surprising since it is well known that the assay for digoxin will detect digoxin bound to digoxin antibodies after the administration of digoxin antibodies [40] , [41] . This VAV assay was developed with F ( ab′ ) 2 antivenom [39] . We found that patients with persistence or recurrence of venom had significantly higher pre-antivenom venom concentrations ( Table 1 ) . This is consistent with the hypothesis that the venom assay detects VAV complexes , because patients with a high venom load will have a lower ratio of antivenom to venom molecules and therefore , VAV complexes that can be detected ( Fig . 1B ) . In contrast , patients with lower concentrations of Russell's viper venom will have the venom molecules completely surrounded by the same dose of antivenom , so VAV complexes cannot be detected and there is no apparent venom recurrence with EIA ( Fig . 1A ) . Clearly , if there is insufficient antivenom there will be persistence of free venom in addition to VAV complexes . Further assays need to be developed to help distinguish free venom from bound venom to confirm that there is no free venom present in samples collected post-antivenom in our study . An unusual finding in the study was that repeat dosing of antivenom was more common in the group with venom recurrence or persistence . This could be interpreted to mean that patients with recurrence were given further antivenom because of suspected recurrent or ongoing envenoming or coagulopathy . This is partly correct because these patients still had an abnormal WBCT 20 which was the likely reason for a second dose of antivenom being given . However , all of these patients had a normal INR or an improving INR showing that the coagulopathy was in fact resolving , and that the WBCT 20 was incorrect . Another difference between to two groups was that patients with persistence or recurrence were more likely to have neurotoxicity and systemic bleeding . This is again consistent with this group having higher venom concentrations and it therefore making sense that they had more severe envenoming . Both systemic bleeding and neurotoxicity ( mostly ptosis ) do not rapidly reverse with antivenom , so their persistence after antivenom does not indicate recurrent envenoming . In fact , this also further explains why repeat doses of antivenom were given in this group because of the common misconception that neurotoxicity will immediately resolve with antivenom , and if it hasn't , the patient needs more antivenom . Our study shows that the recurrence or persistent of venom post-antivenom does not necessarily mean that insufficient or inefficacious antivenom has been given . For the doses of antivenom given in these patients with Russell's viper envenoming , sufficient antivenom had been given and the coagulopathy was recovering . Further work is required to clarify the best measures of antivenom efficacy in vivo . | Snakebite is a major public health problem and understanding the effectiveness of antivenom is essential to improving health outcomes . The measurement of venom in blood has been used to assess the effectiveness of antivenom . The absence of venom post-antivenom indicating that sufficient antivenom has been given , and the persistence or recurrence of venom indicating that insufficient antivenom has been given . There are numerous reports of venom recurrence with viper bites , including Russell's viper bites . However , it remains unclear if venom recurrence is always an indicator of inadequate antivenom and recurrence of clinical envenoming . In this study , we compare patients with and without the persistence or recurrence of venom who develop coagulopathy after Russell's viper bites . There was no difference in the recovery of the coagulopathy between the two groups of patients demonstrating that for Russell's viper envenoming , venom recurrence or persistence was not associated with the recurrence or persistence of clinical effects such as coagulopathy . Patients with detectable venom after antivenom did have higher pre-antivenom venom concentrations . Further investigation is required to interpret venom concentrations post-antivenom . | [
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"... | 2014 | Detection of Venom after Antivenom Is Not Associated with Persistent Coagulopathy in a Prospective Cohort of Russell's Viper (Daboia russelii) Envenomings |
Foot-and-mouth disease ( FMD ) virus causes an acute vesicular disease of domesticated and wild ruminants and pigs . Identifying sources of FMD outbreaks is often confounded by incomplete epidemiological evidence and the numerous routes by which virus can spread ( movements of infected animals or their products , contaminated persons , objects , and aerosols ) . Here , we show that the outbreaks of FMD in the United Kingdom in August 2007 were caused by a derivative of FMDV O1 BFS 1860 , a virus strain handled at two FMD laboratories located on a single site at Pirbright in Surrey . Genetic analysis of complete viral genomes generated in real-time reveals a probable chain of transmission events , predicting undisclosed infected premises , and connecting the second cluster of outbreaks in September to those in August . Complete genome sequence analysis of FMD viruses conducted in real-time have identified the initial and intermediate sources of these outbreaks and demonstrate the value of such techniques in providing information useful to contemporary disease control programmes .
Foot-and-mouth disease ( FMD ) is an economically devastating vesicular disease of domesticated and wild cloven-hoofed animals . FMD is caused by a 30 nm un-enveloped virus belonging to the genus Aphthovirus in the family Picornaviridae . Its genome consists of a single strand of positive-sense RNA approximately 8 . 3 kb in length [1] encoding a single polyprotein which is post-translationally processed by virally-encoded proteinases [2] . FMD viruses ( FMDV ) are divided into seven immunologically distinct serotypes known as O , A , C , South African Territories ( SAT ) 1 , SAT 2 , SAT 3 and Asia 1 . FMDV has a high mutation rate resulting in rapid evolution and extensive variation between and within serotypes [3] . The molecular epidemiology of FMDV has been extensively studied [4] , [5]; and has allowed the tracing of outbreak origins on a global scale [4] . Most of these studies have been conducted using nucleotide sequences of one of the three major capsid-coding genes ( VP1 ) which represents less than 10% of the genome . However , VP1 sequence data alone does not have the required resolution for within-epidemic transmission tracing . In common with some other RNA viruses , for example , human immunodeficiency virus ( HIV ) [6] , hepatitis C virus ( HCV ) [7] and SARS coronavirus [8] , full genome sequence for FMDV has recently been used for high-resolution molecular epidemiological studies [9] . To date , fine scale tracing of pathogen transmission has focussed on retrospective analysis; production of full-genome sequences during the course of an outbreak ( in real-time ) may assist in the interpretation of field epidemiology data and directly influence measures to control the spread of the disease . The UK 2007 FMD outbreaks have been characterised by the emergence of two temporally and spatially distinct clusters . Eight infected premises ( IP1-8: designation of IP numbering is according to The Department for Environment , Food and Rural Affairs [Defra] , UK ) have been identified ( Figure 1 and Table 1 ) , two in August and six in September . The first case ( IP1b ) was recognised in beef cattle in a field off Westwood Lane , Normandy , Surrey , UK . Samples collected on 3rd August 2007 from animals exhibiting suspect clinical signs were submitted to the World Reference Laboratory for FMD located at the Institute for Animal Health ( IAH ) , Pirbright , Surrey . Within 24 hours , FMDV sequence data obtained from the first IP ( holding IP1b ) revealed a VP1 gene-identity of 99 . 84% to FMDV O1 British Field Sample 1860 ( O1 BFS 1860 ) ; intratypic identities between type O VP1 sequences may be as low as 80% [4] . O1BFS 1860 is a widely used reference and vaccine strain , originally derived from bovine tongue epithelium received at the World Reference Laboratory for FMD at Pirbright in 1967 from a farm near Wrexham , England . The Pirbright site , comprising the laboratories of the IAH and Merial Animal Health Limited ( Merial ) , is situated 4 . 4 km from the first IP . Both laboratories were working with the O1 BFS 1860 virus strain , making this site a likely source of the outbreak . Three days after the case at IP1b , a second infected premises ( IP2b ) was identified at Willey Green , approximately 1 . 5 Km from IP1b . Cattle at a further holding ( IP2c ) near to and under the same ownership as IP2b were found to be incubating disease at the time of slaughter . Animals on both the affected farms were destroyed and the premises were disinfected . Subsequent clinical and serological surveillance within a 10 km control zone found no evidence of further dissemination of FMD . However , on 12th September 2007 , five weeks after the IP1 and IP2 cattle had been culled , FMD was confirmed on the holding of a new IP ( IP3b ) situated outside the 10 km control zone surrounding IP1 and IP2 ( Figure 1 ) . FMD outbreaks were subsequently reported on an additional holding ( IP3c ) and five more premises ( IP4 , 5 , 6 , 7 and 8 ) all located close to IP3b and outside the original surveillance area ( Figure 1 ) . These outbreaks of FMDV in the UK during August and September 2007 have caused severe disruption to the farming sector and cost more than one hundred million pounds . Investigating and determining the source of these outbreaks has been imperative for their effective management and is vital for future prevention . The aim of this study was to trace FMDV movement from farm-to-farm by comparing complete genome sequences acquired during the course of the epidemic . These “real-time” analyses helped to determine the most likely source of the outbreak , assisted ongoing epidemiological investigations as to whether these field cases were linked to single or multiple releases from the source , and predicted the existence of undetected intermediate infected premises that were subsequently identified .
The UK 2007 FMD outbreaks were characterised by the emergence of two temporally and spatially distinct clusters . The genetic relationships of FMDV present in eleven field samples from the 2007 outbreak , three cell culture derived laboratory viruses ( see Table S1 ) used at the Pirbright site during July 2007 ( designated IAH1 , IAH2 and MAH ) and a published sequence of O1 BFS 1860 ( AY593815 ) are illustrated in Figure 2A . Whereas IAH1 and the virus from which the published sequence was derived are believed to have been passaged no more than ten times in cell cultures , the IAH2 and MAH viruses had been extensively adapted to grow in a baby hamster kidney cell line ( Table S1 ) . In natural hosts , FMDV attaches to integrin receptors on the cell surface [10] . However , when grown in cell cultures , the virus may adapt to attach to heparan sulphate ( HS ) , through acquisition of positively charged amino acid residues on the virus coat at positions VP2134 and/or VP356 [11] , [12] . An additional change from a negatively charged amino acid residue at VP360 to a neutral residue often occurs but may not be essential for HS binding [11] . IAH1 and the previously sequenced isolate of O1 BFS 1860 have lysine at VP2134 , histidine at VP356 and aspartic acid at VP360 , none of the residues associated with HS binding , whereas substitutions at VP356 ( arginine ) and VP360 ( glycine ) are present in MAH and IAH2 , consistent with their history of extensive culture passage ( Table 2 ) . The presence of the HS binding-associated substitution at residue VP360 ( aspartic acid to glycine ) in all but one of the field viruses provides evidence that a cell culture adapted virus is an ancestor of the outbreak . Since this residue is not critical for HS binding it is less likely to undergo reversion [11] , [12] . The wild type configurations at VP356 in all of the outbreak viruses and at VP360 in the IP5 virus most likely reflect reversions that have been selected upon replication within the animal host . It is known that there is a strong selection pressure for the reversion at VP356 when FMDV replicates in cattle [11] . The viruses from the outbreaks differ by at least five unique synonymous substitutions from the laboratory viruses examined ( Table 2 , Figure 2A ) . In terms of nucleotide substitutions , two very closely related laboratory viruses ( MAH and IAH2 ) are closest to the sequence of the virus from IP1b ( 6 and 7 substitutions , respectively ) compared with IAH1 ( 12 substitutions ) . Viruses IAH2 and MAH differ by only one non-synonymous change at amino acid residue 2 of the Leader-b ( Lb ) polypeptide ( a papain-like cysteine proteinase ) ( Table 2 ) . Since FMDV is known to exist as variant populations of genetically related viruses [3] , it is possible that virus containing the MAH consensus sequence was present as a minority component within the virus population of IAH2 . It is also possible that a reversion of the amino acid change at residue 2 of Lb could be selected when the virus goes back into the natural host . Consequently , either of these viruses could be the source of the 2007 outbreak . Sequence analysis of virus from the first affected holding identified in the second cluster of outbreaks ( IP3b ) demonstrated that it had evolved from virus from the first cluster of outbreaks ( Figure 2A and B ) . The sequence data are not consistent with a second escape of virus from the Pirbright site , as the virus from IP3b shares five common nucleotide changes with IP1b and IP2c and six in common with IP2b . A Bayesian majority rule consensus tree , Figure S1 , estimated in MrBayes [13] indicated that the group linking the second cluster of outbreaks to the first is strongly supported with a posterior probability greater than 0 . 999 . An alternative explanation that these outbreaks arose as a result of a second release of virus that contained this combination of mutations already is difficult to quantify precisely , however , calculations using the highest estimate of population heterogeneity ( determined from in-vitro experiments; [14] ) indicate that this probability is still many magnitudes less likely than a single release ( data not shown ) . During the second phase of the epidemic , analysis of the data ( within 24–48 hours: see Table 1 ) were rapidly reported to Defra to inform field investigations . As an example , the virus from IP3b was nine nucleotides different from the virus from IP1b ( Table 2 , Figure 2A ) . This is a high number of changes for a single farm-to-farm transmission ( a retrospective study of virus genomes acquired from sequentially infected farms during the UK 2001 outbreak in Darlington , County Durham , found a mean of 4 . 5 ( SD 2 . 1 ) nucleotide changes [15] ) , and we predicted that there were likely to be intermediate undetected infected premises between the first outbreaks in August and IP3b . Subsequent field investigations discovered IP4b and IP3c , which differed by one nucleotide from each other . IP4b was three nucleotides closer to virus from the first outbreaks , and IP3c also branched off the tree at this point . However , there were still six nucleotide differences between FMDV sourced from IP4b and FMDV sourced from the August outbreaks . Serosurveillance of all sheep within 3 km of the September outbreaks revealed another infected premises ( IP5 ) , on which it was estimated that disease had been present for at least two , and possibly up to five weeks . As Figure 2B shows , IP5 is a likely link between the August and September outbreaks . Epidemiological investigations suggest that animal movements were not involved in the transmission of virus between premises , but a variety of local spread mechanisms ( such as movements of contaminated persons , objects and aerosols ) could account for the transmission within each geographic and temporal cluster . Although the epidemiological link between the August and September clusters is not known , the genetic data provide strong evidence to link FMDV transmission between these and the other infected farms . The consensus sequences from individual farms were found to differ by 1–5 nucleotide substitutions . It is probable that the variation in number of changes observed ( between premises ) have resulted from a number of factors including variation in the degree of bottleneck on the transmitted virus population by different transmission routes and number of virus replication cycles that have occurred in the host post-transmission . The genetic relationships between viruses from individual animals shown in Figure 2A and B follows an identical topology to the Bayesian majority rule consensus tree ( Figure S1 ) and in-group relationships are strongly supported by posterior probabilities on genome groupings that were never less than 99% . Although a more confident resolution of the IP-to-IP transmission pathways might be achieved by characterising additional virus haplotypes present on individual holdings , previous sequencing of virus from different animals from the same farm conducted following the UK 2001 outbreaks indicated very limited intra-farm sequence variability [9] . Furthermore , the relationships presented here reveal a transmission pathway between outbreaks that is consistent with the estimates of when holdings became infected and infectious ( Figure 2B ) . The small number of nucleotide substitutions observed between viruses from source and recipient IP suggests that there has been direct transmission without the involvement of other susceptible species , e . g . sheep or deer .
Total RNA was extracted directly from a 10% epithelial suspension using the RNeasy Mini Kit ( Qiagen , Crawley , West Sussex ) , or from blood or oesophageal/pharyngeal scrapings using TRIzol ( Invitrogen , Paisley , UK ) . Reverse transcription of the RNA was performed using Superscript III reverse transcriptase ( Invitrogen ) and an oligo-dT primer ( see Table S2 ) . Twenty four PCR reactions per genome were performed with Platinum Taq Hi-Fidelity ( Invitrogen ) , using 23 primer sets tagged with forward and reverse M13 universal primer sequences , and one primer set with a oligo-dT reverse primer to obtain the very 3′ end genomic sequence ( Table S2 ) . The PCR products overlap such that each nucleotide is covered by two products . The reactions were run on a thermal cycling programme of 94°C for 2 min , followed by 40 cycles of 94°C for 30 s , 55°C for 30 s , 72°C for 1 min , with final step at 72°C for 7 min . Sequencing reactions were performed using the Beckman DTCS kit , with M13 universal forward and reverse primers and specific forward and reverse primers for each PCR product . This resulted in an average of 7 . 4 times coverage of each base . The raw data was assembled using the Lasergene® 7 Software package ( DNASTAR , Madison , WI ) and all further sequence manipulations were performed using BioEdit ( version 7 . 0 . 1 [16] ) and DNAsp ( version 3 . 52 [17] ) . The data were analysed by statistical parsimony methods [18] incorporated in the TCS freeware [19] . A Bayesian majority rule consensus tree ( based on 10 , 000 trees sampled from 10 million generations ) was estimated in MrBayes [13] assuming a General Time Reversible model of nucleotide substitution with invariant sites ( the model most strongly supported by more extensive genome data from the UK 2001 outbreak , [15] . This analysis was performed on consensus sequences as supported by previous analysis of within individual viral diversity of naturally infected animals based on results from cloning the capsid genes ( the most variable parts of the genome ) that show almost 50% of cloned sequences to be identical to the consensus sequences and with an average pi ( π ) value of 7×10−4 [20] . The FMDV genome sequences have been submitted to the GenBank/EMBL/DDBJ and assigned the accession numbers EU448368 to EU448381 . | Foot-and-mouth disease ( FMD ) outbreaks in the United Kingdom during August and September 2007 have caused severe disruption to the farming sector and cost hundreds of millions of pounds . Investigating and determining the source of these outbreaks is imperative for their effective management and future prevention . Foot-and-mouth disease virus ( FMDV ) has a high mutation rate , resulting in rapid evolution . We show how complete genome sequences ( acquired within 24–48 h of sample receipt ) can be used to track FMDV movement from farm to farm in real time . This helped to determine the most likely source of the outbreak , assisted ongoing epidemiological investigations as to whether these field cases were linked to single or multiple releases from the source , and predicted the existence of undetected intermediate infected premises . | [
"Abstract",
"Introduction",
"Results/Discussion",
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"Methods"
] | [
"virology"
] | 2008 | Transmission Pathways of Foot-and-Mouth Disease Virus in the United Kingdom in 2007 |
Shading is known to produce vivid perceptions of depth . However , the influence of specular highlights on perceived shape is unclear: some studies have shown that highlights improve quantitative shape perception while others have shown no effect . Here we ask how specular highlights combine with Lambertian shading cues to determine perceived surface curvature , and to what degree this is based upon a coherent model of the scene geometry . Observers viewed ambiguous convex/concave shaded surfaces , with or without highlights . We show that the presence/absence of specular highlights has an effect on qualitative shape , their presence biasing perception toward convex interpretations of ambiguous shaded objects . We also find that the alignment of a highlight with the Lambertian shading modulates its effect on perceived shape; misaligned highlights are less likely to be perceived as specularities , and thus have less effect on shape perception . Increasing the depth of the surface or the slant of the illuminant also modulated the effect of the highlight , increasing the bias toward convexity . The effect of highlights on perceived shape can be understood probabilistically in terms of scene geometry: for deeper objects and/or highly slanted illuminants , highlights will occur on convex but not concave surfaces , due to occlusion of the illuminant . Given uncertainty about the exact object depth and illuminant direction , the presence of a highlight increases the probability that the surface is convex .
Shading can produce striking impressions of 3D shape . However , recovering shape from shading is far from straightforward; luminance variations in the image are determined not only by the object's shape but also by its reflectance and the illumination conditions . To estimate shape from shading , the visual system biases judgements toward more common scenes , for example , light sources that are roughly overhead ( e . g . [1] , [2] ) and surfaces with homogenous reflectance , at least in the absence of hue variation [3] . Here we explore an additional regularity that the visual system appears to exploit in estimating surface shape: that specular highlights suggest convex , rather than concave curvature . We test this proposal psychophysically and show why , given certain assumptions , this bias is rational: it reflects a higher likelihood of observing a specular reflection from a convex object . It is well known that a specular highlight can change the perception of surface material , making a matte object look glossy ( Figure 1a ) . However , the effect of specular highlights on shape perception has received little attention . Specular highlights do carry shape information , tending to ‘cling’ to regions of high curvature [4]–[6] , and observers can use the structure of specular highlights alone ( e . g . on perfectly mirrored surfaces ) to estimate curvature magnitude [7] . Several studies have compared monocular shape perception across matte and specular surfaces to assess the role of specular highlights in quantitative shape estimation . Whilst some studies found that specular highlights increased perceived depth [8]–[10] or improved shape discrimination [11] , others have found no effect of surface specularity on shape judgements [12]–[14] . Ho , Landy and Maloney [15] found that the glossiness and bumpiness of a surface are somewhat confusable , even under binocular viewing: increasing surface depth increases perceived glossiness and vice versa . When a glossy object is rotated , specular highlights glide across the object's surface , rather than being fixed to it like texture . This motion provides information that observers exploit to judge both gloss [16] , [17] and shape [11] , [18] , [19] . Similarly , under binocular viewing , the disparity of specular highlights holds information not only about the magnitude of surface curvature but also its sign; for simple convex objects , specular highlights are stereoscopically behind the surface , for concave they are generally in front . The visual system appears to use this information in judgments of glossiness and , to a limited extent , shape [11] , [20]–[24] . Note that this cue to surface convexity depends upon the binocular disparity of reflections . For distant surfaces , this disparity signal will be weak , and thus no bias is predicted . Intriguingly , however , Blake & Bülthoff [20] noted informally that under monocular viewing , the addition of a specular highlight seemed to bias perception of their stimuli toward convexity , though this effect was not tested empirically . Unlike the binocular effect , such a bias does not have a straightforward geometric explanation . Yet it is important to determine whether this effect is real and quantifiable , since in the real world , disparity signals become very unreliable for distant surfaces , and other visual features ( e . g . , shading , texture ) provide only weak cues to curvature sign . If specular highlights provide a cue to surface convexity , they may prevent observers from making large perceptual errors about distant surfaces in the environment . Here we ask how specular highlights combine with Lambertian shading cues to determine perceived surface curvature , and to what degree this is based upon a coherent account of the scene geometry . To avoid covariation with other features found in natural scenes ( stereoscopic disparity , motion , texture , etc . ) we employ relatively simple stimuli ( shaded ellipsoidal surfaces with constant albedo ) , and manipulate the location of highlights relative to the Lambertian shading gradient to vary the consistency of the two cues . In three experiments we ask: The problem of judging surface shape for these stimuli is ill-posed: there are many possible scene configurations that could give rise to each observed image , and in particular both signs of surface curvature , convex and concave , are possible . Here we hypothesize that the human visual system attempts to determine the most probable curvature sign given the image data . In order to assess whether our psychophysical results are consistent with this principle , we construct a quantitative Bayesian model that attempts to explain the shading and highlights observed in the image in terms of the illumination field , object shape and surface material ( glossy or matte ) . So as not to obscure the empirical results , we defer detailed presentation of the model to the Materials and Methods section , however we will discuss its qualitative properties and show the fit of the model to the psychophysical data alongside our empirical results .
In our first experiment , observers viewed a pair of shaded objects , with or without specular highlights ( Figure 1a ) and reported perceived sign of surface curvature ( convex or concave ) of one of the objects . The shading gradients on the two objects were always in opposition and were systematically varied over all angular directions , in 15 deg increments . There were four conditions: MM: Neither object has a highlight . SM: The target object has a highlight , the distractor object does not . MS: The target object does not have a highlight , the distractor object does . SS: Both objects have highlights . Our first experiment shows that the appearance of a specular highlight biases observers toward a convex interpretation of the stimulus . For these stimuli , the geometry of reflection dictates that the highlight appears on the lighter side of the shape , aligned with the shading gradient . In Experiment 2 we ask how the effect on perceived convexity varies as a function of this alignment ( Figure 4a ) . Figure 4 ( b ) shows data averaged across 10 observers . Each subplot shows perceived convexity as a function of shading orientation for a single specular highlight position ( indicated by a yellow star ) . As in Experiment 1 , objects with a highlight were judged to be convex more often than objects without . Furthermore , this effect seems to persist even when the highlight is rotated out of alignment with the shading gradient , although the magnitude of the effect is reduced . To better understand this variation , we calculated the mutual information between the presence of a highlight and perceived shape using data from the SM and MS conditions . Figure 5a shows the results as a function of Lambertian shading orientation , averaged across highlight location . As in Experiment 1 , a highlight is ineffectual when objects are bright at the top; these objects are perceived as convex ( and lit from above ) with or without a highlight . When a highlight appears near the top of the object , therefore , it is not possible to assess whether highlight-shading alignment ( and thus highlight interpretation ) modulates the effect of highlights on shape perception . However , we can examine the effects of highlight misalignment on shape by considering the mutual information between perceived and convexity and highlights appearing on the lower half of the object ( Figure 5 ( b–d ) ) . We see that the effect on shape is largest when the highlight is aligned , or nearly aligned , with the diffuse shading gradient . The specular occlusion account is qualitatively consistent with the observed bias to convex surfaces induced by the appearance of a highlight , but without quantitative measurement of the prior over object shape and illuminant slant it cannot be verified quantitatively . Here we present an additional psychophysical experiment that provides an additional test of the model . The specular occlusion hypothesis is rooted in uncertainty over the exact shape of the surface and the location of the illuminant . As a result , visual cues that shift the posterior distribution over these scene variables should alter the probability of highlight occlusion and therefore the induced convexity bias . In particular , the bias should get stronger when these cues suggest either ( i ) an increase in surface depth or ( ii ) an increase in illuminant slant ( deviation from the view vector ) , since both variations increase the probability of specular occlusion for a concave surface . Our third experiment directly tests this prediction of the model ( we thank one of the anonymous reviewers for suggesting such an experiment ) . As in Experiments 1 and 2 , observers viewed pairs of shaded stimuli , and reported the perceived shape ( convex or concave ) of one object . The shading and highlight cues to absolute depth are subtle and confounded with illuminant slant; by adding texture to the objects we provided an independent cue to depth that should allow observers to better dissociate these two scene variables ( Figure 6a ) . The shading gradients of the two objects were always in opposition and either one or neither of the objects had a specular highlight . The two objects always had the same depth magnitude , however , this depth and the slant of the illuminant varied across trials . To focus the experiment , we determined the shading gradient direction for each observer that produced balanced ( 50% ) reports of ‘convex’ and ‘concave’ for the two oppositely shaded matte objects , and then examined the effect of the highlight on perceived convexity while varying object depth and illuminant slant . Figure 6 shows example stimuli and the data from this experiment . The highlight effect is quantified by the proportion of ‘convex’ responses in the presence of a highlight ( in contrast to 50% when absent ) . Figure 6b shows the effect as a function of illuminant slant , collapsed across stimulus depth . As the direction of illumination approaches the image plane ( increasing slant ) , the effect of the highlight on perceived shape increases ( F5 = 7 . 3; p<0 . 01 ) . Figure 6c shows the effect as a function of stimulus depth , collapsed across illuminant slant . As object depth increases , the effect of the highlight on perceived shape again increases ( F3 = 6 . 2; p<0 . 05 ) . In summary , as predicted by the geometry of specular occlusion , increases in illuminant slant or object depth both increase the probability of convex report . Interestingly , while increasing illuminant slant or object depth both increase the convexity bias , they have opposite effects on the position of the highlight ( dashed lines in Figures 6b and c ) . In particular , while increasing the slant of the illuminant shifts the highlight toward the rim of the object , increasing the depth of the object shifts the highlight in the opposite direction , toward the centre of the object . Our results therefore indicate that the observer is not simply relying on the position of the highlight when judging curvature sign . Instead , our data suggest that the observer's perception is modulated by estimates of quantitative depth and illumination direction , becoming increasingly biased toward a convex interpretation as the probability of highlight occlusion increases . These results are thus a strong confirmation of the specular occlusion account of the convexity bias induced by the appearance of a highlight .
We have conducted three experiments to explore the effects of highlights on perceived convexity: The results from all three experiments are consistent with a Bayesian model that takes into account potential light source occlusion . Does this mean that observers are constructing a complete and detailed 3D solution for the entire scene ? Some have argued against this kind of ‘inverse optics’ model [14] , suggesting that the underlying variables of shape , reflectance and illumination may not be estimated concurrently , so that probing the percept of each will not necessarily yield consistent results . Furthermore , while shape and material may be important for manipulating and recognizing objects , we might question whether observers require an explicit estimate of the illumination field . On the other hand , there is evidence that observers make judgments of shape and/or reflectance consistent with a particular estimate of the illumination field without necessarily making this estimate explicit . Observers can manipulate the shading pattern of one object to appear consistent with a second object , such that the implicit illumination environments match [43] , although like our observers , they relied on priors for overhead illumination and object convexity when image cues were ambiguous . Similarly , reflectance judgements for ambiguous images are consistent with a single overhead illuminant [25] . In contrast , observers are poor at making explicit judgements of illumination consistency across multiple objects [44] . In our experiments , observers are asked only to judge the convexity of objects , and not the glossiness of the surfaces or the number or direction of light sources . As a consequence , the predictions of the Bayesian model ( Materials and Methods ) are not based upon explicit joint estimation of these scene variables , but do depend critically on at least approximate marginalization over the unknown ‘nuisance’ variables ( object depth , illumination ) when judging convexity . This process of marginalizing over or ‘integrating out’ nuisance variables when judging other scene variables of interest is widely believed to explain a number of visual phenomena ( e . g . , [27] , [45] ) , and the consistency of our Bayesian model with the psychophysical data suggests that it may also explain the effect of highlights on the perception of surface convexity . The interplay between the light field , surface reflectance and surface shape is complex and many issues remain to be resolved . Our experiments reveal the effect of specular highlights on perceived convexity for ellipsoidal surfaces and point light sources . It remains to be seen whether this effect generalises to more complex surfaces and light fields ( see Figure S1 for examples of ellipsoidal stimuli rendered with ray-tracing under a complex illumination field ) . In addition , further studies may resolve the existing inconsistencies in the literature regarding the effect of highlights on perceived curvature magnitude [8]–[14] . Overall , our results shed new light on how the human brain uses highlights to disambiguate 3D surface shape . Our Bayesian model suggests that this is more than a ‘bag of tricks’[46] . Rather , inference can be accounted for as a rational computation that selects the most probable shape interpretation , given the observed data and prior information about the relative probability of alternative scene configurations .
For all experiments , participants gave informed consent and the local ethics committee approved the study . Stimuli consisted of two axis-aligned half-ellipsoids , compressed in depth by a factor of two relative to a hemisphere , illuminated by a single , distant light-source . The orientations of the smooth ( Lambertian ) shading gradients on the two objects were always in opposite directions . When a single object ( either with or without a highlight ) is presented in isolation it is perceived as convex for all illumination tilts due to the widely documented prior for object convexity [47]–[50] . This convex bias is represented in our model by the prior over curvature sign : over observers . When two objects are presented with opposing shading gradients , the prior for a single illuminant counteracts the convexity prior , causing the observer to perceive the objects as having opposing curvature sign , on most trials . The two-object scene thus allows us to explore the effects of specular highlights on shape perception . There were four stimulus configurations: ( 1 ) Highlight on neither object , ( 2 ) Highlight on the left object , ( 3 ) Highlight on the right object , ( 4 ) Highlight on both objects ( Figure 1a ) . Stimuli were generated as grey objects under white light using the Phong lighting model implemented in OpenGL , without inter-reflections or cast shadows , under orthographic projection . Shiny objects were rendered with ambient ( 7% of maximum ) , diffuse ( 36% of maximum ) and specular components ( 48% of maximum , with Phong exponent of 80 ) . Matte objects had only diffuse and ambient components . Under this Phong lighting model and orthographic projection , convex and concave objects generate identical images , thus rendering the estimation of the sign of surface curvature completely ill-posed , allowing us to isolate the role of highlights in the perception of surface convexity . In a real scene , however , subtly different patterns of interreflection could in theory serve to discriminate convex from concave surfaces . In practice however , these differences are relatively minor for our scenes , as confirmed by comparing ray-traced renderings , under a complex light field , with and without inter-reflections ( compare Figures S1a and b ) . We define a coordinate frame with origin at the centre of the display , X- and Y-axes in the horizontal and vertical directions in the plane of the screen , respectively , and Z-axis positive toward the observer . The slant of the single directional light ( the angle between the lighting vector and the Z-axis ) was held constant at 68° . The tilt of the lighting direction ( the angle between the projection of the lighting vector and the Y-axis ) varied across trials . The orientation of the shading gradient for each object was thus a function of its curvature sign and light source tilt . The room was unlit aside from the light emitted by the monitor . To eliminate binocular and motion-based depth cues , stimuli were viewed monocularly , with the observer's head fixed by a chin rest and forehead bar . At the viewing distance of 57 cm , each object subtended 5° with their centres displaced horizontally ±3 . 4° from the display centre . Scenes were rendered with orthographic projection , simulating an infinite viewing distance . Given the small angular subtense of our stimuli , switching to perspective projection has only a small effect on the shading gradient and position of the highlight in our images ( see Figure S1c ) . On each trial , the two shaded objects appeared for 1 second . Halfway through the presentation , a star appeared next to one of the objects , indicating that this ‘target’ should be judged . By a key-press , the subject reported the target curvature as either ‘convex’ or ‘concave’ . The four conditions ( Target Matte , Distractor Matte ( MM ) ; Target Matte , Distractor Shiny ( MS ) ; Target Shiny , Distractor Matte ( SM ) ; Target Shiny , Distractor Shiny ( SS ) ) and the target's shading orientation were randomly interleaved . Ten observers ( 9 naïve and 1 author ) each completed 1536 trials ( 24 target orientations x 4 conditions x 16 repetitions ) in a single session lasting approximately 1 hour . One additional naïve observer was excluded from the analyses as the direction of the shading gradient had little effect on his/her shape judgements . Only one of the two objects was rendered with a highlight , and the orientation of the diffuse shading component ( 16 equally spaced values ) and the angular position of the highlight ( 10 equally spaced values ) were varied independently , by rendering the diffuse and specular components of the image with independently positioned illuminants ( Figure 4 ) . As in Experiment 1 , the two objects had opposite gradient directions and a star indicated which of the two objects should be judged ( convex vs . concave ) . The 3840 trials ( 10 highlight positions x 16 shading orientations x 2 conditions ( SM: only the target has a highlight , MS: only the distractor has a highlight ) x 12 repetitions ) were completed in 3 sessions of approximately 45 minutes . All other details were identical to Experiment 1 . The 10 observers who completed Experiment 1 also participated in Experiment 2 . In our third experiment we studied the effect of object depth and illuminant slant on the convexity bias caused by a highlight . This is tricky to do in a controlled fashion using the ellipsoid objects of Experiments 1 and 2 , as the variation in curvature across the shape induces changes to both the shape and size of the highlight as the slant of the illuminant is varied . To stabilize the appearance of the highlight , we replaced the ellipsoidal surfaces with sections of hemispheres that protruded from or recessed into the planar background surface . Since surface curvature is constant over the hemisphere , variations in illuminant slant induce much smaller variations in the shape and size of the highlight . As in Experiments 1 and 2 , the direction of the shading gradient on the two objects was always in opposition , such that the stimulus was consistent with one convex and one concave object , both illuminated by a single light source . The simulated depth of both objects always matched , but varied across trials ( depth:radius ratio was 0 . 25 , 0 . 5 , 0 . 75 or 1 ) , by changing the radius of the sphere from which the domes were constructed . Highlight position was yoked to the shading gradient: i . e . both were rendered with the same illuminant . In order to determine the shading gradient that produces a roughly balanced perception of convex and concave shape for each object depth and illuminant slant , for each observer , we sampled a range of illumination tilts between 90° to 270° ( 7 equally spaced values ) . Illuminant slant varied across trials from 25° to 75° ( 6 equally spaced values ) . A green texture ( see Figure 6a ) was wrapped around both objects and the planar background to facilitate depth perception . We found that the sharp join between the hemisphere sections and the planar background caused the objects to appear detached from the background; to avoid this , we introduced a thin curved section to smooth this join . For specular objects , this generated an additional very thin specularity at the join ( Figure 6a ) ; this additional feature does not appear to be correlated with variations in observer reports of perceived convexity . Four observers completed 2016 trials ( 4 depths x 6 illuminant slants x 7 illuminant tilts x 2 specularity conditions ( no highlights or highlight on the target only ) x 6 repetitions in two sessions of approximately 30 minutes . Both object depth and illuminant slant have a systematic effect on the perceived curvature sign of matte objects; shallow objects and small illuminant slants produce shading patterns that are more similar for the two objects , and perhaps for this reason the overall proportion of convex responses increases under these conditions ( although this did not reach significance ) . To compare the effect of the highlight across these conditions without the confound of varying baseline convexity , for each condition and each subject we found the shading orientation at which the matte stimulus was perceived as convex on 50% of trials . This was found by fitting a psychometric function to the proportion of convex responses as a function of shading orientation , and obtaining the 50% threshold . We then measured the effect of the highlight by the proportion of convexity judgments relative to this consistent 50% baseline ( Figures 6b and c ) . Our psychophysical experiments have shown that the judgement of surface convexity is dependent upon the appearance of surface highlights and their locations relative to the shading gradient induced by surface curvature . In our view , the most important question is why a highlight has this effect . Here we put forward a specific theory: due to potential occlusion of the light source for a concave surface , highlights occur more frequently on convex surfaces in natural scenes . As a consequence , the convexity bias induced by highlights will increase the ability of the observer to correctly judge the sign of surface curvature . While this theory is qualitatively consistent with the psychophysical data , it remains to be seen whether it is quantitatively consistent with the data . To assess this , we have constructed a Bayesian model for the discrimination of convex vs concave surface curvature given the shading gradients and highlights appearing on the two objects comprising our stimuli . Specifically , the observable variables are ( Figure 7 ) : The model incorporates the minimal set of hidden scene variables sufficient to explain the observed shading and highlight cues . These include: We believe this to be the minimal set of hidden variables that makes sense: removal of any one of these variables would mean that the model would not capture a basic feature of the phenomenology or relationship between observable features and observer reports ( see Model complexity ) . Capturing the relationship between perceived surface curvature sign and illumination requires modelling probability distributions over the angular direction ( tilt ) of the illuminant and corresponding observable variables . Observers have a well-documented prior for overhead illumination [1] , [2] , [25] , [26] , [34] , [48] , [51]–[53] that has previously been successfully modelled by a von Mises distribution [51] although the mean of this distribution varies considerably across observers [25] . We employ the von Mises distribution to model observers' prior distribution over illuminant tilt , with the general formwhere is the tilt angle , and are the mean and concentration ( inverse variance ) , and is the modified Bessel function of order 0 , required for normalization . This distribution is used to model: For each observer , the values of the 9 model parameters ( summarized in Table 2 ) were found ( MATLAB fminsearch ) that maximize the joint likelihood of the observed data for both Experiments 1 and 2 . Multiple iterations of the parameter search were performed , with the initial values on each iteration determined by uniform sampling within a plausible parameter range . All equations for the model can be found in Text S1 . Our model was constructed to include only scene variables relevant to the observers' judgement of convexity for the two-object stimuli used in our experiments . Nevertheless , the model does have nine free parameters , raising the question of whether we are overfitting the data . To address this question , we considered three models of reduced complexity and compared their ability to account for the psychophysical data ( Table 1 ) . We find that the full model provides the best account of the data , for every observer , as indexed by the Bayesian Information Criterion ( see Figure 3b ) . This result suggests that to account for the perception of surface convexity one must allow for a ) a prior bias for convexity , b ) the possibility of complex illumination fields , c ) the biasing effects of highlights and d ) the possibility of attributing these highlights either to specular reflection or to a local illumination effect , depending upon the consistency of the highlight with the shading gradient . To understand the scene parameters leading to specular highlight occlusion , we can , without loss of generality , consider the viewing geometry of our scene in cross-section , in the plane defined by the viewing and illuminant vectors , with the illuminant on the right ( see Figure 2a ) . The resulting cross-section of the surface describes a semi-ellipse . We define the depth expansion factor d to be the ratio of the length of the semi-axis in the viewing direction z to the length of the semi-axis in the horizontal direction x . Without loss of generality , we assume that the length of the semi-axis of the ellipse in the horizontal direction is 1 , so that the length of the other semi-axis ( in the viewing direction z ) is equal to the depth expansion factor d . Centering a 2D coordinate system directly above the concave surface , at the level of the rim , the surface cross-section can be described by the equation ( 0 . 1 ) Taking a first derivative yields , so that the tangent vector must be in the direction and the normal vector must be in the direction . The specular highlight will be located at the point on the semi-ellipse where the normal bisects the angle formed by the view vector and the illuminant vector . Thus we have ( 0 . 2 ) Together , Equations ( 0 . 1 ) and ( 0 . 2 ) determine the location of the highlight: solving ( 0 . 2 ) for and substituting in ( 0 . 1 ) yields ( 0 . 3 ) For our stimuli , the depth expansion factor and illuminant direction were fixed at and , yielding a highlight location of . Of course the observer does not know the exact surface depth or illuminant direction , and for a highlight appearing at this particular location there is in fact a one-dimensional family of solutions to Equation ( 0 . 3 ) given by ( 0 . 4 ) and described by the blue curve in Figure 2c . However , not all of these solutions are physically possible: for larger illumination angles ( and larger surface depths ) , the view of the illuminant from the required highlight location will be occluded by the rim of the surface . To quantify this constraint , we note that the angle of the vector pointing to the rim from the highlight location , relative to the view vector ( Figure 2a ) , can be written as ( 0 . 5 ) Substituting for from ( 0 . 2 ) yields ( 0 . 6 ) and substituting for from Equation ( 0 . 4 ) yields ( 0 . 7 ) Equation ( 0 . 7 ) describes the angle of the rim of the surface as seen from the potential highlight location , as a function of the estimated depth expansion factor . This function is shown by the red curve in Figure 2c . Note that for a subset of solutions with highly oblique illumination and large surface depth , the red curve lies below the blue curve . These solutions are physically infeasible because the illuminant is occluded by the rim of the surface . For a Bayesian observer who is uncertain about the surface depth and elevation of the illuminant , a consequence is that observation of a highlight will decrease the probability of concave surface curvature relative to the probability of convex surface curvature , for which all solutions are feasible . | A primary goal of the human visual system is to reconstruct the three-dimensional structure of the environment from two-dimensional retinal images . This process is under-determined: an infinite number of combinations of shape , material properties and illumination conditions could give rise to any single image . Rather than determining the true three-dimensional scene in a deductive manner , the visual system must make its ‘best guess’ based on the image , probabilistic models of image formation , and the stored probability of various scene configurations . For example , the visual system appears to assume that convex surfaces are more common than concave ones , biasing perception toward convex surfaces when the image is ambiguous . Here we identify a new probabilistic cue for surface shape: a shape with a visible specular highlight is more likely to be convex than one without . Highlights occur when light is reflected in a mirror-like way from glossy surfaces such as polished marble or metal . Due to the geometry of reflection , however , highlights are more likely to be occluded on concave objects . We show that the human visual system makes use of this constraint: shape perception is biased toward convex surfaces when highlights are apparent . | [
"Abstract",
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] | 2014 | Effects of Specular Highlights on Perceived Surface Convexity |
In 2004 Niger established a large scale schistosomiasis and soil-transmitted helminths control programme targeting children aged 5–14 years and adults . In two years 4 . 3 million treatments were delivered in 40 districts using school based and community distribution . Four districts were surveyed in 2006 to estimate the economic cost per district , per treatment and per schistosomiasis infection averted . The study compares the costs of treatment at start up and in a subsequent year , identifies the allocation of costs by activity , input and organisation , and assesses the cost of treatment . The cost of delivery provided by teachers is compared to cost of delivery by community distributers ( CDD ) . The total economic cost of the programme including programmatic , national and local government costs and international support in four study districts , over two years , was US$ 456 , 718; an economic cost/treatment of $0 . 58 . The full economic delivery cost of school based treatment in 2005/06 was $0 . 76 , and for community distribution was $0 . 46 . Including only the programme costs the figures are $0 . 47 and $0 . 41 respectively . Differences at sub-district are more marked . This is partly explained by the fact that a CDD treats 5 . 8 people for every one treated in school . The range in cost effectiveness for both direct and direct and indirect treatments is quantified and the need to develop and refine such estimates is emphasised . The relative cost effectiveness of school and community delivery differs by country according to the composition of the population treated , the numbers targeted and treated at school and in the community , the cost and frequency of training teachers and CDDs . Options analysis of technical and implementation alternatives including a financial analysis should form part of the programme design process .
In 2004 Niger established a national programme to control schistosomiasis and soil-transmitted helminths ( PNLBG ) supported by the Schistosomiasis Control Initiative ( SCI ) , funded by the Bill and Melinda Gates foundation [10] . Its objective in line with Resolution WHA54 . 19 was to treat 75% of school age children at risk of infection and in communities where prevalence is over 50% to also treat at risk adults . The purpose being to reduce the morbidity related to schistosomiasis infection to a level at which it would not constitute a public health problem [11] . The primary school net enrolment rate ( NER ) in 2004 in Niger was 41% ( UNESCO UIS global education database Table 5 , Enrolment ratios by International Standard Classification of Education ( ISCED ) level ) , lower in rural areas; and considerably less than the rate of 68% for Sub Saharan Africa ( SSA ) . To achieve high treatment coverage in targeted school age children and at risk adults two treatment strategies , school-based and community-based distribution , were established . Treatment for S . haematobium was provided every two years in most endemic areas , and annually in high prevalence areas to reduce initial levels . School-based distribution was provided by trained teachers who distributed the drugs to students in the schools . Children not attending school could receive treatment either in the schools or from the Community Drug Distributor ( CDD ) at home or at another fixed treatment location . The MDA programme was established and rolled out over 2 years from April 2004 across 40 districts in all 7 regions of the country , including the capital city Niamey . The programme activities were implemented progressively commencing in the Tilaberi and Dosso regions . In 2004/05 , 1 , 627 , 828 treatments were delivered in 22 districts and in 2005/06 2 , 683 , 121 treatments were delivered in 40 districts . Figure 1 outlines the main MDA programme activities . Initial meetings and agreement of the programme with the regional and district administrations were followed by a prevalence survey and mapping to prioritise areas for MDA . A national workshop and practical field sessions to develop capacity in the diagnosis of schistosomiasis was organised . Further capacity-building workshops and training for staff in organisation , management and implementation of MDA was provided to key district health and educational inspectorate staff in an initial national workshop . These staff then trained clinic staff , health workers and head teachers though district meetings . Training was provided on the calculation of drug requirements , drug distribution organisation , the management of side effects and the reporting of results . Community and school-based drug distributors were trained in the use of dose poles ( to determine drug dosage ) , and the completion of treatment forms . The national programme developed , piloted , printed and delivered information , education and communication ( IEC ) materials for distribution to the districts . These materials included posters and a booklet for use in schools and communities as well as technical sheets for those administering the drugs . Drugs were procured centrally by SCI on behalf of countries which SCI supported in West and East Africa [12] . The drugs were sent directly from the National store to the districts . The districts and inspectorates repacked the drugs and IEC material for distribution to or collection by clinics and schools . Social mobilisation activities were undertaken at various administrative levels . National radio and television broadcasts were undertaken in three local languages and in French , organised by the PNLBG; local radio broadcasts were organised by the districts; village criers were organised by clinics to inform communities about the logistics of the MDA . A rally to launch the campaign was undertaken and organised by an host region; it involved a day of speeches , dance and hospitality supported by national , regional and programme dignitaries and was broadcast on national radio and television . Treatments were delivered at schools by teachers; CDDs provided treatment by going from door to door and at other fixed points supervised by clinic , district and regional staff . Technical and management support and supervision were provided to the districts by national staff . At the end of the MDA unused drugs and monitoring reports were collected by national staff . A one day post MDA evaluation meeting was held in each participating region attended by national , regional and district staff . A summary of the partner roles and responsibilities is identified in Table 1 .
The study examines the economic costs of the MDA programme in its first and second years . The economic costs include the full value of the resources used . Where this is not adequately represented by the financial or market cost , an opportunity cost is used ( see Table S4 ) . The main cost elements include: the programme specific expenditure; the opportunity cost or value of government contributions related to in-kind costs of using local government staff and vehicles and the value of CDD's time ( taken as the daily agricultural labour rate ) ; and the international costs of programme co-ordination , reporting and technical support . Programme costs include directly incurred capital costs; recurrent costs; and variable costs . Capital costs incurred by the programme included central level purchase of Information Technology ( IT ) , medical and laboratory equipment and other electrical and mechanical goods and furniture used to equip the PNLBG office including the purchase of four 4×4 vehicles for PNLBG ( Table S1 ) . Capital costs were annualised over their useful lives ( Table S2 ) using a discount rate of 3% . This represents the annual cost of owning and operating an asset over its lifespan . Programme recurrent costs including staff costs , office and vehicle running costs , and programme variable costs were collected from the programme records , accounts and receipts . These costs were apportioned in relation to the time spent by programme staff on MDA activities and the proportion of that time allocated to study areas . Variable costs related to perdiems , materials and services incurred in relation to the programme activities . Centralised activities ( e . g . organisation and provision of national training for all district technicians , planning and organisation ) and regional activities ( e . g . MDA launch ) were equally apportioned in relation to the number of districts in the MDA and share of the four study districts . Location specific activity costs such as supervision , mapping , central delivery of drugs to districts were allocated on the basis of costs incurred in the study districts . Sentinel monitoring informs the national treatment strategy . The costs of sentinel site monitoring were apportioned to the study districts on the basis of study area treatments relative to national treatments . Government staff costs were based on salary costs collected through questionnaire and the Government salary grid . District and sub district vehicle usage was calculated from questionnaire returns and costed using hire rates . These values are estimated to reflect the opportunity cost of using the resources for the MDA rather than for an alternative activity . Costs were collated and classified by three levels of organisation ( national , regional & district , or community ) , type of activity ( training , support & supervision , baseline & monitoring , reporting , evaluation , advocacy , mobilisation & IEC ) and cost type ( fuel , transport , materials , services , drugs , per diems , temporary contracts and office related recurrent costs ) . Prices are in constant 2005 terms ( Table S4 ) . Foreign exchange was converted at the fixed rate of CFA 655/Euro and $1 . 244/Euro ( http://www . federalreserve . gov/release/January 2 2009 ) . Discounted economic analysis was undertaken using discount rate of 3% in line with World Bank rates [13] . The cost of a treatment includes both albendazole and praziquantel . Community and school based delivery was and is practised nationally . The costs incurred by the two systems were equally attributed at national , regional and district level . It is at sub district level that the systems differ in the organisation and implementation of the delivery activities . The school and sub district delivery services used a partial analysis which took account of these cost components only . The cost of delivery using a CDD and of using a teacher was calculated . These costs included per diems and travel allowances for CDD and head teacher training; allowances for delivery ( applicable only for CDD ) , health clinic staff costs for CDD selection ( per diems and fuel ) and supervision ( fuel only ) . The training of one or more teachers and their supervision in schools was undertaken by the school head , no financial cost was incurred . Joint activity costs of the district health and education inspectorate ( training , drug repacking , drug delivery to sub districts and schools and supervision ) would be incurred despite the system . These have not been included in the partial analysis but an allowance has been estimated to allow comparability with other MDA programmes . The effectiveness of treatment was calculated as the difference between the population with schistosomiasis infection at baseline and follow-up survey . The prevalence rates used are from a longitudinal health impact study ( Nadine Seward ( 2007 ) Niger Three Years Data Analysis , SCI internal report ( unpublished ) ) . To assess the effectiveness of the programme's direct and direct and indirect treatment effects an assessment of the impact in the treated population and in the targeted population was made . Treatment costs were calculated as the number of treatments in each year multiplied by the full economic cost in 2004/5 and in 2005/6 . Eight schools and four communities located in areas highly endemic for schistosomiasis took part in a longitudinal health impact study . The study used baseline and longitudinal follow-up surveys one year post treatment to monitor: parasitological indicators ( prevalence and intensity of helminth disease examining stool and urine samples following standard procedures using kato katz and filtration methods [14] ) ; morbidity indicators ( anaemia and associated pathology of schistosomiasis , assessed by ultrasound examination following standard protocols developed by WHO ) and general indicators of height and weight . The baseline survey enrolled 1659 children from 8 different schools in 3 regions prior to the first MDA campaigns of 2004 and 2005 . The number of children enrolled from each school ranged from 179 to 299; with almost equal numbers of children in age groups of 7 , 8 & 11 years old . Of those recruited 1193 ( 72% ) were followed-up successfully at year 1 and year 2 surveys . Adults and adolescents were monitored in 4 sites in a single region . A total of 484 adolescents and adults were recruited at baseline . Of these , 143 ( 30% ) were followed-up successfully at both year 1 and year 2 surveys . The sample sizes was estimated using the same criteria as described in [15] . The surveyed sites mirror the MDA treatment and represent MDA performance in targeted populations taking into account the treated and untreated participants in proportion to the MDA coverage . Any indirect effect of reduced infection in untreated pupils resulting from changes in the force of infection is reflected in the intensity of infection [16] which is related to prevalence ( [17] provides more detail ) . To assess the wider impacts on the community , untreated first year students were monitored in the schools . Adults and adolescents were monitored at four sites .
The total economic cost of the programme including programme specific expenditure , national and local government costs and international technical support and programme co-ordination in four study districts , over two years , was US$ 456 , 718 ( Table 2 ) ; an economic cost per treatment of $0 . 58 . Excluding international costs , the programme and government expenditure was $0 . 54 per treatment . The programme expenditure per treatment was $0 . 44 . The average drugs cost was $0 . 28 per treatment . The numbers treated in these two years totalled 818 , 562 ( 781 , 883 , discounted at 3% ) . The distribution of costs between the programme , the government and international support are shown in Table 2 . Drugs accounted for 49% of the total economic cost ( 65% of programme expenditure ) , variable costs accounted for 19% of the economic cost ( 26% of programme expenditure ) . Overall there was little difference in the total economic cost of the programme in the four districts between the first and second years . However the total economic cost per treatment in the first year was $0 . 68 and in the second year was $0 . 51 . Cost differences are shown in Table 3 and discussed below . Excluding the MDA drug costs , the economic cost of the programme in the four districts in the second year was 29% less costly than the first year and treated 25% more people . Higher costs in the first year of the programme are seen in programme costs and international support . Three factors contribute to this . The cost of the initial start up activities incurred in the first year only . The activities involved advocacy , development of IEC materials , prevalence surveys and data collection for planning and the establishment of monitoring and evaluation ( M&E ) activities , in particular the longitudinal monitoring sites ( illustrated in figures 2 and 3 ) , and repair and maintenance of the national office . In the second year the programme was scaled up . This reduced the apportioned share of recurrent and capital programme costs and international costs allocated to the study area . In 2004/05 22 districts were treated and in 2005/06 40 districts were treated . Within the study area the population treated in the second year which was 25% more than those treated in the first year . The distribution of variable expenditure ( excluding drugs ) by activity in the study area is presented in Figures 2 and 3 . These show the relatively large proportion of expenditure on training and on MDA delivery . It also highlights activities mainly undertaken at establishment . Total programme variable costs in 2004/05 were $ 51 , 970 and in 2005/06 were $ 40 , 318 , 22% less than those in the first year . M&E costs include costs of process monitoring in 2004/5 , annual district and regional evaluations and programme health impact monitoring undertaken through the National sentinel sites . These costs amounted to an average of 13% of variable costs over the 2 years . Table 4 presents the average allocation of cost by category ( capital , recurrent and variable ) and type of input . Labour related costs ( salary plus per diems ) and vehicle and fuel costs account for 64% and 19% of all costs excluding drugs . Sensitivity analysis was undertaken on major cost items . A 10% increase in the cost of drugs would result in a 4 . 9% increase in the total economic cost of treatment ( $456 , 718 ) , and a 6 . 5% increase in the current programme cost ( $342 , 226 ) . A 10% increase in perdiems and allowances would result in a 1 . 1% increase in the total economic cost of treatment , or a 1 . 5% increase in the programme cost , a 4 . 2% increase in the programme cost excluding drug costs . A 10% increase in wages and salaries would result in a 1 . 5% increase in the economic cost of treatment . It would impact most on the government sector and distributer opportunity costs increasing costs by 7 . 7% . The increase on the programme cost would be 0 . 2% The sensitivity of total economic cost to a saving in teacher training costs was explored . This assumed community distributers would undertake the school treatments for the same fixed allowance . Savings in teacher training allowances are assumed , but not the economic cost of their time which would still be required to support distributers in school based treatment . Any savings in teacher time would be offset by the increased opportunity cost of time for community distributers . The impact of the net saving on the total economic cost would be 2 . 9% . This is equivalent to a saving of 4 . 1% in the programme cost . This provides an approximate scale of magnitude within which to assess comparative costs of sub district delivery systems below . Sub district costs ( i . e . clinic , school and community costs ) account for the largest portion of the economic cost by administrative level . This is 23% of the total economic cost ( based on Table 2 ) ; so , it is important to understand the allocation and usage . Sub district variable programme costs include head teacher and CDD per diems for training and CDD payments for distribution . Sub district government costs include the opportunity cost for the use of motorbikes ( 11% ) and labour ( 89% ) . The opportunity cost of labour is principally accounted for by the time of the teacher and head teachers ( 61% ) , of the clinic staff in supervision ( 20% ) and CDD time for training and distribution ( 19% ) . Table 5 presents the characteristics and costs of sub district delivery . The economic cost per school based treatment and per CDD treatment delivered was $0 . 36 ( range $0 . 26–$0 . 55 ) and $0 . 06 ( range $0 . 04–$0 . 07 ) respectively . The programme cost per school based treatment and per CDD treatment delivered was $0 . 09 ( range $0 . 07–$0 . 15 ) and $0 . 03 ( range from $0 . 03–0 . 04 ) respectively . The full economic delivery cost of school based treatment in 2005/06 was $0 . 76 , and community treatment was $0 . 46 . If only programme costs are included this figures are $0 . 47 and $0 . 41 respectively . The difference in costs is in part explained by the fact that a CDD delivers 5 . 8 treatments for every one delivered in school . On average each CDD delivered 407 treatments while each school delivered 70 . Over the 2 treatment cycles 530 , 300 treatments were provided to an estimated 317 , 549 adults and 288 , 262 treatments were provided to 241 , 218 children in the study areas in the regions of Dosso and Tilaberi . Coverage in the target population in Gaya , Dosso was 78% in both years , and was 69% and 71% in the three districts monitored in Tilaberi . Two estimates of the cost of treatment per case of infection averted ( Table 6 ) are presented . One includes only the direct impacts of treatment and the other includes the direct and indirect impacts of treatment . They provide minimum and maximum limits of the true value . This is discussed further in the next section . Including only the direct impacts on the treated population the average cost per infection averted in treated children in the 4 districts over the two years was $1 . 10 . Of the 317 , 549 adults treated in a single round the cost per infection averted was $4 . 4 and over two rounds it is estimated to be $6 . 5 . The overall cost per infection averted in the treated population of children and adults is calculated as $2 . 5 . If indirect treatment effects are included the average cost per infection averted in targeted ( treated and untreated ) children in the 4 districts over the two years was $0 . 78 . Of the 446 , 180 adults targeted in a single round the cost per infection averted was $3 . 08 and over two rounds it is estimated to be $4 . 6 . The overall cost per infection averted in the targeted population of children and adults is calculated as $1 . 78 . The higher cost of infection averted in adults reflects the lower base prevalence rate . The longitudinal adult cohort followed up over the 2 year period suffered high drop-out rates and its composition was significant different at the 0 . 05 significance level . Males in particular those who were infected with S . haematobium infection were more difficult to retain at follow-up . The resulting cohort of 116 adults had a lower proportion of males , and had a lower base rate of infection ( 24 . 1% , ( 95% CI:16 . 35–31 . 93 ) ) as compared with the original baseline sample ( 39 . 62% ( 95%CI:35 . 23–44 . 01 ) ) . To avoid this issue the results for the sample monitored at the first year follow up are used and it is conservatively assumed that the prevalence in the second follow up did not change .
The cost of treatment per person is driven by the scale of treatment . The strategy , in Niger , to include targeted adults as well as school age children has increased the treatment numbers and reduced the cost per person treated and increased effectiveness . A conservative estimate of cost effectiveness over 2 years for the treated population is estimated to be $1 . 1 per infection averted for children and $6 . 5 for adults . This study used a targeted treatment strategy; 53% of treatments were to children , but only 16% of the population treated received school based treatment . Under these conditions community based treatment was more cost effective than school based treatment . In Burkina Faso , only school age children were treated; 40% of these received school based treatment ) ; the school based system was more cost effective per treatment . However , the school and community based distribution systems serve overlapping groups in the population; and was designed to facilitate access to treatment for different groups and support a coverage rate of 75% or more in target populations . Any improvement in either system must be the result of improved resource use or increased coverage at the district and programme level if the change is not to impact on the effectiveness of the other system . In designing cost effective and sustainable programmes factors relating to: the treatment strategy , the demographic mix of the population served , system acceptability to stakeholders and the coverage rate need to be taken into account along with logistic issues such as health staff availability . Economic and financial assessment of alternative implementation plans should be undertaken for the project or programme design . This would support decision makers and programme managers , provide financial evidence in planning discussions and negotiations and potentially reduce the need for programme changes to improve cost effectiveness . | Schistosomiasis and soil-transmitted helminth control programmes are important , relatively low cost means to improve the health of those affected , in particular rural school age children . It can also reduce schistosomiasis related morbidity in their later lives . The paper presents information on the implementation and costs of a large scale national programme in Niger . The total economic cost per treatment was $0 . 58 . This includes programme , government and international costs . Two systems , school based and community delivery were used to treat children and targeted adults . Contrary to findings in some countries we find that school based delivery is less cost effective than community delivery . This is due to the low proportion of the population targeted and treated by the school based system . Treating adults as well as children increased the numbers treated and reduced the overall cost per treatment . Prevalence and infection is higher in children than adults and overall effectiveness in terms of infection averted is affected . The cost per infection averted is assessed for direct treatment and direct and indirect treatment effects . The study expands the evidence available for decision makers involved in programme planning and design , funding and implementation . | [
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"Methods",
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"medicine",
"infectious",
"diseases",
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] | 2011 | Schistosomiais and Soil-Transmitted Helminth Control in Niger: Cost Effectiveness of School Based and Community Distributed Mass Drug Administration |
Cyclic GMP-AMP ( cGAMP ) synthase ( cGAS ) stimulator of interferon genes ( STING ) senses pathogen-derived or abnormal self-DNA in the cytosol and triggers an innate immune defense against microbial infection and cancer . STING agonists induce both innate and adaptive immune responses and are a new class of cancer immunotherapy agents tested in multiple clinical trials . However , STING is commonly silenced in cancer cells via unclear mechanisms , limiting the application of these agonists . Here , we report that the expression of STING is epigenetically suppressed by the histone H3K4 lysine demethylases KDM5B and KDM5C and is activated by the opposing H3K4 methyltransferases . The induction of STING expression by KDM5 blockade triggered a robust interferon response in a cytosolic DNA-dependent manner in breast cancer cells . This response resulted in resistance to infection by DNA and RNA viruses . In human tumors , KDM5B expression is inversely associated with STING expression in multiple cancer types , with the level of intratumoral CD8+ T cells , and with patient survival in cancers with a high level of cytosolic DNA , such as human papilloma virus ( HPV ) -positive head and neck cancer . These results demonstrate a novel epigenetic regulatory pathway of immune response and suggest that KDM5 demethylases are potential targets for antipathogen treatment and anticancer immunotherapy .
Evasion from immunosurveillance by cancer cells is a major cancer hallmark [1] , and restoration of immunosurveillance has been demonstrated as an effective antitumor strategy . For example , antibodies targeting inhibitory checkpoint molecules , including programmed cell death protein 1 ( PD-1 ) and cytotoxic T-cell lymphocyte-associated protein 4 ( CTLA-4 ) , have achieved remarkable efficacy in the clinic [2] . However , only a small percentage of patients respond to these therapies . Thus , the mechanisms for lack of response to these treatments are areas of intense investigation . Lack of T-cell infiltration ( also known as immunologically “cold” tumors ) appears to characterize a major subset of patients who do not respond to treatment [3] . Identification of strategies that convert tumors from an immunologically “cold” to “hot” state could enhance immune checkpoint inhibitor therapies and potentially result in the effective treatment of patients who otherwise would not have responded . Pattern recognition receptors ( PRR ) are cell surface and intracellular sensors that recognize pathogen-associated and abnormal-self molecular patterns , e . g . , nucleic acids , and trigger intracellular signaling cascades to activate cell-intrinsic antipathogen or antitumor responses [4] . Cyclic GMP-AMP ( cGAMP ) synthase ( cGAS ) senses pathogen- or abnormally released self-DNA [5 , 6] and signals through stimulator of interferon genes ( STING ) [7] . RNA helicases retinoic acid inducible gene I ( RIG-I ) and melanoma differentiation-associated gene 5 ( MDA5 ) are the main cytosolic RNA sensors—and activate the interferon pathway through mitochondrial antiviral signaling protein ( MAVS ) —whereas toll-like receptors ( TLRs ) respond to pathogen-associated molecular patterns on the cell surface or in endosomal compartments [4] . The downstream pathway of these diverse receptors converges on a few key transcription factors called interferon regulatory factors ( notably IRF3 and IRF7 ) and protein kinases ( such as TANK-binding kinase 1 [TBK1] ) responsible for the phosphorylation and nuclear translocation of IRF3 and IRF7 [8] . Activated IRFs drive the transcription of type I interferons , which bind to their cognate cell surface receptors and lead to the formation of the canonical signal transducer and activator of transcription 1 ( STAT1 ) –STAT2–IRF9 ( also known as interferon-stimulated gene factor 3 [ISGF3] ) complex . The ISGF3 complex binds to the promoters of interferon-stimulated genes ( ISGs ) and activates these genes , many of which mediate the immune response [8] . Emerging evidence suggests that the cGAS-STING pathway plays a critical role in bridging innate immunity and adaptive immunity in tumors [9–11] . However , this pathway is silenced in many tumors , and the mechanisms of their silencing remain largely unknown [12–15] . Tri-methylation on histone H3 lysine 4 ( H3K4me3 ) is enriched near transcription start sites and strongly correlates with active transcription [16] . Methylation on H3K4 , like other histone marks , is dynamically controlled through the concerted action of lysine methyltransferases , the writers , and demethylases , the erasers [16] . The lysine demethylase 5 ( KDM5 ) family proteins—including KDM5A-D ( also known as JARID1A-D ) —are Fe ( II ) - and α-ketoglutarate-dependent dioxygenases and catalyze the removal of the methyl groups from H3K4me3 [17] . The KDM5 family demethylases play major roles in human cancers . KDM5A physically and functionally interacts with tumor suppressor pRb [18] . KDM5B is up-regulated in breast cancer cells overexpressing the ERBB2/HER2 oncogene [19] . Gene amplification of both KDM5A and KDM5B were found in various human cancers [20 , 21] . Studies using cancer cell lines and mouse models demonstrated their functions in promoting tumorigenesis in multiple cancer types [17 , 21–29] . However , the mechanisms by which KDM5 proteins contribute to these phenotypes are still largely unclear . Here , we report that KDM5 demethylases suppress STING-induced innate immune response in tumor cells . We found that KDM5B and KDM5C bind to the STING locus and maintains a low level of H3K4me3 to suppress STING expression . Inhibition or depletion of KDM5B and KDM5C led to increased STING expression in a wide range of cancer cells . In the presence of abnormal cytosolic DNA , the increased STING led to a robust induction of ISGs in breast cancer cells and antiviral response through the cGAS-STING-TBK1-IRF3 pathway . Lastly , we found a strong negative correlation between KDM5B expression and STING expression in The Cancer Genome Atlas ( TCGA ) tumor samples . Our findings reveal a novel epigenetic suppressive mechanism of innate immune response and suggest KDM5 demethylases as attractive targets to boost antitumor immune response .
All 4 family members of KDM5 demethylases ( KDM5A-D ) share sequence and structure similarity [17] , have similar in vitro kinetic parameters [30] , and display functional redundancy [31] . Depletion of individual KDM5 enzymes usually alters histone modification level and gene expression in a context-dependent manner [17] , but the effects of inhibiting multiple KDM5 enzymes remain unclear . Multiple potent pan-KDM5 inhibitors—including KDM5-C49 ( cell active form is KDM5-C70 ) [30 , 32] , Dong-A-167 ( patent WO2016068580 ) , GDC-50 [33] , and CPI-48 [34]—have been reported . These inhibitors are known or predicted to compete with the cofactor α-ketoglutarate in the active site of KDM5 enzymes ( S1A–S1F Fig and S1 Table ) and inhibited KDM5 enzymes with half maximal inhibitory concentration ( IC50 ) values in the nM range ( S1G and S1H Fig and S1 Data ) . We examined the effects of these small-molecule inhibitors on histone modifications and gene expression in MCF7 breast cancer cells . First , global levels of H3K4me3 increased in inhibitor-treated cells ( Fig 1A ) , consistent with previous results [30 , 32–36] . Second , these inhibitors showed minimal effects on other histone methylation marks , including tri-methylation on histone H3 lysine 9 ( H3K9me3—a substrate for the KDM4 family ) , lysine 27 ( H3K27me3—a substrate for the KDM6 family ) , and lysine 36 ( H3K36me3—another substrate for the KDM4 family ) , as well as di- or mono-methylation on histone H3 lysine 4 ( H3K4me2/me1 , substrates for the KDM1/LSD and KDM5 family ) ( Fig 1A ) . Third , KDM5-C70 treatment induced KDM5B and KDM5C protein levels without affecting KDM5A protein level ( S2A Fig ) . It is possible that the induction of KDM5B and KDM5C is due to a feedback regulation , and the mechanism of their differential induction will require further investigation . Fourth , despite the global increase of H3K4me3 , RNA sequencing ( RNA-seq ) analysis of MCF7 cells treated with inhibitors KDM5-C70 and CPI-48 revealed major up-regulation of gene expression only in limited pathways ( S2B Fig ) . The top up-regulated genes are involved in the interferon response pathway ( Fig 1B , S2B Fig and S2 and S3 Data ) . Reverse transcription followed by quantitative PCR ( RT-qPCR ) analysis detected a robust increase of ISGs with direct antiviral activities , such as OAS2 , IFI44L , IFI44 , IFIT1 , and IFIT3 , and chemokine genes involved in immune cell recruitment , such as CXCL10 , upon treatment with inhibitors ( Fig 1C and S2C Fig ) . Phosphorylated STAT1 , which is often required for induction of ISGs [8] , increased along with total STAT1 ( Fig 1D ) . Consistently , other genes involved in type I interferon response were up-regulated , including cytosolic RNA sensors RIG-I and MDA5 , and interferon-regulatory factors IRF7 and IRF9 ( Fig 1D and S2C Fig ) . Treatment of other breast cancer cells SKBR3 and BT474 by compound KDM5-C70 also induced expression of OAS2 , IFI44L , and IFI44 , but to a lesser extent ( S2D and S2E Fig ) . We noted that compound KDM5-C70 at 1 μM significantly induced a global change of H3K4me3 level and targeted gene expression , whereas the other 3 compounds at 10 μM showed similar ( or less ) potency ( Fig 1A and 1D ) , therefore we used 1 μM KDM5-C70 in the remaining study . Depletion of KDM5B or KDM5C , but not KDM5A , mediated by clustered regular interspaced short palindromic repeats/CRISPR-associated protein 9 ( CRISPR/Cas9 ) led to moderately increased expression of ISGs , and knockout of KDM5B and KDM5C synergistically enhanced their expression ( Fig 1E and 1F ) . KDM5D is located in the Y chromosome [17] and thus not expressed in breast cancer cells derived from female patients . Similar effects were observed in cells with small interfering RNA ( siRNA ) -mediated individual and combinatorial knockdown of KDM5B and KDM5C demethylases ( S2F and S2G Fig ) . Compared to the effects of KDM5 inhibitor treatment , the magnitude of ISG activation was slightly lower in KDM5B and KDM5C double knockout cells . It may be due to incomplete depletion of KDM5B and KDM5C in polyclonal knockout cells that we used . Activation of negative feedback pathways during the time required to generate stable cell lines could have also dampened the effects . Ectopic overexpression of a catalytic deficient KDM5B mutant ( H499A ) , but not wild-type KDM5B , dramatically activated expression of ISGs ( Fig 1G ) , suggesting that this KDM5B mutant had dominant negative effects . Collectively , these results showed that the demethylase activities of KDM5B and KDM5C are required to inhibit the interferon pathway . It is well-known that type I interferon establishes an antiviral state [8] . To assess the biological outcome of interferon response induced by KDM5 inhibition , we challenged inhibitor-treated cells with vesicular stomatitis virus ( VSV , a negative-stranded RNA virus ) carrying a green fluorescence protein ( GFP ) reporter ( VSV-GFP ) or vaccinia virus ( a double-stranded DNA [dsDNA] virus ) . Infection by both viruses can be suppressed by treatment with type I interferons [37 , 38] . To exclude the direct effects of KDM5 inhibition on viral infection or reproduction , KDM5-C70 was removed 1 day before infection . We found that pretreatment of cells with KDM5-C70 significantly inhibited VSV-GFP infection ( Fig 2A and 2B ) . Similarly , analyzing the copy number of the viral genome at different time points after vaccinia virus infection revealed that viral replication was significantly restrained in inhibitor-pretreated cells ( Fig 2C ) . As a result , inhibitor-pretreated cells resisted some lytic effects of vaccinia virus ( Fig 2D ) and produced much fewer viruses compared with control cells ( Fig 2E ) . Similar results were obtained when KDM5B and KDM5C were depleted by CRIPSR/Cas9-mediated knockout ( Fig 2F–2H ) . In summary , inhibition of KDM5 enzymes potentiates antiviral innate immunity . We next examined which pathway is required for the interferon response triggered by KDM5 inhibition . Using the CRISPR/Cas9 system , we depleted major components in the interferon-inducing PRR pathways individually , including RIG-I , MDA5 , MAVS , TBK1 , IRF3 , IRF7 , cGAS , STING , and TLR3 ( Fig 3A–3C ) . Efficient knockout of these genes was achieved in polyclonal setting as shown by western blot ( Fig 3B and 3C ) or T7 endonuclease assay ( S3A Fig ) . Depletion of cGAS , STING , IRF3 , or TBK1 largely abolished KDM5-C70-induced expression of IFI44L , ISG15 , and other ISGs ( Fig 3B–3D and S3B and S3C Fig ) . In contrast , loss of RIG-I , MDA5 , MAVS , IRF7 , or TLR3 had minimal effect ( Fig 3B–3D ) . We note that some components in these pathways—including RIG-I , MDA5 , and IRF7—are ISG products themselves , and KDM5-C70 treatment induced the expression of these proteins as well ( Figs 1D , 3B and 3C and S2C Fig ) . Consistently , knockout of essential components in the KDM5-C70-triggered interferon response—such as IRF3 and TBK1—blocked the induction of RIG-I and MDA5 ( Fig 3B ) . These data highlight the predominant roles of cGAS-STING in KDM5-inhibition–dependent activation of ISGs . To further confirm the requirement of the cGAS-STING-TBK1-IRF3 signaling pathway for the KDM5-inhibitor–induced interferon response , we conducted combinatorial knockdown of KDM5B and KDM5C in cGAS , STING , TBK1 , or IRF3 knockout cells . Loss of any of the components in this signaling pathway was sufficient to blunt the KDM5B/C-loss–induced interferon response ( Fig 3E and 3F and S3D Fig ) . Together , these data suggest that activation of interferon response by KDM5 deficiency is dependent on the cGAS-STING-TBK1-IRF3 signaling cascade rather than on direct modulation of ISG expression . Consistent with the activation of ISGs in KDM5 inhibitor-treated cells , we observed increased expression of type I interferon and IFN-β , as well as type III interferons IFN-λ1 and IFN-λ2 , in response to KDM5-C70 treatment ( S3E Fig ) . We also compared the effects of KDM5-C70 treatment to 5 to 500 unit/ml IFN-β treatment on the expression levels of 32 ISGs , most of which have antiviral activity [39] . We found that KDM5 inhibition induced similar patterns of ISGs as IFN-β treatment , and the extent of ISG induction upon KDM5 inhibition is similar to 25 unit/ml IFN-β treatment ( S3F Fig ) . Knockout of individual components of the cGAS-STING-TBK1-IRF3 signaling pathway significantly blocked the effect of KDM5-C70 on the induction of interferons ( S3E Fig ) . Moreover , conditioned media collected from inhibitor-pretreated control MCF7 cells—but not from cGAS- , STING- , TBK1- , or IRF3-deficient cells—were able to activate ISG expression in inhibitor-untreated cells ( S3G Fig ) . Furthermore , loss of any member of the ISGF3 complex , namely STAT1 , STAT2 , and IRF9 , blocked the effects of KDM5-C70 ( Fig 3G and S3H Fig ) . Taken together , our data suggest that inhibition of KDM5 enzymes facilitates the cGAS-STING-TBK1-IRF3 signaling cascade to trigger an interferon response , resulting in increased secretion of interferons and activation of the ISGF3 complex to induce the expression of ISGs . To further determine whether the resistance to viral infection by KDM5 inhibition was also dependent on cGAS-STING-TBK1-IRF3 signaling , we infected inhibitor-treated knockout cells with VSV-GFP or vaccinia virus . Depletion of any member of the cGAS-STING-TBK1-IRF3 signaling cascade , which was required for a KDM5 inhibition-triggered interferon response , diminished the antiviral effects of inhibitor treatment , further confirming the requirement of the cGAS-STING-TBK1-IRF3 pathway for KDM5 inhibition-mediated interferon response ( Fig 3H and S4A–S4C Fig ) . We showed that the cGAS-STING-TBK1-IRF3 axis was required for KDM5 inhibition-triggered interferon response ( Fig 3 ) . The increase of STING after inhibitor treatment does not require IRF3 and TBK1 ( Fig 3B ) , suggesting that STING is directly regulated by KDM5 enzymes in this axis . Both mRNA and protein levels of STING significantly increased after treatment with KDM5-C70 in MCF7 , SKBR3 , and BT474 breast cancer cells ( Fig 4A and 4B ) and was variably up-regulated in most of the other cell lines examined ( S5A–S5D Fig ) . Consistently , knockout or knockdown of KDM5B and KDM5C ( S5E–S5H Fig ) , or overexpression of KDM5B H499A mutant , but not wild-type KDM5B , led to STING increase ( Fig 4C ) . The induction of STING by KDM5 inhibitor treatment or by siRNA-mediated combinatorial knockdown of KDM5B and KDM5C was not affected by cGAS , IRF3 , TBK1 , STAT1 , STAT2 , or IRF9 knockout ( Figs 3B and 4D–4F and S5H Fig ) , excluding the possibility that the increase of STING was secondary to an activated interferon response . This is in contrast to the RNA sensors RIG-I and MDA5 , whose inhibitor-dependent inductions were attenuated upon STING , cGAS , IRF3 , or TBK1 knockout ( Fig 3B ) . Overexpression of STING in MCF7 cells was sufficient to induce an interferon response ( Fig 4G ) , further supporting that increased STING per se was responsible for the interferon response resulting from KDM5 inhibition . To further dissect the mechanisms of STING activation and interferon response , we conducted time course studies to examine the effects of KDM5-C70 on H3K4me3 levels and expression levels of STING and ISGs . The global levels of H3K4me3 increased at day 1 after KDM5-C70 treatment and remained high over time ( Fig 4H ) . STING mRNA levels began elevating at day 1 and peaked at day 3 in all 3 cell lines ( Fig 4I–4K ) . Consistently , STING protein levels also started to increase at day 1 and further increased over time ( Fig 4H ) . In contrast , the activation of ISGs , including RIG-I , MDA5 , IRF9 , and OAS2 , was first seen at day 3 or day 4 ( Fig 4H and 4L ) . Thus , STING induction preceded activation of ISGs , further supporting that STING mediates KDM5 inhibition-induced interferon response . We next asked whether decreasing the level of H3K4me3 , the KDM5 substrate , affects STING expression . The WD40-repeat protein WDR5 is a core component of H3K4 methyltransferase complexes and critical for tri-methylation of H3K4 [40] . Both WDR5 knockout or WDR5 inhibitor OICR-9429 , which prevents the binding of WDR5 to the methyltransferase complexes [41] , precluded H3K4me3 increase by KDM5 inhibition and abolished the effect of KDM5 inhibition on STING expression ( Fig 5A–5C ) . In addition , chromatin immunoprecipitation ( ChIP ) -qPCR analysis showed that H3K4me3 at the promoter of STING is induced by KDM5 inhibitor treatment for 1 day in both MCF7 ( S6A Fig ) and BT474 cells ( S6B Fig ) . In contrast , treatment by KDM5-C70 inhibitor for 1 day had minimal effects on H3K4me3 at the promoters of GAPDH and IFNβ ( S6A and S6B Fig ) . Although H3K4me3 at the promoter of ISGs such as OAS2 and IFI44L increased at day 1 , their increases were much smaller than those at day 6 ( Fig 5D ) . These increases of H3K4me3 were abolished in STING knockout cells ( Fig 5D ) , consistent with the idea that KDM5 loss-triggered interferon response results from increased H4K3me3 at the STING promoter and the subsequent up-regulation of STING . Furthermore , KDM5B binds to the promoter of STING in MCF7 cells ( Fig 5E ) and K562 cells ( Fig 5F ) , while KDM5C binds to the promoter of STING in ZR-75-30 cells ( Fig 5F ) . In contrast , KDM5B and KDM5C do not directly bind to the promoter of cGAS or downstream ISGs , such as OAS2 , IFI44L , and IFI44 ( S6C Fig ) . In comparison , although KDM5A binds to the promoter of a known KDM5A target NDUFA9 [29] , it does not bind to the STING promoter ( Fig 5E ) . These data suggest that KDM5B and KDM5C maintain a low level of H3K4me3 at the STING promoter , suppress STING expression , and prevent the STING-mediated interferon response . We noticed that overexpression of STING was sufficient to trigger a robust interferon response in MCF7 cells ( Fig 4G ) , but knockout of cGAS blocked the induction of interferon response by KDM5 inhibition in these cells ( Fig 3B and 3D ) . These data suggested that MCF7 cells had sufficient cytosolic DNA to bind cGAS and trigger cGAMP production to activate STING but had a low level of STING protein that prevented a robust interferon response . Tumor cytosolic DNA can be derived from mitochondria , nuclear DNA leakage , micro-nuclei , or other sources such as oncoviruses [43–48] . We first examined whether MCF7 cells have cytosolic DNA . MCF7 cells were costained with dsDNA and the mitochondrial marker Hsp60 . As expected , we observed dsDNA in the cytoplasm of MCF7 cells , but most of these dsDNA did not colocalize with mitochondria ( Fig 6A ) . Treatment with dideoxycytidine ( ddC ) , a deoxyribonucleoside analogue that specifically inhibits mitochondrial DNA ( mtDNA ) replication [6 , 46] , led to a dramatic decrease of mtDNA ( Fig 6A , right panel ) and disappearance of cytosolic DNA ( Fig 6A , left panel ) . These results indicated that cytosolic DNA in MCF7 is mainly derived from mitochondria . To test the requirement of cytosolic DNA derived from mitochondria for the induction of interferon response by KDM5 inhibition , we treated MCF7 cells with KDM5-C70 and ddC . Treatment of ddC strongly inhibited the induction of ISGs by KDM5-C70 ( Fig 6B and 6C ) . These results suggest that mtDNA is required for KDM5-inhibition–triggered interferon response in MCF7 cells . In contrast , treatment with leptomycin B ( LMB ) , an inhibitor of nuclear DNA export , prevented the induction of ISGs by Ataxia-telangiectasia mutated ( ATM ) and Ataxia-telangiectasia and Rad3-related protein ( ATR ) inhibitor VE-821 treatment ( S7A Fig ) [49 , 50] but did not suppress the ISG induction by KDM5 inhibition ( S7B Fig ) . These results indicate that nuclear DNA leakage is not the major source of cytosolic DNA in MCF7 cells . Further experiments will be necessary to exclude the possibility that nonmitochondria-derived sources of cytosolic DNA contribute to ISG induction . It is worth mentioning that KDM5 inhibitor treatment did not alter the amount of cytosolic DNA in these cells ( S7C Fig ) . In contrast to MCF7 cells , we observed limited cytosolic DNA in SKBR3 cells ( Fig 6D ) , in which the induction of interferon response by KDM5 inhibition was less robust compared with MCF7 cells ( S2D Fig ) , suggesting that the amount of cytosolic DNA is also a limiting factor for a potent interferon response . To further examine this possibility , we introduced additional cytosolic DNA into SKBR3 cells by transfecting dsDNA , and followed with KDM5-C70 treatment . Treatment with dsDNA or KDM5-C70 alone only led to minimal increase of ISGs , while combinatorial treatment with dsDNA and KDM5-C70 dramatically induced ISGs ( Fig 6E ) . This induction was blocked by knockout of cGAS , STING , TBK1 , or IRF3 ( Fig 6F and S7D Fig ) . These data demonstrate that cytosolic DNA is required for full activation of interferon response upon KDM5 inhibition , suggesting that cancer cells with an elevated level of cytosolic DNA can elicit a strong interferon response upon STING induction by KDM5 loss or inhibition . To validate the regulation of STING by KDM5 in human patients , we compared STING expression levels in “KDM5B low” and “KDM5B high” samples . We found that STING expression level is lower in “KDM5B high” samples than in “KDM5B low” samples from multiple human tumor types , including breast invasive carcinoma , bladder urothelial carcinoma , and ovarian serous cystadenocarcinoma ( Fig 7A ) . To validate the effects of KDM5 on interferon response in tumors with an elevated level of cytosolic DNA , we analyzed human papilloma virus ( HPV; a dsDNA oncovirus ) -induced tumors , such as head and neck cancer and cervical cancer . In HPV+ head and neck cancer , we found significant negative correlation between KDM5B and STING expression , with a Spearman’s correlation of −0 . 465 ( Fig 7B ) . Despite the inability to separate HPV+ and HPV− cervical cancer , we observed significant negative correlation between KDM5B and STING expression in cervical cancer , with a Spearman’s correlation of −0 . 172 ( S8A Fig ) . CXCL10 is one of the interferon-stimulated chemokines that promotes infiltration of immune cells into the tumor microenvironment [10 , 51] . We found CXCL10 expression negatively correlated with KDM5B expression in HPV+ head and neck cancer and positively correlated with STING expression in both HPV+ head and neck cancer and cervical cancer ( Fig 7C and S8B Fig ) . Additionally , we found that CD8+ T-cell infiltration was negatively associated with KDM5B , especially in HPV+ head and neck cancer ( correlation score −0 . 458 ) ( Fig 7D and S8C Fig ) . Lastly , we found a positive correlation between CD8+ T-cell infiltration level and patient survival and a negative correlation between KDM5B expression and patient survival in HPV+ head and neck cancer ( Fig 7E ) . These data show that tumors with high KDM5B expression levels present with low STING expression , suppressed interferon response , and decreased tumor-infiltrating lymphocytes , especially in the presence of abundant cytosolic DNA . As a result , high KDM5B expression is associated with poor prognosis , suggesting KDM5B as a potential target of immunotherapy .
Here , we identified a novel epigenetic regulatory mechanism that tumor cells use to avoid damage caused by cytosolic DNA-triggered innate immune response . Specifically , expression of STING , a key component of the interferon pathway , was silenced by KDM5 family demethylases through removal of H3K4me3 from the STING locus . Suppression of STING by KDM5 demethylase blocked the signal transduction initiated by cytosolic DNA and mediated by the cGAS-STING-TBK1-IRF3 axis ( Fig 7F ) . Inhibition or depletion of KDM5B and KDM5C—by small-molecule inhibitors , siRNA-mediated knockdown , or CRISPR/Cas9-mediated knockout—enhanced STING expression and activated ISGs . The enhanced STING expression was dependent on the activity of H3K4 methyltransferases . This epigenetic regulation allows for a fast , robust , and reversible control of the interferon pathway and is thus expected to have major implications in controlling infection by DNA-containing pathogens and treating cancer . Robust activation of the cGAS/STING pathway requires not only STING activation by cGAMP—generated by cGAS after it binds pathogen-derived or abnormal self-DNA in the cytosol—but also sufficient STING protein to mediate the signal cascade . Although cytosolic DNA is commonly found in tumor cells [44 , 52–56] , cGAS-STING signaling is disrupted or silenced in many tumors , enabling cancer cells to evade immunosurveillance [12–15] . Recent studies showed that the expression levels of cGAS and STING were inversely correlated with DNA methylation and can be activated by a DNA methyltransferase ( DNMT ) inhibitor in a subset of colorectal cancer and melanoma cells [12–14] , indicating that DNA methylation contributes to silencing of the cGAS-STING pathway . Here , we found that STING was up-regulated by KDM5 inhibitors in a panel of cell lines , and the expression levels of KDM5B and STING were negatively associated in multiple tumor datasets . These results suggest that regulation of STING by KDM5 is another common mechanism to modulate the cGAS/STING pathway . Epigenetic changes contribute to tumorigenesis through reprogramming of gene expression profiles [57] . Alternations of epigenetic marks , caused by dysregulation of their writers and erasers , are reversible [58] . This makes epigenetic regulators very attractive drug targets . In fact , inhibitors of epigenetic regulators are either approved or under extensive clinical development , such as inhibitors against DNMTs , Enhancer of zeste homolog 2 ( EZH2 ) , histone deacetylases ( HDACs ) , and bromodomain proteins . Emerging evidence shows that , in addition to their effects on tumor cells , these inhibitors also affect the tumor microenvironment , including immune cells [59] . Previous studies , including ours , have shown that KDM5 family histone demethylases , especially KDM5A and KDM5B , are highly expressed and promote tumorigenesis in multiple cancer types [17 , 21–29] . The mechanisms for their up-regulation in cancer remain largely unknown . KDM5B was identified as a gene up-regulated by HER2 in human breast cancers [19] . KDM5B undergoes post-translational modifications such as SUMOylation by small ubiquitin-like modifier protein ( SUMO ) E3 ligase hPc2 and ubiquitination by ubiquitin E3 ligase RNF4 that mediates KDM5B for proteasomal degradation [60] . KDM5B and KDM5C are also regulated by microRNA ( miRNA ) -137 and miRNA-138 , respectively . Both miRNAs are down-regulated in several breast cancer cell lines compared with nontumorigenic human mammary epithelial cell line MCF10A , consistent with the higher expression levels of KDM5B and KDM5C in these cancer cells [61] . In line with the oncogenic roles of KDM5A and KDM5B , suppression of KDM5A or KDM5B delays tumor formation , metastasis , and drug resistance in breast , lung , melanoma , and gastric cancers [17 , 21–29] . Although inhibition of KDM5C could have adverse effects on neuronal circuits [62] or promote tumor formation in clear cell renal carcinoma [63] and cervical cancer [64] , KDM5C was also shown to have oncogenic roles in prostate cancer [65] . Small-molecule inhibitors of KDM5 enzymes have been developed for cancer treatment [30 , 33 , 34 , 66 , 67] . Here , we find that KDM5 inhibitors trigger a robust interferon response through a STING-dependent manner . Further development of these inhibitors could lead to a new class of cancer immunotherapeutic drugs . The cGAS/STING pathway has been targeted in the clinic to induce both innate immune response and subsequent adaptive immune response for cancer treatment . Small-molecule agonists of STING induce systemic immune responses and regression of established tumors in mice [10 , 68] . However , this strategy is predicted to have limited efficacy in tumors with abnormal cytosolic DNA but silenced STING . In these tumors , such as HPV+ head and neck or cervical tumors , KDM5 inhibitors could be used to restore STING expression and induce antitumor immune responses . Furthermore , while immune checkpoint inhibitors have achieved remarkable success , most patients do not respond to these treatments . A major mechanism of intrinsic resistance to these treatments is due to lack of T-cell infiltration , which could be induced by STING activation . In fact , inhibition of the cGAS/STING pathway prevents the therapeutic effects of immune checkpoint blockade in a mouse model [69] . Therefore , KDM5 inhibitors , or a combination of STING agonists and KDM5 inhibitors , could maximize the antitumor immune response and allow for effective treatment of nonresponders to the current immunotherapies .
Antibody for KDM5A was described previously [26] . The following antibodies were obtained commercially: rabbit anti-histone H3 ( ab1791 ) ( Abcam , Cambridge , UK ) ; rabbit anti-KDM5B ( HPA027179 ) ( Sigma , St . Louis , MO ) ; mouse anti-tubulin ( T5168 ) ( Sigma , St . Louis , MO ) ; mouse anti-STAT1 ( sc-345 ) , −STAT2 ( sc-514193 ) , and −IRF9 ( sc-135953 ) ( Santa Cruz , Dallas , TX ) ; goat anti-Hsp60 ( sc-1052 ) ( Santa Cruz , Dallas , TX ) ; rabbit anti-KDM5C ( A301-034A ) ( Bethyl , Montgomery , TX ) ; rabbit anti-H3K4me3 ( C42D8 ) , −H3K4me1 ( D1A9 ) , −H3K4me2 ( C64G9 ) , −H3K9me3 ( D4W1U ) , −H3K27me3 ( C36B11 ) , −H3K36me3 ( D5A7 ) , −RIG-I ( D14G6 ) , −MDA5 ( D74E4 ) , −STING ( D2P2F ) , −cGAS ( D1D3G ) , −IRF3 ( D83B9 ) , −TBK1 ( D1B4 ) , −MAVS ( 3993 ) , −IRF7 ( 4920 ) , −Phospho-STAT1 ( 58D6 ) , and −HA ( C29F4 ) ( Cell Signaling Technology , Danvers , MA ) ; and mouse anti-dsDNA ( MAB1293 ) ( Millipore , Burlington , MA ) . pcDNA3 . 1-3xHA-KDM5B construct was described previously [66] . An H499A mutation was introduced into KDM5B plasmid by site-directed mutagenesis . pcDNA3 . 1-3xHA-STING construct was generated by PCR amplification of the full length of STING coding sequence from cDNA and inserting into pcDNA3 . 1-3xHA vector between BamHI and XhoI sites . Compound OICR-9429 , VE821 was purchased from Sigma . LMB was purchased from Santa Cruz ( Dallas , TX ) ( sc-202210 ) . KDM5-C70 ( NCGC00371443 ) was purchased from Xcess Biosciences ( San Diego , CA ) . Compounds Dong-A-167 ( NCGC00487054 ) , GDC-50 ( NCGC00482457 ) [33] , and CPI-48 ( NCGC00488278 ) [34] were prepared according to patents WO2016/68580 , WO2016/57924 , and WO2015/135094 , respectively . The linked KDM5A JmjN-JmjC catalytic domain was prepared and purified by 3-column chromatography utilizing affinity , anion exchange , and sizing exclusion as previously described in detail [70] . The purified protein , in 20 mM Hepes ( pH 8 . 0 ) , 300 mM NaCl , 5% glycerol , and 0 . 5 mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) , was mixed with MnCl2 and αKG at an approximate molar ratio of 1:5 and concentrated to approximately 10 mg/ml ( 280 μM ) for co-crystallization as described [30] . Inhibitor CPI-48 was soaked into these preformed crystals of KDM5A-αKG-Mn ( II ) complexes by transferring a crystal into a new drop containing mother liquor ( 1 . 2–1 . 35 M [NH4]2SO4 , 0 . 1 M Tris-HCl [pH 8 . 6–9 . 2] , 0%–20% glycerol , and 25 mM [Na/K] dibasic/monobasic phosphate ) and CPI-48 ( approximately 500 μM ) , allowing the crystal to remain in this drop overnight for CPI-48 to exchange with αKG . The crystals were then mounted into nylon cryoloops ( Hampton Research , Aliso Viejo , CA ) and frozen in liquid nitrogen after the addition of more glycerol ( up to approximately 30% total ) to the mother liquor as a cryoprotectant . X-ray diffraction data were collected SER-CAT beam-line 22-ID at the Advanced Photon Source at Argonne National Laboratory at 100 K with 1-degree oscillation images , and the structure was determined by molecular replacement and refinement performed as described ( S1 Table ) [30] . AlphaLISA assays were performed and analyzed as described previously [30] with 25 nM KDM5A ( BPS Biosciences , San Diego , CA; 50110 ) , 10 nM KDM5B ( 1–755 ) ΔAP [70] , 20 nM KDM5B [30 , 66] , or 25 nM KDM5C ( BPS Biosciences , San Diego , CA; 50112 ) . MCF7 and BT474 cells were cultured in RPMI1640 supplemented with 10% fetal bovine serum and 1% penicillin and streptomycin . SKBR3 cells were cultured in Dulbecco’s Modified Eagle Medium supplemented with 10% fetal bovine serum and 1% penicillin and streptomycin . siRNA transfections were performed using RNAiMAX ( Invitrogen ) , and plasmid transfections were performed using Lipofectamine 3000 ( Invitrogen ) according to the manufacturer’s instructions . The sequence of dsDNA90 was described previously [71] , transfected using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instructions . siRNA universal negative control 1 and 2 were purchased from Sigma ( SIC001 and SIC002 ) . siKDM5A targeting sequences were described previously [29] . Other siRNA targeting sequences were as follows: siKDM5B-1 , CAGTGAATGAGCTCCGGCA; siKDM5B-2 , GGAGCTGACATTGCCTCAA; siKDM5C-1 , GGAGGAAGGTGGTTATGAA; and siKDM5C-2 , GGAGGAAGGTGGTTATGAA . Histone extraction was conducted as described previously [66] . sgRNAs were designed using CHOPCHOP ( https://chopchop . rc . fas . harvard . edu/ ) and cloned into LentiCRISPRv2 . Knockout cells were generated as described previously [31] . Briefly , 293T cells in 6-well plates were introduced with 1 . 5 μg lentiviral plasmid , 1 μg psPAX2 , and 0 . 5 μg pMD2 . G . At 48 hours after transfection , lentivirus-containing media were collected and filtered through a 0 . 45 μm filter before being used to infect cells . Cells were infected with lentivirus for 24 hours , then refed with fresh medium with puromycin . sgRNA controls were described previously [67] . Other sgRNA targeting sequences are listed in S2 Table . T7 endonuclease assays were conducted as described previously [31] . The primers for amplifying the region flanking TLR3 sgRNA targeting site were as follows: TLR3-F , TCATGAGACAGACTTTGCCTTG; and TLR3-R , GGCTATACCTTGTGAAGTTGGC . Vaccinia viruses are recombinant vaccinia virus ( vTF7-3 , strain WR ) expressing T7 RNA polymerase [72] . They were kindly provided by Linda Buonacore and Dr . John Rose ( Yale University , New Haven , CT ) . VSV-GFP viruses ( VSV-G/GFP , Indiana strain ) were generated as described previously [73] . MCF7 cells were infected and incubated at MOI indicated in the figure legends for the indicated time . FACS analyses were performed using a Stratedigm 13-color cytometer with cells fixed in 4% paraformaldehyde . FACS plots were first gated on live cells before analyzing viral GFP fluorescence . Viral copy numbers of vaccinia virus were determined by quantification of pox14KD [74] . For immunostaining , cells were seeded on coverslips , fixed with 4% paraformaldehyde for 10 minutes , permeabilized with 0 . 4% Triton in PBS for 5 minutes , and then blocked with 10% FBS before incubation with primary antibodies at 4°C overnight . dsDNA staining and image processing were performed according to previous studies [54 , 55] . For DNase I–treated samples , cells were permeabilized with 10 μg/ml digitonin and 50 μg/ml DNase I for 30 minutes at 37 °C before fixation with 4% paraformaldehyde . Z-stack images were taken using Leica SP5 confocal microscope . Surface rendering of 3D Z-stacks were processed using Huygens with threshold levels set based on DNase I–treated samples . ChIP assays were conducted as described previously [75] . Total RNA was isolated using RNeasy Plus Mini Kit ( Qiagen , Hilden , Germany ) . Reverse transcription was performed using High-Capacity cDNA Reverse Transcription Kit ( ABI , Sterling , VA ) . For both ChIP-qPCR and RT-qPCR , qPCR analyses were performed in triplicate using Fast SYBR Green Master Mix ( Applied Biosystems , Foster City , CA ) . The primers for RT-qPCR analysis of ISG15 , RIG-I , MDA5 , IFNβ , IFNλ1 , and IFNλ2 were described previously [76] . Other primers for RT-qPCR are listed in S3 Table . The primers for ChIP-qPCR are listed in S4 Table . MCF7 cells were treated with 3 μM of KDM5-C70 or CPI-48 for 6 days . Total RNA was isolated using RNeasy Plus Mini Kit ( Qiagen , Hilden , Germany ) . mRNA libraries for sequencing were prepared according to the standard Illumina protocol . Sequencing ( 100 bp , paired-end ) was performed using Illumina HiSeq 2000 sequencing system at the Genomics Core of Yale Stem Cell Center . RNA-seq data were deposited in the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus database under accession number GSE108502 . The RNA-seq reads were mapped to human genome ( hg38 ) with Bowtie2 [77] in local mode , which allows the reads spanning the exon–exon junctions to get mapped to one of the 2 exons ( whichever gives the higher mapping score ) independent of the transcriptome annotation . The uniquely mapped reads ( cutoff: MAPQ >10 ) were counted to ENCODE gene annotation ( version 24 ) [78] using FeatureCounts [79] . Differential gene expression was performed with DESeq2 [80] . Gene expression profiles of DMSO- or KDM5-inhibitor–treated cells were used for GSEA using GSEA version 2 . 0 software [81] . The gene set database of h . all . v6 . 1 . symbols . gmt ( Hallmarks ) was used . Statistical significance was assessed by comparing the enrichment score to enrichment results generated from 10 , 000 random permutations of the gene set . TCGA expression datasets were downloaded using the Broad Institute Firehose application programming interface ( https://gdac . broadinstitute . org ) . Expression data are in log2 RSEM format . For each TCGA dataset , primary tumor samples were ranked by their expression of KDM5B and evenly divided into 4 groups . Samples with KDM5B expression less than the first quartile were deemed “KDM5B low , ” while samples with KDM5B expression greater than or equal to the third quartile were deemed “KDM5B high . ” Statistical comparisons were performed between the STING expression of the samples in “KDM5B low” and “KDM5B high” groups . Significance was computed using the Student t test . For box plots , the lower and upper hinges signify the first and third quartiles , respectively , while the center line depicts the median . The whisker tips correspond to the first observation beyond 1 . 5 times the interquartile range . Outliers are illustrated with points . R scripts are available upon request . The correlation between KDM5B and clinical impact in HPV-positive head and neck cancer or cervical cancer were analyzed using a web server TIMER ( https://cistrome . shinyapps . io/timer/ ) [82 , 83] . The correlation between KDM5B and STING , KDM5B and CXCL10 , or STING and CXCL10 were adjusted by tumor purity . KDM5B and input ChIP-seq data were obtained from the ENCODE K562 dataset ( GSE29611 ) in bigwig format . KDM5C wild-type and knockout ChIP-seq data were obtained from GSE71327 [42] , aligned with Bowtie2 , and processed into bigwig using Deeptools [84] . All signal tracks were visualized using IGV [85] . Statistical significance was determined using the unpaired Student t test . Error bars represent SEM . SEM was calculated from triplicate technical replicates of each biological sample or 2 or 3 biological replicates . Data shown were representative of 3 independent experiments or biological replicates as indicated in figure legends . | Pathogens often find ways to turn down cell-intrinsic antipathogen immune responses by the host . Similarly , cancer cells use various mechanisms to evade attack by immune cells . One of the common mechanisms is suppression of the stimulator of interferon genes ( STING ) -dependent innate immune response . Using potent and specific small-molecule inhibitors and genetic-depletion approaches , we found that the silenced STING pathway can be reactivated in breast cancer cells by suppressing KDM5 demethylases . Activation of the STING pathway led to a robust interferon response , which blocked viral infection , and was associated with increased tumor-infiltrated lymphocytes and better patient survival in multiple cancer types . This discovery has major clinical implications for treating both pathogen infection and cancer because KDM5 inhibition provides a fast , robust , and reversible control of innate immune response . Since the discovery of histone demethylase activity of KDM5 proteins a decade ago , significant efforts have been dedicated to developing KDM5 inhibitors for clinical applications . In fact , a KDM5 inhibitor recently entered phase I clinical trial for treatment of hepatitis B infection . Here , we provide mechanistic insights on how KDM5 inhibitors block viral infection . Moreover , our results suggest that KDM5 inhibitors can also be combined with other cancer immunotherapies . | [
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"viruses",
"oncol... | 2018 | KDM5 histone demethylases repress immune response via suppression of STING |
Flaviviruses bud into the endoplasmic reticulum and are transported through the secretory pathway , where the mildly acidic environment triggers particle rearrangement and allows furin processing of the prM protein to pr and M . The peripheral pr peptide remains bound to virus at low pH and inhibits virus-membrane interaction . Upon exocytosis , the release of pr at neutral pH completes virus maturation to an infectious particle . Together this evidence suggests that pr may shield the flavivirus fusion protein E from the low pH environment of the exocytic pathway . Here we developed an in vitro system to reconstitute the interaction of dengue virus ( DENV ) pr with soluble truncated E proteins . At low pH recombinant pr bound to both monomeric and dimeric forms of E and blocked their membrane insertion . Exogenous pr interacted with mature infectious DENV and specifically inhibited virus fusion and infection . Alanine substitution of E H244 , a highly conserved histidine residue in the pr-E interface , blocked pr-E interaction and reduced release of DENV virus-like particles . Folding , membrane insertion and trimerization of the H244A mutant E protein were preserved , and particle release could be partially rescued by neutralization of the low pH of the secretory pathway . Thus , pr acts to silence flavivirus fusion activity during virus secretion , and this function can be separated from the chaperone activity of prM . The sequence conservation of key residues involved in the flavivirus pr-E interaction suggests that this protein-protein interface may be a useful target for broad-spectrum inhibitors .
The emergence and resurgence of human viral pathogens can be traced to a complex variety of causes including increased urbanization , human contact with animal reservoirs , a decrease in effective public health systems , and the spread of insect vectors that disseminate some viral infections [1] , [2] , [3] . Flaviviruses are a genus in the Flaviviridae family and include important emerging and resurgent human pathogens such as dengue virus ( DENV ) , West Nile virus ( WNV ) , tick-borne encephalitis virus ( TBEV ) and yellow fever virus [2] , [4] . Flaviviruses are transmitted by insects such as mosquitoes and ticks , and can cause severe human diseases characterized by encephalitis , meningitis , and hemorrhages [2] , [3] . More than one third of the world's population lives in dengue fever endemic areas , and there are an estimated 50–100 million cases of dengue infection and 500 , 000 cases of the more lethal complication , dengue hemorrhagic fever , per year [5] , [6] , [7] , [8] . There are currently no antiviral therapies for flaviviruses . DENV vaccine development is underway but is problematic due to the presence of four DENV serotypes and the potential for antibody-dependent enhancement of infection [2] , [6] , [9] , [10] . Antiviral therapies could thus be an important alternative for DENV and for viruses such as WNV in which the cost and potential side effects of vaccination must be weighed against the relatively low number of human cases [2] . Flaviviruses are small , highly organized enveloped viruses with a spherical shape [4] , [11] . They contain a positive-sense RNA genome packaged by the viral capsid protein . The nucleocapsid is surrounded by a lipid bilayer containing the viral membrane protein E . Flaviviruses infect cells by receptor engagement at the plasma membrane , endocytic uptake , and a membrane fusion reaction triggered by the low pH of the endosome compartment [12] , [13] . The viral E protein binds the receptor and drives the fusion of the viral and endosome membranes to initiate virus infection . The pre-fusion structure of the E protein ectodomain ( here referred to as E′ ) shows that E contains three domains composed primarily of β-sheets: a central domain I ( DI ) connecting on one side to the elongated domain II ( DII ) with the hydrophobic fusion loop at its tip , and connecting via a flexible linker on the other side to the immunoglobulin-like domain III ( DIII ) [14] , [15] , [16] , [17] , [18] , [19] ( Figs . 1A , S1 ) . Although these regions are not present in the truncated E′ ectodomain , DIII connects to a stem domain and C-terminal membrane anchor ( TM ) . The E protein in mature infectious flavivirus is organized in homodimers that lie tangential to the virus membrane [20] . Within each dimer the E proteins interact in a head to tail fashion , with the fusion loop of each E protein hidden in a hydrophobic pocket formed by DI and DIII of the dimeric E partner . The E protein mediates virus-membrane fusion by refolding to a hairpin-like E homotrimer with the fusion loops and TM domains at the same end [21] , [22] . This reaction involves low pH-triggered dissociation of the homodimer , fusion loop insertion into the endosome membrane , formation of a core trimer composed of DI and DII , and the foldback of the DIII and stem regions towards the target membrane and their packing against the core trimer . The prefusion and postfusion conformations of the flavivirus E fusion protein are structurally and functionally similar to those of the E1 fusion protein from the alphavirus Semliki Forest virus ( SFV ) [23] , [24] , [25] , and these fusion proteins are often referred to as “class II” [26] , [27] , [28] . In addition to the ectodomains whose trimer structures are described above , truncated fusion proteins composed of domains I and II ( DI/II ) can reconstitute SFV and DENV core trimer formation on target membranes [29] , [30] . Such core trimers act as specific targets for DIII binding , thus recapitulating the protein-protein interactions during class II trimerization and hairpin formation . Flaviviruses bud into the endoplasmic reticulum ( ER ) and are transported as virus particles through the secretory pathway and released by exocytosis [4] . Given the low pH that is present in the Golgi complex and trans-Golgi network ( TGN ) [31] , how do flaviviruses avoid inactivation during their transport ? The particles are assembled in the ER as immature non-infectious viruses containing heterodimers of the precursor membrane protein ( prM ) and E protein [4] , [26] , [32] . Subsequent exposure to low pH in the secretory pathway triggers a dramatic rearrangement to E homodimers and makes the prM protein accessible to furin cleavage [33] , [34] . Processing of prM by cellular furin results in mature infectious virus in which E homodimers are poised to mediate fusion [33] . Important recent studies describe the structure of pr peptide in complex with E , and indicate that processed pr remains associated with the virus at low pH and can inhibit virus-membrane interaction [34] , [35] , [36] . Thus , pr on the virus could protect E protein from low pH in the secretory pathway . The flavivirus prM/pr protein plays multiple roles in the virus life cycle ( reviewed in [26] ) . prM acts as a chaperone for E protein folding [37] and associates with the tip of E [34] . prM also appears to respond to low pH to permit E rearrangement on the virus surface and allow furin access for prM processing [34] , [38] . Following cleavage , the pr peptide may prevent premature virus fusion through bridging interactions that stabilize the E homodimer and thereby prevent dissociation to E monomers , a key fusion intermediate [35] , [36] . To better understand these multiple roles of prM/pr , separation of its chaperone and pH-protection functions and characterization of the pr-E interaction are needed . Here we developed a system to produce DENV pr peptide and reconstitute the pr-E interaction in vitro . At low pH pr bound to both monomeric and dimeric forms of E and blocked their membrane insertion and trimerization . Addition of exogenous pr to mature DENV particles inhibited virus fusion and infection . Mutation of a key histidine residue in the pr-E interface , E H244 , reduced pr's binding and inhibitory activity , and reduced DENV secondary infection and particle production . The defect in particle production could be partially rescued by neutralization of exocytic low pH , indicating the important role of pr in protecting DENV from premature fusion during transport to the plasma membrane .
A number of truncated E proteins have been successfully produced by co-expression with prM ( e . g . , references [30] , [39] ) , while the pr-E structural studies were based on a secreted hybrid protein containing truncated prM linked to truncated E [34] . Previous studies indicated that full-length TBEV prM could fold correctly when expressed in the absence of E protein [37] , suggesting that production of pr peptide alone might be possible . We generated a construct based on residues 1–86 of DENV2 prM , truncating pr just before the start of the furin cleavage recognition site at residue 87 ( Fig . 1A ) . This sequence was linked to a mammalian signal peptide at the N-terminus and to an affinity tag at the C-terminus , and expressed in 293T cells . The protein was isolated in a highly purified form by affinity chromatography and gel filtration ( Fig . 1B ) , and was recognized by mAb prM-6 . 1 against prM [40] ( data not shown ) . The pr peptide migrated at a position of ∼17 kDa in reducing SDS-PAGE , in keeping with its predicted size of 13 kDa plus the presence of carbohydrate due to the glycosylation site at position 69 . This carbohydrate was removed by Peptide N-glycosidase F ( PNGase F ) to give a peptide of the predicted size . The protein was largely resistant to Endoglyosidase H ( Endo H ) digestion , indicating maturation of the carbohydrate chain as the protein transited through the Golgi complex . A mobility shift was observed upon reduction of pr , in keeping with the presence of 3 disulfide bonds in the structure of pr [34] . We also produced and purified a dimeric ectodomain form of DENV2 E protein containing all three domains ( E′ ) , a monomeric form containing E domains I and II ( DI/II ) , and E domain III ( DIII ) ( Fig . 1A and 1C ) , all as previously described in detail [30] , [41] . As a first test of in vitro pr-E binding , we coupled pr to sepharose beads and tested its ability to pull-down truncated E protein containing only domains I and II . This form of E protein is monomeric and the tip of DII is thus accessible even at neutral pH . Previous studies showed that this and other DENV DI/II proteins are active in membrane insertion and trimerization at both neutral and low pH [30] . We observed efficient pull-down of DI/II protein by pr-sepharose ( Fig . 2A ) , but in spite of the accessibility of the pr binding site on DI/II at neutral pH , pull-down was low pH-dependent . The pull-down of DI/II protein by pr was specific , as it was blocked by inclusion of mAb 4G2 against the E fusion loop at the DII tip , and did not occur with BSA-sepharose beads . These data suggested that the recombinant pr peptide could bind to the tip of DI/II in a low pH-dependent reaction . For more detailed studies of pr-E binding , we performed surface plasmon resonance ( SPR ) assays using our various forms of recombinant E protein with immobilized pr peptide . Compared to the pull-down assay , SPR can detect low levels of protein-protein interactions as binding is detected in real time and does not require removal of unbound E . The E′ protein is a dimer at neutral pH and dissociates to monomers at low pH [30] . When SPR was performed with E′ protein buffered at pH 8 . 0 there was very low binding ( low signal response ) ( Fig . 2B ) . As the buffer pH was decreased , the signal gradually increased , with maximal response observed at ∼pH 6 . 25 and no further increase at pH 6 . 0 . A rapid decrease in signal was observed when the samples were shifted to protein-free buffer , indicating rapid dissociation of the pr-E interaction . Similar results were obtained using monomeric DI/II , with the lowest binding at pH 8 . 0 , highest binding at pH 6 . 25 , and a slight decrease at pH 6 . 0 ( Fig . 2C ) . Thus , the dimeric E′ and monomeric E DI/II proteins bound pr peptide with similar pH-dependence . Binding to pr was specific , as little interaction was observed using the structurally similar E1 DI/II protein of SFV ( Fig . 2D ) . In addition , binding of DENV E DI/II protein to pr was inhibited by preincubation with mAb 4G2 against the fusion loop ( molar ratio 1∶1 ) ( data not shown ) . Determination of the affinity of pr-E binding was not performed as the data did not fit to a simple Langmuir model of 1∶1 binding , presumably because of E protein aggregation at low pH . Previous studies showed that retention of endogenous pr peptide on the furin-processed DENV particle inhibits virus interaction with liposomes at low pH [35] . Structural considerations suggested that this inhibition occurs primarily by blocking low pH-triggered dissociation of the E dimer , a required first step in the fusion reaction . To test this mechanism , we evaluated the effect of pr on the membrane interactions of dimeric and monomeric forms of E protein . The E′ dimer was preincubated with pr peptide or an unrelated protein with the same affinity tag for 5 min at pH 8 . 0 , and then treated at pH 5 . 75 in the presence of target liposomes . Membrane-associated proteins were separated by liposome floatation on sucrose gradients . There was no liposome co-floatation when E′ protein was incubated with liposomes at neutral pH ( Fig . 3A ) . About 70% of the total E′ floated with liposomes in the top part of the sucrose gradient after treatment at pH 5 . 75 in the presence ( Fig . 3A , top panel ) or absence ( data not shown ) of a control protein . In contrast , when E′ was preincubated with pr peptide ( pr∶E′ molar ratio 12∶1 ) and treated with low pH , only ∼2% of E′-ST floated with the liposomes ( Fig . 3A , middle panel ) . Inhibition by pr was not observed when it was added after E′ was treated at low pH in the presence of liposomes for 30 min ( Fig . 3A , bottom panel ) , and thus pr needed to be present during the membrane insertion step . Inhibition was concentration-dependent , with 22% E′ co-floatation at a pr∶E′ molar ratio of 3∶1 , 8% at 6∶1 , and 0 . 4% for 24∶1 ( data not shown; see also Fig . 3E ) . We then tested the effect of pr on the DENV E DI/II protein . This protein is monomeric and its stable membrane interaction requires DIII to “clamp” the core trimer [30] . As shown in Fig . 3B , ∼25% of DI/II co-floated with liposomes at low pH in the present of DIII , while no co-floatation was detected when BSA was substituted for DIII protein . The addition of pr peptide blocked membrane interaction of DI/II when added prior to liposome incubation ( Fig . 3B , 3rd panel ) , but not after liposome incubation ( Fig . 3B , bottom panel ) . The structurally related alphavirus protein SFV E1 DI/II is monomeric and efficiently interacts with membranes at low pH ( 80% cofloatation , Fig . 3C , middle panel ) . No inhibition occurred when pr peptide was added prior to liposome addition ( Fig . 3C , bottom panel ) , in keeping with the lack of pr-SFV DI/II binding in the SPR experiments discussed above . Thus , pr peptide specifically inhibits target membrane interaction of both monomeric and dimeric forms of the DENV E protein . E′ protein efficiently inserted into membranes over a wide range of pH values from 6 . 25-4 . 5 ( Fig . 3D–E ) . However , pr's inhibition of E membrane insertion was less efficient in the pH range ( pH 5 . 0 ) present in the late endocytic pathway ( Fig . 3D–E ) . This loss of pr inhibition at more acidic pH may be relevant to recent studies of infection by immature DENV [42] , as mentioned in the discussion section below . All of the results above were obtained with soluble forms of the E protein . In order to test the ability of exogenous pr peptide to interact with and inhibit intact DENV , we took advantage of a previously described assay that monitors low pH-triggered fusion of DENV with cells [41] . In this fusion-infection assay , virus is pre-bound to target cells on ice , and then treated at 37°C for 1 min at low pH to trigger virus fusion with the plasma membrane . This fusion reaction is then quantitated by detecting the infected cells by immunofluorescence . We tested the effect of pr peptide during this 1 min low pH treatment using DENV1 WP and DENV2 NGC . The sequence of E DI/II is 68% identical between these two serotypes . Both serotypes showed efficient fusion and infection after treatment at pH 6 . 0 , with about a 10-fold increase compared to samples treated at pH 7 . 9 ( Fig . 4 ) . The addition of pr peptide during the 1 min low pH treatment strongly inhibited DENV fusion and infection . Inhibition was dose-dependent , with 45–49% inhibition at 6 µM pr and 81–85-% inhibition at 30 µM pr . In contrast , pr did not inhibit low pH-triggered fusion by the alphavirus SIN ( Fig . 4 ) . Thus , exogenous DENV2 pr peptide can specifically interact with mature DENV1 and DENV2 to block virus fusion and infection . We did not observe inhibition when DENV was preincubated with 30 µM pr at pH 7 . 0 and then added to target cells in a standard infection assay , suggesting that under these conditions an inhibitory concentration of pr was not present during low pH-triggered fusion reaction in the endosome . This result also indicates that the presence of pr did not affect virus-cell binding . Although the interaction of pr with DENV can clearly prevent virus-membrane interaction and fusion ( this study and [35] ) , the importance of pr in protecting DENV during exocytic transport has not been defined . The binding interface between prM and E contains three complementary electrostatic patches containing 11 residues [34] ( see also Fig . S1 ) . Sequence analysis shows that these 11 residues ( Fig . 5A , numbered residues ) are highly conserved among the 4 DENV serotypes , and that D63 and D65 of pr , and the complementary H244 on E protein are conserved among all reported flavivirus sequences [34] . Optimal pr-E binding in vitro occurred at ∼pH 6 . 25 ( Fig . 2 ) , suggesting that protonation of H244 could be involved in this pH-dependence . To test this we substituted alanine for H244 in the DI/II protein . DI/II H244A was produced in highly purified form with electrophoretic mobility similar to that of the wild type ( WT ) protein in reducing and non-reducing SDS-PAGE ( Fig . 1C ) . We first tested the effect of the H244A mutation on pr-E binding . In agreement with our earlier results , WT DI/II protein was efficiently pulled-down by pr-sepharose ( Fig . 5B ) . Pull-down was low pH-dependent and blocked by mAb 4G2 against the E fusion loop at the DII tip . In contrast , almost no H244A DI/II protein was pulled-down by pr-sepharose at either low pH or neural pH ( Fig . 5B ) . SPR analysis of WT DI/II protein showed most efficient binding at pH 6 . 0 , and binding was blocked by pre-incubating the DI/II protein with mAb 4G2 ( molar ratio 1∶1 ) before dilution into SPR buffer ( Fig . 5C , upper panel ) . Equivalent concentrations of H244A DI/II protein showed greatly reduced binding to pr compared to that of WT protein ( Fig . 5C , lower panel ) . Although H244A binding was decreased , the residual binding was still blocked by mAb 4G2 and had an acidic pH optimum . This suggests that binding also involves other residues in the pr-E interface , such as the complementary residues identified in the structural studies and shown in Fig . 5A . We then asked if the H244A DI/II protein was still active in binding to target liposomes . WT or mutant DI/II proteins were mixed with liposomes at low pH in the presence of DIII protein to stabilize the core trimer . Both proteins efficiently bound liposomes in a DIII-dependent reaction ( Fig . 6 ) , indicating that the mutant protein retains its ability to insert into target membranes and form a core trimer . In agreement with the results in Fig . 3C , floatation of the WT protein was blocked by inclusion of pr during the membrane insertion step ( Fig . 6 ) . In contrast , the efficiency of floatation of the H244A mutant protein was 43% in the absence of pr and 47% in the presence of pr . Thus , the H244A mutation did not inhibit E-membrane interaction but made that interaction insensitive to the presence of pr . Since the E H244A mutation disrupts E protein's interaction with pr , we used this mutation to address the importance of pr in protecting DENV during transport through the exocytic pathway . We introduced the E H244A mutation into the infectious clone of DENV1 WP . WT and mutant viral RNAs were prepared by in vitro transcription and were electroporated into BHK cells . After culture for 3 d at 37°C , both WT and mutant RNA-electroporated cells expressed abundant E protein as detected by immunofluorescence microscopy ( Fig . 7 ) . Parallel cultures were incubated for 6 d and progeny virus in the culture media was detected by infectious center assays on indicator BHK cells . WT-infected cells produced infectious progeny virus with a titer of ∼1 . 5×105 IC/ml . However , two independent infectious clones of the H244A mutant produced no detectable progeny virus , even though the viral RNAs mediated efficient primary infection as shown in Fig . 7 . This agrees with previous studies indicating lethal effects of an H244A mutation on DENV2 [43] . The absence of secondary infection by the H244A DENV1 mutant could be due to decreased virus particle production and/or production of particles that are non-infectious . Efficient DENV particle production is dependent on E protein folding , particle budding into the ER , and subsequent particle egress through the secretory pathway . To investigate these issues , we took advantage of the ability of the flavivirus prM and E proteins to assemble into virus-like particles ( VLP ) in the absence of other viral components or virus infection [44] , [45] , [46] . The VLP system avoids complications arising from selection of revertants of deleterious virus mutations such as H244A . Flavivirus VLP bud into the ER in the immature prM form , undergo furin maturation during transport through the secretory pathway , and display similar low pH-dependent fusion activity as infectious virions [44] , [47] . The VLP system has been used extensively to follow the process of flavivirus particle production and the role of prM in this process [37] , [44] , [45] , [48] . We established stable HEK 293 cells that inducibly express the DENV1 WT or H244A prM-E proteins . After 36 h induction with tetracycline , both WT and H244A cells show abundant intracellular expression of the DENV1 E protein as detected by immunofluorescence , while the parent cell line is negative for E expression ( Fig . 8A ) . To evaluate whether WT and H244A E proteins were correctly folded , cells were induced for 36 h , lysed , and immunoprecipitated with a rabbit polyclonal antibody to E DIII , and with two conformation-specific mAbs . mAb 4E11 recognizes a discontinuous epitope on DENV E DIII and requires proper DIII disulfide bond formation for recognition [49] , [50] . mAb 4G2 recognizes the fusion loop at the tip of flavivirus E DII and its epitope is sensitive to reduction [51] . Expression studies have shown that the 4G2 epitope is not formed if the E protein is expressed in the absence of prM [52] , indicating that this epitope is particularly useful for diagnostic tests of prM's chaperone interaction with E ( see also reference [37] ) . As shown in Fig . 8B , lysates from cells induced to express prM plus WT or H244A E proteins showed strong reactivity with all three antibodies . Quantitation of multiple experiments confirmed that WT and H244A E proteins were comparably recognized by the 4E11 and 4G2 mAbs . Thus , by these criteria H244A E protein interacts with prM protein and is correctly folded . This result also agrees with our finding that truncated H244A E protein expressed with prM in the S2 cell system was fully active in low pH-dependent membrane binding and trimerization , suggesting correct folding ( Fig . 6 ) . We then used the inducible cells to examine VLP production . Expression was induced for 36 h . The cells were then lysed and the E proteins immunoprecipitated , and the VLP in the culture media were pelleted by ultracentrifugation . Analysis by western blotting showed strong E protein expression in both WT and H244A cells , and no expression in the parent cells ( Fig . 8C ) . The WT cells released E protein in VLP , but VLP release from cells expressing the H244A mutant E protein was greatly reduced ( Fig . 8C , - media samples ) . This result is in keeping with the hypothesis that the H244A cells assemble VLP in the neutral pH environment of the ER but that VLP release is inhibited by the lack of pr protection from the low pH of the secretory pathway . To test this idea , we induced WT and H244A prM-E expression and cultured the cells in the presence of 20 mM NH4Cl to neutralize the acidic pH in the Golgi and TGN compartments ( Fig . 8C , +NH4Cl lanes ) . The cellular expression level of either E protein was not significantly affected by NH4Cl treatment , and WT VLP production was similar in NH4Cl-treated cells and untreated cells . However , production of VLP containing the H244A mutant E protein was increased 4–7 fold in NH4Cl-treated cells . While H244A VLP production was still significantly decreased compared to that of WT , it was selectively rescued by NH4Cl treatment .
The in vitro interaction of pr with various truncated forms of E protein was strongly pH-dependent , with a pH optimum of ∼6 . 25 . In situ measurements indicate that the pH of the TGN is ∼6 [53] , while the pH optimum of DENV2 NGC fusion is ∼6 . 2 [41] . The low pH of the TGN is critical for the rearrangement of immature DENV to allow furin cleavage , but once the virus is processed it becomes fusion-active in this same pH range . Thus the pH dependence of the pr-E interaction appears optimized to protect DENV during its continued transit through the secretory pathway . Pr's inhibition of E membrane insertion was less efficient at a pH value ( pH 5 . 0 ) similar to that in the late endocytic pathway ( Fig . 3D–E ) . This loss of pr inhibition at more acidic pH could help to explain the recent finding that infection by immature DENV is enhanced by antibodies to prM [42] . The antibody-bound immature virus is likely to be endocytosed and processed by cellular furin in the endocytic pathway [54] . The lower pH conditions of the late endocytic pathway could then cause the loss of pr inhibition and allow virus fusion . The structure of furin-cleaved DENV at pH 6 . 0 shows that pr is bound to the virion through interactions with the DII tip of one E protein and DI on the neighboring E monomer [35] , [36] . This suggested that pr might primarily block virus-membrane interaction by preventing dissociation of E dimers , a required first step in the fusion pathway [55] . Our results show efficient binding of pr to the dimeric form of the DENV E protein , but also to the monomeric DI/II form . We do not know if the E′ protein dimer is stabilized by pr interaction or if the dimer dissociates prior to interaction with pr , and experiments to address these points were inconclusive ( data not shown ) . The similar pH dependence of pr binding to monomeric and dimeric E proteins suggests that pr may bind the same site in both cases . mAb 4G2 against the fusion loop inhibited pr interaction with E DI/II , confirming that pr was binding to the DII tip rather than to other sites on expressed E proteins . In keeping with its binding site in the vicinity of the fusion loop , pr peptide blocked the membrane insertion and liposome co-floatation of E′ and DI/II proteins . Prior studies showed that a monomeric DI/II protein with a single Strep affinity tag stably inserts into liposomes at either neutral or low pH [30] , and pr blocked this insertion even at pH 8 . 0 where its interaction with DI/II was suboptimal ( data not shown ) . Thus , while the pr-E interaction is strongly low pH-dependent , its functional inhibition of membrane insertion can still be observed at neutral pH in the presence of excess pr . Several other studies have addressed the role of E H244 in the flavivirus lifecycle . Experiments in TBEV evaluated particle production and membrane fusion activity using a VLP system [56] . Mutation of H248 ( TBE numbering ) to A or I blocks VLP secretion , in agreement with our results . However , an H248N mutant efficiently produces VLP , and these particles show WT levels of fusion activity . WNV E H246A or Q mutations inhibit release of infectious reporter virus particles from cells , as do a number of other substitutions at this position [57] . Replacement of H246 with aromatic residues such as phenylalanine allows both particle release and infectivity . An H244A mutation in DENV2 NGC inhibits infectious virus production [43] . E H244 and its interacting partners D63 and D65 on pr are conserved within the flaviviruses , and thus these data from several flaviviruses plus our DENV results support an important role for the E 244 position . However , a histidine residue at this position does not seem to be strictly required for particle production , suggesting that substitutions such as 244F and 244N can support the interaction of E with pr . In contrast to the block in production of H244A VLP , the H244A DI/II protein was efficiently secreted from cells . Mutant protein secretion was somewhat reduced , with the final yield of DI/II H244A about half that of the WT protein in two separate preparations ( data not shown ) , suggesting some effects of non-optimal pr interaction . However , unlike the E protein in virus or VLP , the truncated DI/II protein lacks the TM region and does not mediate membrane fusion , and thus may be relatively independent of the pH-protection function of pr . The purified WT and mutant DI/II proteins were able to bind liposomes and form core trimers that were stabilized by DIII ( Fig . 6 ) . Thus , the mutant protein is correctly folded and active in membrane insertion . Studies with conformation-specific mAbs also provided evidence for the correct folding of H244A E protein ( Fig . 8B ) . Together , these results suggest that the H244A E protein is still able to access the chaperone functions of prM , while its decreased pr binding indicates that it can no longer utilize the pH protection functions of pr . These data are consistent with the idea that , similar to WT E , the mutant protein is assembled with prM into VLP in the ER . The membrane insertion and trimerization activity of H244A suggest that the full-length mutant protein would be fusion-active on such VLP once they are transported from the neutral pH of the ER to the low pH of the Golgi and TGN [31] . Thus , the decreased release of H244A VLP and its partial rescue by neutralization of the exocytic pathway support a critical role for pr in protecting DENV from exocytic low pH , and suggest that virus/VLP fuses in the TGN in the absence of pr-E interaction . Rescue of H244A VLP production by NH4Cl was clearly incomplete . This may be due to complex aspects of both virus and cell , such as direct effects of the H244A mutation on particle assembly in the ER , or difficulties in blocking fusion of a virus with the relatively high pH threshold of DENV . Several strategies have been used to block flavivirus and alphavirus fusion reactions and thus inhibit virus infection . SFV and DENV fusion are specifically blocked by exogenous DIII , which binds to the core trimer and prevents the foldback of endogenous DIII and hairpin formation [41] . A later stage in DENV fusion is targeted by a stem-derived peptide , which binds to the ectodomain trimer in which DIII has folded back but stem packing has not yet occurred [58] . These virus protein-protein interactions can be reconstituted in vitro [29] , [30] , [58] , opening the possibility of using them as screens for small molecule inhibitors of virus fusion and infection . The in vitro reconstitution of the pr-E interaction using soluble components could also act as a screen for small molecule inhibitors of this important flavivirus protein-protein interaction . Such inhibitors could act at multiple points in the virus lifecycle . During virus protein biosynthesis , an inhibitor could block the chaperone interaction of prM with E , leading to misfolding of E and its elimination by the ER quality control pathway . An inhibitor of pr interaction could make E protein susceptible to premature fusion in the TGN and could thus block virus production similar to the H244A mutation . It is also possible that small molecule inhibitors of pr-E binding could interact directly with the DII tip on mature virus particles , perhaps stabilizing the dimer and/or blocking membrane insertion of the fusion loop , thereby blocking virus fusion . Thus the in vitro system we describe here has the potential to identify molecules that could aid in the study of the flavivirus lifecycle and that could act to inhibit specific steps . Previous studies showed that after cleavage endogenous pr is retained on the virus particle if the virus is maintained at acidic pH [35] . Under these conditions , the virus-pr complex does not bind target membranes , while virus from which pr is first released at neutral pH efficiently binds membranes upon shift to acid pH . Thus , the bound endogenous pr inhibits virus-membrane interaction and presumably blocks virus fusion [35] . Our results demonstrated that even after maturation to fully infectious DENV particles , exogenous pr could add back to the virus and inhibit low pH-triggered virus fusion and infection . The flavivirus membrane fusion reaction is very rapid , occurring within seconds of low pH treatment [47] . Recombinant DENV2 pr peptide inhibited fusion by both DENV1 and DENV2 , suggestive of a fairly broad spectrum inhibition in agreement with the strong sequence conservation at the pr-E interface [34] . The structure of the flavivirus E protein in its pre-fusion and post-fusion conformations defines the dramatic conformational changes between these two states . Many questions about the intermediates that connect the pre- and post-fusion conformations remain . In particular , it will be important to define the membrane protein rearrangements in the context of the highly organized flavivirus particle . For example , a neutralizing E mAb that blocks virus fusion was used to trap a West Nile virus fusion intermediate [59] . It will be interesting to evaluate if exogenous pr peptide could also be used as a novel probe to capture intermediates in the flavivirus fusion pathway .
BHK-21 cells and C6/36 mosquito cells were cultured as described previously [60] . 293T cells and T-REx™-293 cells were cultured as previously described using tetracycline-deficient fetal calf serum for the latter cells [61] . The DENV2 New Guinea C ( NGC ) strain and the DENV1 Western Pacific ( WP ) strain were propagated in C6/36 cells in DMEM containing 2% heat-inactivated fetal calf serum and 10 mM Hepes , pH 8 . 0 , as previously described [41] , [62] . Sindbis virus expressing green fluorescent protein was obtained as an infectious clone ( a kind gift from Dr . Hans Heidner ) and propagated in BHK cells [63] . 4G2 is a mouse monoclonal antibody ( mAb ) that recognizes the fusion loop of flavivirus E proteins [51] , [64] . mAb prM-6 . 1 recognizes a linear epitope on prM , and was a kind gift of Drs . Chunya Puttikhunt and Nopporn Sittisombut [40] . 4E11 is a mouse mAb that recognizes DIII of DENV E protein and neutralizes all 4 serotypes of dengue virus [49] , [50] , and was a kind gift of Dr . Fernando Arenzana-Seisdedos ( Institute Pasteur , Paris ) . The anti-DIII polyclonal antibody Sango was raised by immunization of a rabbit with purified DENV2 DIII protein [30] . Western blot detection of truncated E proteins used 4G2 or Sango antibodies . A mAb to β-actin was obtained from Sigma and used to confirm equivalent loading of cell lysate samples . Immunofluorescence detection of DENV-infected cells used the antibody to DIII or mouse polyclonal anti-DENV2 hyperimmune ascitic fluid ( obtained from Robert B . Tesh , University of Texas Medical Branch ) , with Alexa Fluor 488 or rhodamine-conjugated secondary antibodies ( Molecular Probes ) . The sequence encoding residues 1–86 of pr was amplified by PCR of an expression plasmid for DENV2 NGC prM-E DI/II [30] . The PCR product was ligated into the pPUR vector ( Clontech ) , with the 21-residue TPA signal peptide [65] fused at the N-terminus and a tandem Strep tag at the C terminus ( Fig . 1 ) . The plasmid , referred to as pPUR-TPA-pr-STST , was transfected into 293T cells using polyethylenimine ( PEI , Polysciences ) . For optimal protein production , 3 . 5×106 cells were plated per 10 cm dish and cultured for 24 h in 10 ml of complete medium . 7 . 5 µg plasmid in 1 ml DME was mixed with 30 µg PEI , incubated 10 min , then added drop wise to the cell culture medium . After 12 h , the medium was changed to 10 ml DME plus 2% serum . The culture medium was collected after 48h and again after 72h . Pr was purified by affinity chromatography on a Strep-Tactin column from IBA BioTAGnology and by gel filtration using a Sephadex G75 column [30] . Final yields were ∼2 mg purified protein/1 liter culture supernatant . Truncated DENV E proteins ( Fig . 1 ) were obtained by inducible expression in Drosophila S2 cells and purified by affinity chromatography as previously described in detail [29] , [30] . The H244A mutation was introduced into the DI/II protein by in vitro mutagenesis , and S2 cell expression and purification were performed as above . DENV2 NGC DIII ( Fig . 1 ) was previously referred to as LDIIIH1CS [30] , and contains domain III , the linker between domain I and domain III , and the H1 and CS regions of the stem domain . DIII was expressed in E . coli and refolded as previously described [41] . SFV E1 DI/II protein was produced as previously described [29] . All purified proteins were stored in TAN buffer ( 20 mM Triethanolamine[TEA] , pH 8 . 0; 130 mM NaCl ) at −80°C . SDS-PAGE analysis was performed using 10–12% acrylamide gels with a Bis-Tris buffer system ( Invitrogen ) . Western blots were performed with Alexa Fluor 688-conjugated secondary antibodies ( Molecular Probes ) , and were quantitated using an Odyssey Infrared Imaging system and Odyssey InCell Western software ( LI-COR Biosciences ) [30] . Standard curves with purified E proteins confirmed the linearity of this analysis ( data not shown ) . Pr or BSA was coupled to NHS-activated sepharose 4 fast flow ( GE Healthcare ) as described in the manual . In brief , sepharose was washed with 1mM HCl , and incubated with 660 µg pr or BSA/ml in 0 . 2 M NaHCO3 , 0 . 5 M NaCl , pH 8 . 3 at room temperature for 1 . 5 hr . The reaction was quenched with 0 . 1M Tris-HCl pH 8 . 5 for 30 min and free protein removed by washing with PBS . About 1mg of protein was coupled to 1ml beads . For the pull-down assay , 3 µg DI/II protein was pre-incubated where indicated with 24 µg 4G2 ( molar ratio 1∶2 ) or control mAb for 10 min at room temperature , and then incubated for 1 h on a rocker at room temperature with 10 µl of pr- or BSA-sepharose in a buffer containing 20 mM MES , 20 mM TEA , 130 mM NaCl , 0 . 2% Tween 20 at pH 8 . 0 or 6 . 25 . The beads were then washed twice with the corresponding buffer and the bound DI/II was analyzed by SDS-PAGE and western blot . SPR studies were performed on a BIAcore 2000 instrument ( GE Healthcare ) . Purified recombinant pr was immobilized on a CM5 biosensor chip by primary amine coupling as described in the manual . In brief , pr peptide was diluted to 10 µg/ml in 10 mM sodium acetate pH 4 . 7 and pre-concentrated on the chip surface . The chip was then activated by a mixture of 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide and N-hydroxysuccinimide , followed by quenching with 1M ethanolamine at pH 8 . 5 . Under these conditions , pr was immobilized to a final density of 600 or 1000 response unit ( RU ) . A control cell was mock-coupled with protein-free solutions . To test interaction , truncated E proteins were diluted to 1 . 2 mM in a MES/TEA buffer ( 20 mM MES , 20 mM TEA , 130 mM NaCl ) at a pH range of 6 . 0 to 8 . 0 , and flowed over the chip for 300 s at 0 . 3 µl/min , followed by buffer alone at the same flow rate . After each round , the chip was regenerated by washing with 50 mM NaOH in 1 M NaCl . The pr chip showed undiminished E binding activity for at least 50 rounds . Liposomes were prepared by freeze-thaw and extrusion through 200 nm polycarbonate filters [66] , and were stored at 4°C in TAN buffer under N2 and used within 2 weeks of preparation . Liposomes were composed of a 1∶1∶1∶3 molar ratio of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) , 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine ( POPE ) , sphingomyelin ( bovine brain ) ( Avanti Polar Lipids; Alabaster , AL ) , and cholesterol ( Steraloids , Inc . ; Wilton , NH ) , plus trace amounts of 3H-cholesterol ( Amersham; Arlington Heights , IL ) . Protein-membrane interaction was monitored using a liposome co-floatation assay [29] , [30] . E′ or DI/II proteins at a final concentration of 50 µg/ml were incubated in TAN buffer ( pH 8 . 0 ) for 5 min at 28°C in the presence of 200 µg pr peptide/ml as indicated . Liposomes were then added to a final concentration of 1mM lipid and the samples were adjusted to pH 5 . 75 by the addition of 0 . 3 M MES or maintained at pH 8 . 0 , and the incubation continued at 28°C for 30–60 min . The samples were then adjusted to 20% sucrose and loaded on top of a 300 µl cushion of 40% sucrose , then overlaid with 1 . 2ml 15% sucrose and 200 µl 5% sucrose . All sucrose solutions were at the same pH as the samples , and were wt/wt in TAN buffer at pH 8 . 0 or in MES buffer ( 50 mM MES , 100 mM NaCl ) at pH 5 . 5 . Gradients were centrifuged for 3 hr at 54 , 000 rpm at 4°C in a TLS55 rotor , and fractioned into the top 700 µl , middle 400 µl and bottom 1 ml . The 3H-cholesterol marker was quantitated by scintillation counting . 200 µl of each fraction were precipitated with 10% trichloroacetic acid and analyzed by SDS-PAGE and western blotting [29] . Purified human secreted placental alkaline phosphatase with a ST affinity tag ( Seap ) was used as a control protein [67] , and was a kind gift from Yves Durocher , Biotechnology Research Institute , Montreal . The fusion-infection assay was performed essentially as described previously [41] . In brief , BHK cells grown on 96-well plates were washed twice with ice cold binding medium ( RPMI without bicarbonate , 0 . 2% BSA , 10 mM Hepes , and 20 mM NH4Cl , pH 7 . 9 ) . Virus stocks were diluted in binding medium and incubated with cells on ice for 3 h with gentle shaking . Cells were washed twice with binding medium to remove unbound virus and pulsed for 1 min at 37°C in 100 µl RPMI without bicarbonate , containing 0 . 2% BSA , 10 mM Hepes and 30 mM sodium succinate at pH 6 . 0 or 7 . 9 , containing the indicated concentration of pr peptide . Infected cells were incubated in MEM plus 2% FCS and 50 mM NH4Cl for 4 h at 37°C , and then at 37°C for 2 d in the presence of 20 mM NH4Cl . The number of infected cells was quantitated by immunofluorescence using mouse polyclonal anti-DENV2 antibody . Infection observed at pH 7 . 9 represents virus that is endocytosed and fuses during 1 min at this pH . The DENV1-WP infectious clone ( reference [68] , a kind gift from Dr . Barry Falgout ) was digested with KpnI and a 3 . 3kb fragment including the E sequence was sub-cloned into the pGEM3Z vector to generate pGDENV1 3 . 3 . pGDENV1 3 . 3 was used as a template to generate the E H244A mutation , using circular mutagenesis as previously described [69] . A 2 . 6kb BstB1/XhoI fragment containing the H244A mutation was sub-cloned into the DENV1-WP infectious clone to obtain DENV1-E H244A . The mutation was confirmed by restriction analysis and sequencing of the complete prM-E region . Two independent infectious clones were used to confirm the phenotype . The WT and the mutant infectious clones were linearized by Sac II digestion and used as templates for in vitro transcription [70] . RNAs were electroporated into BHK cells and cells were cultured overnight at 37°C followed by 6 d at 28°C in MEM containing 2% FBS and 10 mM HEPES , pH 8 . 0 . Progeny virus in the medium was quantitated by infectious center assay on indicator BHK cells , using mouse polyclonal anti-DENV2 antibody . To detect primary infection , aliquots of the electroporated cells were plated on coverslips , cultured 3 d at 37°C , and processed for immunofluorescence microscopy as above . WT and E H244A mutant DENV1 prM-E sequences were PCR-amplified from the pGDENV1 3 . 3 subclones described above , and cloned into pcDNA4/TO ( Invitrogen ) . These constructs were transfected into T-REx™-293cells using Lipofectamine 2000 ( Invitrogen ) and selected in T-REx HEK medium containing 125 µg/ml Zeocin , all as previous described [61] . To test E protein folding and expression , 1×106 WT and mutant E expressing cells were seeded in 10 cm plates , cultured for 24h , and then E protein expression was induced by culture for 36 h in 1 . 5 µg/ml tetracycline in DME medium with 10% FCS at 37°C . Cells were lysed in RIPA buffer ( 50 mM Tris-HCl pH 7 . 4 , 150mM NaCl , 1% NP40 , 0 . 5% Na-deoxycholate , 0 . 1% SDS , 1mM PMSF , 1× Roche complete protease inhibitor cocktail ) on ice for 1 hr . The cell lysates were cleared by centrifugation for 30 min at 10 , 000×g and protein concentrations were quantitated and normalized . E proteins were immunoprecipitated from cell lysate samples ( 500 µg total cellular protein ) using 20 µg purified mAb 4G2 or mAb 4E11 and 20 µl protein-G sepharose , or 30 µl Sango antibody and 20 µl protein-A sepharose . 4E11 and 4G2 immunoprecipitated samples were blotted with Sango . Sango immunoprecipitated samples were blotted with mouse anti DENV2 serum . For VLP secretion studies , 2–3×106 cells were seeded in 10 cm plates , cultured for 24h , and then induced by culture for 36 h in 1 . 5 µg/ml tetracycline in DME medium with 10% FCS at 37°C . The culture media were centrifuged at 10 , 000×g for 30 min to remove cell debris . VLPs were then pelleted through a 0 . 5 ml sucrose cushion by centrifugation at 54 , 000 rpm for 2 h at 4°C using a TLS55 rotor . To test the effect of neutralizing the pH of acidic cellular compartments , cells were seeded and induced as above . After 2 h of induction the media were changed to DME medium containing 20 mM HEPES pH 8 . 0 , 2% FCS , and 1 . 5 µg/ml tetracycline plus 20 mM NH4Cl as indicated , and the incubation continued for a total of 36 h . E proteins in the cell lysates were immunoprecipitated using mAb 4G2 . VLP and lysate samples were then analyzed by SDS-PAGE and western blot using Sango . | Enveloped viruses infect cells by fusing their membrane with that of the host cell . Dengue virus ( DENV ) is an important human pathogen whose membrane fusion is triggered by low pH during virus entry into the cell . However , newly synthesized DENV must also transit through a low pH environment during virus exit . DENV is believed to escape premature fusion in the exit pathway via the small viral protein pr , which is processed and associates with virus after biosynthesis , and is released from the virus particle in the neutral pH extracellular environment . Here we have reconstituted the interaction of pr with the DENV fusion protein E using soluble protein components . The interaction has a low pH optimum and inhibits membrane insertion of the fusion protein . The recombinant pr peptide can “add back” to fully infectious mature DENV and block virus fusion and infection . We found that mutation of a critical conserved histidine on the fusion protein inhibits the interaction of E and pr , and makes the virus susceptible to low pH-induced inactivation during exit . This work characterizes the mechanism of pr protection , and suggests that the conserved multifunctional pr-E interaction may be an important target for anti-viral strategies . | [
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] | 2010 | In Vitro and In Vivo Studies Identify Important Features of Dengue Virus pr-E Protein Interactions |
Adult neurogenesis in the dentate gyrus plays a critical role in hippocampus-dependent spatial learning . It remains unknown , however , how new neurons become functionally integrated into spatial circuits and contribute to hippocampus-mediated forms of learning and memory . To investigate these issues , we used a mouse model in which the differentiation of adult-generated dentate gyrus neurons can be anticipated by conditionally expressing the pro-differentiative gene PC3 ( Tis21/BTG2 ) in nestin-positive progenitor cells . In contrast to previous studies that affected the number of newly generated neurons , this strategy selectively changes their timing of differentiation . New , adult-generated dentate gyrus progenitors , in which the PC3 transgene was expressed , showed accelerated differentiation and significantly reduced dendritic arborization and spine density . Functionally , this genetic manipulation specifically affected different hippocampus-dependent learning and memory tasks , including contextual fear conditioning , and selectively reduced synaptic plasticity in the dentate gyrus . Morphological and functional analyses of hippocampal neurons at different stages of differentiation , following transgene activation within defined time-windows , revealed that the new , adult-generated neurons up to 3–4 weeks of age are required not only to acquire new spatial information but also to use previously consolidated memories . Thus , the correct unwinding of these key memory functions , which can be an expression of the ability of adult-generated neurons to link subsequent events in memory circuits , is critically dependent on the correct timing of the initial stages of neuron maturation and connection to existing circuits .
Observations in mammals and birds have revealed that neurogenesis continues in the dentate gyrus of the hippocampus throughout adulthood , due to the presence of progenitor cells localized in the innermost part of the granule cell layer , the subgranular zone ( SGZ , [1–3] ) . These progenitor cells continue to proliferate and generate new dentate granule neurons for the entire life of the organism [4 , 5] . The process of adult hippocampal neurogenesis originates from dividing putative neural stem cells [6] and has been tentatively divided into six developmental stages [7] , in which putative neural stem cells ( named type-1 cells ) develop into post-mitotic neurons through three consecutive stages of progenitor cells ( type-2ab and type-3 cells; [8–10] ) . This process is thought to govern the number and the differentiation of adult-generated neurons [11] . Newborn neurons become functionally integrated into existing dentate gyrus circuitry within 3 wk , extending their axons to CA3 , as indicated by morphological and electrophysiological studies [12–20] . Interestingly , recent observations indicate that new neurons of the dentate gyrus become functionally active in learning circuits at late stages of their maturation ( ∼4–6 postnatal weeks , [21] ) . Adult hippocampal neurogenesis is required for hippocampus-dependent learning and memory [22 , 23] . Indeed , the almost complete ablation of neurogenesis by an antimitotic toxin , x-ray irradiation , or virus-activated pro-drugs results in profound cognitive deficits [24–28] . On the other hand , learning and/or physical exercise can enhance neurogenesis in the dentate gyrus , suggesting a two-way relationship between the generation of new neurons in the adult hippocampus and cognitive processes [9 , 29–33] . Recently , it has been hypothesized that new , adult-generated neurons confer to the dentate gyrus the basic ability to encode the timing of new memories by integrating new events on the pre-existing memory circuits [23] . The strategies used so far to reveal the functional role of adult neurogenesis deeply affected the total number of new neurons , either by severely reducing neuronal progenitors [24 , 26] or by increasing the number of new neurons generated [29] . Nonetheless , the ablation of new neurons by toxin or irradiation in some instance did not affect certain hippocampus-dependent tasks of spatial learning , such as water maze or contextual fear conditioning [24 , 26 , 34 , 35] . In this context , the role of the specific differentiation steps during neurogenesis and dynamics of integration of new neurons into existing circuitry remains unknown [36] . To address this issue , we used the approach of selectively enhancing the differentiation of progenitor cells in adult dentate gyrus . We conditionally expressed the gene PC3 ( also known as Tis21 or BTG2 , see [37] for review; GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession number M60921 ) in nestin-positive progenitor cells ( type-1 and type-2; [7] ) of the adult dentate gyrus . PC3 is normally expressed in neuronal precursors immediately before the last asymmetric division [38 , 39] and , during neurogenesis , it is known to induce their terminal differentiation in several areas of the CNS [40 , 41] . We found that nestin-driven expression of PC3 resulted in premature differentiation of adult-generated dentate gyrus neurons , with a reduction in the number of type-1 and type-2 neuronal progenitors . This genetic manipulation did not change the overall number of newly generated neurons; rather it altered their differentiation timing and resulted in profound changes in newborn neuron morphology . Remarkably , early PC3 expression caused a severe impairment in performance on the different hippocampus-dependent spatial learning and memory tests used and resulted in a selective reduction in synaptic plasticity in the dentate gyrus . These results indicate that early stages of adult neurogenesis are crucial for the correct functional integration of newborn neurons into cognitive hippocampal circuits .
Up-regulation of PC3 induces neural precursors to shift from proliferation to differentiation , thereby promoting the generation of new neurons [41] . Such enhancement of neurogenesis has been observed so far in neuroblasts of the neural tube and in cerebellar granule cell precursors , and follows inhibition of G1 to S phase progression and stimulation of proneural genes [41 , 42] . We reasoned that conditionally controlling the differentiation of progenitor cells in the adult dentate gyrus would allow a selective analysis of their involvement in spatial memory networks . Thus , to start , we tested whether the differentiation of adult-generated newborn dentate gyrus neurons was influenced by PC3 , which begins to be physiologically expressed in progenitor cells before differentiation , at the moment of their final mitosis ( SF-V and FT , unpublished data ) . Up-regulation of PC3 was activated earlier in nestin-expressing adult hippocampal stem and progenitor cells ( type-1 and type-2; [7] ) of bitransgenic nestin-rtTA/TRE-PC3 mice ( hereafter named TgPC3 ) . This was achieved by conditional expression from postnatal day 30 ( P30 ) onward of the transgene under control of the nestin promoter , through administration of doxycycline , as previously shown [41] . Sixty days after activation of the PC3 transgene , mice received five daily injections of BrdU ( at P90–P94 ) to detect new progenitor cells and/or neurons , immediately followed by immunohistochemical analyses . Analysis of the expression of transgenic PC3 in P95 mice with the PC3 transgene active ( named TgPC3 ON mice ) indicated targeting to the dentate gyrus , as visualized by X-gal staining , which revealed the β-galactosidase activity of the β-geo reporter gene fused to the nestin-rtTA transgene ( Figure 1A ) . Among the different cell populations of the dentate gyrus , the glial fibrillary acidic protein ( GFAP ) -expressing astroglia are considered to be dividing putative neural stem cells from which neuronal progenitor cells originate , given their ability to repopulate the dentate gyrus after cytotoxic ablation of dividing cells [6] . These stem cells are identified primarily by the expression of GFAP , accompanied by nestin or also Sox2 [8 , 10 , 43 , 44] and have been defined as type-1 cells [7] . We found that , in TgPC3 ON mice at P95 , the BrdU+/GFAP+/nestin+ cells—corresponding to 1–5-d-old type-1 cells—decreased significantly with respect to TgPC3 OFF control mice ( about 40%; Figure 1B , p < 0 . 0003 ) . The whole GFAP-positive astroglial cell population decreased only slightly , confirming the selectivity of effect on type-1 cells ( Figure 1C ) . Since no difference between TgPC3 OFF and wild-type ( WT ) mice was found in these and in the following immunohistochemical analyses ( unpublished data; p > 0 . 05 ) , only TgPC3 OFF were considered as controls . Next , we analyzed the transiently amplifying progenitor cells derived from type-1 cells . These progenitors express nestin but lack GFAP and astrocytic features and are divided into two subgroups based on the absence or presence of the immature neuronal marker , doublecortin ( DCX ) ( named type-2a and type-2b , respectively; [8–10] ) . A further group of progenitor cells lacks nestin but expresses DCX ( type-3; [9] ) . We observed that TgPC3 ON mice presented a significant decrease of 35% in the whole type-2a/type-2b population of 1–5-d-old newborn progenitor cells ( identified as BrdU+/nestin+/GFAP–; p < 0 . 02 , Figure 1D ) and of 45% in type-2b progenitor cells ( i . e . , BrdU+/nestin+/DCX+; p < 0 . 002 , Figure 1E ) . Moreover , TgPC3 ON mice also showed a reduction , albeit not significant , of type-3 progenitor cells ( identified as BrdU+/nestin−/DCX+; Figure 1F ) , consistent with the fact that at this stage , the nestin promoter becomes physiologically inactive and thus ceases to drive the expression of transgenic PC3 . A control of the expression of transgenic PC3 in nestin-positive type-1and type-2ab progenitor cell types , as determined by β-galactosidase expression , is shown in Figure S1A–S1D . Type-3 progenitor cells generate early post-mitotic neurons identified by expression of the neuronal-specific differentiation marker , NeuN ( stage 5 and 6; [45] ) . In contrast to progenitor cells type-1–type-3 , in TgPC3 ON mice the number of early post-mitotic , differentiated neurons up to 5 d old expressing NeuN increased considerably—more than twice than in control mice ( identified as Brdu+/NeuN+ as well as Brdu+/DCX+/NeuN+; p < 0 . 0001; Figure 1G and 1H , respectively ) . Representative images of Brdu+/DCX+/NeuN+ cells are shown in Figure 1L and 1M . These new neurons , which increased in number by activation of the PC3 transgene , were also positive for NeuroD1 ( Brdu+/NeuroD1+/NeuN+; Figure 1I ) , whose expression begins in type-2b progenitor cells , is maintained in post-mitotic hippocampal granule neurons , and is required for differentiation [46 , 47] . We conclude that the nestin promoter-driven expression of transgenic PC3 accelerates the shift of adult-generated hippocampal stem and progenitors cells towards a post-mitotic , terminally differentiated phenotype , corresponding to NeuN-expressing neurons stage 5 and 6 . Notably , PC3 transgene activation did not change the total number of 1–5-d-old new progenitors generated in the dentate gyrus ( BrdU+ cells analyzed at P95 after completion of BrdU injections; Figure 1J ) or the total number of 4-wk-old new neurons ( BrdU+/NeuN+ cells analyzed at P116; Figure 1K ) . The effects of transgene activation on the relative abundance of new dentate gyrus progenitor cell types and neurons is summarized in Figure 1N . In addition , no significant change was detected in the number of proliferating progenitor cells of the whole dentate gyrus in mice with active transgene with respect to control mice , as detected by Ki67 labeling ( Figure S2A ) . Furthermore , an analysis of proliferating progenitor cell types expressing Ki67 showed that in TgPC3 mice , the number of type-1 ( Ki67+/nestin+/GFAP+; p > 0 . 05 ) , type-2ab ( Ki67+/nestin+/GFAP–; p < 0 . 01 ) and type-2b ( Ki67+/nestin+/DCX+; p < 0 . 01 ) decreased , whereas that of type-3 progenitor cells increased significantly ( Ki67+/nestin–/DCX+; p < 0 . 01; Figure S2B–S2F ) . Together , these data indicate that PC3 selectively accelerates the transition of stem and progenitor cells ( type-1 to type-3 ) toward a post-mitotic differentiated state without affecting the following: ( i ) the total number of proliferating cells; ( ii ) the total number of newly generated neurons; or ( iii ) the final number of neurons differentiated . The decrease in the number of proliferating Ki67+/nestin+ type-1–type-2ab progenitor cells expressing PC3 is consistent with the notion that PC3 expression is associated to the neurogenic asymmetric type of division in neuroblasts [37–39] . To test whether activation of the PC3 transgene caused nonspecific changes , a stereological analysis of the hippocampus was conducted . Activation of the PC3 transgene after P30 did not alter the volume of the dentate gyrus or the whole hippocampus ( Figure S3A and S3B ) or the total cell number in the dentate gyrus ( Figure S3C ) . Moreover , there was no evidence of reduced cell survival either in the whole hippocampus , as defined by TUNEL analysis ( Figure S3D ) , or within different subpopulations , as defined by labeling with the apoptotic marker caspase-3 [48] ( Figure S3E–S3J ) . Also , no evident alteration in distribution of mature granule neurons or in morphology was detected in the hippocampus or in the whole brain ( unpublished data ) . In conclusion , these results show that the activation of the PC3 transgene accelerates the process of differentiation of dentate gyrus progenitor cells , thus providing a tool to specifically analyze the relationship between timing of hippocampal neurogenesis and spatial learning . We wished also to verify whether the expression of PC3 had any effect on nestin-positive neural cells in the other adult neurogenic niche , i . e . , the subventricular zone ( SVZ ) , comprising type B astrocytic-like progenitors , type C transit amplifying cells , and type A migrating neuroblasts [49–51] . We observed that the number of type B and type C cells , labeled by BrdU/GFAP and BrdU/NG2 , respectively , did not change significantly in TgPC3 ON mice ( p > 0 . 05; Figure S4A and S4B ) ; on the other hand , the number of type A cells , corresponding to immature neurons derived from type C cells and identified by BrdU/DCX staining , was significantly lower than in control mice ( 40% decrease , p < 0 . 01; Figure S4C ) . Conversely , differentiated SVZ neurons up to 5 d old ( BrdU+/NeuN+ cells ) increased more than 2-fold in TgPC3 ON mice ( p < 0 . 01; Figure S4D ) , although the final number of new neurons generated in SVZ , as analyzed measuring BrdU+/NeuN+ cells 3 wk later ( at P116 ) in their final migratory destination , i . e . , the olfactory bulb , did not differ in TgPC3 ON and OFF mice ( unpublished data ) . This suggests that PC3 accelerated the differentiation of SVZ cells , as observed for the cells of the dentate gyrus . It should be emphasized , however , that SVZ cells are not involved in spatial memory processes , being anatomically and functionally independent from those of the dentate gyrus , since they migrate through the rostral migratory stream to the olfactory bulb where they play a specific role in the olfactory processes [49 , 52] . We then investigated whether early-differentiated dentate gyrus neurons in TgPC3 ON mice showed an altered morphology in the three different stages of the morphogenetic process underlying the maturation of granule neurons from progenitors in the SGZ of dentate gyrus . We labeled the newly generated granule neurons in adult hippocampus of TgPC3 mice by infecting the dentate gyrus region with a retrovirus produced by the vector CAG-GFP [19] . Highly concentrated retrovirus ( about 108 pfu/ml ) was delivered through sterotaxic surgery in the dentate gyrus of TgPC3 mice at P95 , two months after activation of the transgene , according to the time schedule followed for immunohistochemical analyses . CAG-GFP vector is replication incompetent; thus , only dividing cells at the moment of surgery could be infected . The soma of the majority of GFP+ neurons detected in control mice was localized on the hilar border of the granule cell layer of the dentate gyrus at the different time points analyzed , i . e . , throughout 7 −28 days post-infection ( dpi ) . The most evident developmental feature of GFP+ neurons , detected at different dpi in TgPC3 mice with either active or inactive transgene , was the progressive growth of dendrites , whose lengths , shorter than the width of the dentate gyrus blade at 7 dpi , increased greatly at 16 dpi and further at 28 dpi , reaching far outside of the dentate gyrus . During these last two time points , the dendritic growth was accompanied by an increase in the complexity of arborization ( Figure 2A ) . There were , however , clear differences in neuronal morphology between TgPC3 ON and TgPC3 OFF mice , which we evaluated by scoring dendritic length and branching points . Analysis of GFP+ neurons at 7 dpi revealed a significantly increased dendritic length in TgPC3 ON mice compared with TgPC3 OFF . This difference , however , was reversed in GFP+ neurons at 16 dpi and more clearly at 28 dpi , when the dendritic lengths in TgPC3 ON mice were significantly shorter than those in TgPC3 OFF mice ( Figure 2B ) . Similarly , the number of branching points in GFP+ neurons at 7 dpi was significantly greater in TgPC3 ON than in TgPC3 OFF mice , but again this difference was transient , because in GFP+ neurons at 16 and 28 dpi , the number of branching points became equivalent in the two groups ( Figure 2C ) . To further evaluate the dendritic complexity , we analyzed the density of spines . The dendritic spine is the locus of connection to excitatory synaptic input , mainly glutamatergic [53]; therefore , the process of spine formation has consequences in the functionality of neurons . Normally , spine growth starts in the adult-generated dentate gyrus neurons at about 16 dpi and reaches a plateau at 56 dpi , but at 28 dpi the exponential phase of growth has already ceased , and this stage can be considered representative of the attainment of the morphological maturity of the neuron [19] . Therefore , we focused our analyses at 28 dpi , and also at 70 dpi , when the plateau of growth is attained . Quantification of spine density in dentate gyrus neurons indicated a significant reduction both at 28 dpi and at 70 dpi in TgPC3 mice with active transgene , compared to mice with inactive transgene ( Figure 2D ) . However , at 28 dpi the decrease of spine density was substantial ( 60% ) , whereas at 70 dpi , it was mild ( 15% decrease; Figure 2D and 2E ) . We conclude that the dendritic length of new dentate gyrus neurons in TgPC3 ON mice , albeit greater than in control mice at 7 dpi , appeared significantly shorter at 28 dpi , associated with reduced spine density . This evidence points to a decrease in dendritic growth and in spine number during the developmental stages between 7 and 28 dpi; a partial recovery of normal values of spine density may slowly occur during the following stages . The absence of alteration in the branching points would indicate that no major morphological alterations occur , other than those likely consequent to a faster attainment of the end point of the maturation process . Spatial learning was tested in the Morris water maze [54 , 55] . In this task , which is mainly dependent on an intact hippocampus [54 , 56] , mice learn across daily sessions to find a hidden escape platform using extra-maze visual cues . A first session of experiments , carried out in 3-mo-old WT , TgPC3 OFF and TgPC3 ON mice ( whose transgene had been activated at P30; see time-schedule in Figure 3A ) , showed significant differences among groups in escape latencies both in learning ( trials 1–18; F ( 2 , 38 ) = 32 . 04; p < 0 . 0001 ) and reversal learning ( trials 19–30; F ( 2 , 38 ) = 34 . 15; p < 0 . 0001 ) . As indicated by post-hoc comparisons ( Duncan multiple range test ) , TgPC3 ON mice were dramatically impaired ( p < 0 . 05 ) both in learning and reversal learning compared to WT and TgPC3 OFF mice , whereas no significant differences were found between the performances of TgPC3 OFF and WT mice ( Figure 3B ) . Moreover , both in the first ( p < 0 . 001 ) and the second ( p < 0 . 01 ) probe trial , carried out 24 h after learning and reversal learning , respectively , TgPC3 ON mice spent a significantly smaller amount of time in the target quadrant , compared to both TgPC3 OFF and WT mice . No significant differences were found between TgPC3 OFF and WT mice ( Figure 3C ) . In this analysis , we considered WT mice , either doxy-treated or doxy-free , as a single control sample , because no significant differences were found between the two groups . An analysis of behavioral aspects not related to learning , i . e . , wall hugging behavior ( thigmotaxys ) and swimming speed , did not show significant differences between TgPC3 ON , TgPC3 OFF , and WT mice either during the first or the second behavioral session ( Figure S5A and S5B ) . However , increased levels of thigmotaxys were detectable at later stages of training in TgPC3 ON mice , which may reflect their poor learning . To further test the effect of the PC3 transgene activation on spatial memory , we submitted transgenic and control mice to a less stressful behavioral task compared to the water maze , namely the eight-arm radial maze [57 , 58] . In this task , mice have to search for a food pellet located at the end of each arm . Reentering a previously visited arm was considered as an error . Significant differences in the percentage of errors were found among groups ( F ( 2 , 38 ) = 6 . 71; p < 0 . 005 ) . As shown by post-hoc comparisons ( Duncan multiple range test ) , the performance of TgPC3 ON mice was severely impaired ( p < 0 . 05 ) in comparison to both TgPC3 OFF and WT mice , whereas no significant differences were found between these latter groups ( Figure 3D , left ) . Given that the above behavioral tests were conducted in TgPC3 ON mice in the presence of an active transgene , we wished to verify whether the observed behavioral phenotype could be related to indirect effects of PC3 over-expression in nestin-positive cells on neighboring , mature granule cells . Thus , we tested spatial learning in the Morris water maze using TgPC3 ON mice in which the transgene was activated at P30 and inactivated at P95 , i . e . , prior to behavioral tests . To this aim , doxycycline treatment was suspended at P90 , to allow 5 d of metabolization of the residual levels ( Figure S6A ) . The performance of TgPC3 ON mice was significantly impaired in learning and reversal learning , as well as in the first and in the second probe trial , compared to WT mice ( Figure S6B and S6C ) . Thus , we can exclude indirect cell-mediated effects by nestin-positive cells expressing the PC3 transgene during the behavioral tests . Altogether , these behavioral results indicate that , in PC3 transgenic mice , the accelerated differentiation of dentate gyrus adult progenitors dramatically impairs learning and memory of spatial information . To verify whether genetically altered neurogenesis could impair learning performance in previously experienced tasks , TgPC3 OFF mice and their controls were treated with doxycycline ( doxy; TgPC3 OFF → ON and WT-doxy , respectively ) following the first experimental phase . In addition , for this set of experiments , the gene was activated for a shorter period of time ( see time schedule in Figure 3A ) . During treatment , both TgPC3 OFF → ON and WT-doxy mice were submitted to a second session of spatial learning and memory tasks . A new piece of information was introduced in the Morris water maze by moving the hidden platform to a different location . By contrast , in the radial maze , all mice were tested with the same procedure used in the first experimental phase . This differential approach was used to test for the ability of the mice to use previously acquired information . In the Morris water maze , TgPC3 OFF → ON mice were impaired in learning the novel position of the platform in comparison to controls ( Figure 3B , trials 31–42; F ( 1 , 17 ) = 5 . 25; p < 0 . 05 ) . In the probe test , TgPC3 OFF → ON mice spent a significantly smaller amount of time in the target quadrant in comparison to WT-doxy mice ( Figure 3C , probe 3; p < 0 . 05 ) . Similarly , in the radial maze , TgPC3 OFF → ON mice committed a significantly greater number of errors compared to WT-doxy mice ( Figure 3D , right; F ( 1 , 17 ) = 7 . 79; p < 0 . 05 ) . These results indicate that the anticipated differentiation of dentate gyrus neural progenitors produces a remarkable impairment in tasks properly solved by transgenic mice before the PC3 transgene activation . Thus , the normal differentiation timing of dentate gyrus neural precursors emerges as a major constraint to learning performance , even in a previously experienced spatial setting , either in the presence or absence of new information . We then investigated the effects on memory of the PC3 transgene activation in a fear-related learning task , namely contextual and cued fear conditioning , involving mainly the hippocampus and amygdala , respectively [59 , 60] . In this task , immobility ( freezing ) , a natural reaction elicited in mice by aversive stimuli , was recorded and considered a measure of fear memory . Animals were preliminarily trained in the conditioning box ( chamber A ) , in which a conditioned stimulus ( CS ) and unconditioned stimulus ( US; see Materials and Methods ) were paired . During the training , no significant effect of the genotype was observed among groups in the level of freezing , both in the pre-CS ( F ( 2 , 34 ) = 1 . 47; p = not significant ) and the post-US ( F ( 2 , 34 ) = 0 . 91; p = not significant ) phases , and all the groups of mice reacted alike to the US ( F ( 1 , 34 ) = 115 . 52; p = 0 . 0001; Figure 4A ) . In the contextual test , analysis of the percentage of freezing showed significant differences between groups ( F ( 2 , 34 ) = 17 . 68; p < 0 . 0001 ) . As shown by post-hoc comparisons ( Duncan multiple range test ) , TgPC3 ON mice spent a significantly smaller amount of time ( p < 0 . 0001 ) in freezing behavior compared to both TgPC3 OFF and WT mice , whereas no significant differences were found between TgPC3 OFF and WT mice ( Figure 4B , left ) . Two hours after the contextual test , cued memory was assessed by the administration of the CS in a different setting ( chamber B ) . No significant differences between groups emerged ( F ( 2 , 34 ) = 0 . 15; p = not significant ) , whereas a significant effect of the CS was observed ( F ( 1 , 34 ) = 318 . 04; p < 0 . 0001 ) , that is to say that , in the novel environment , all groups of mice exhibited an appreciable level of freezing only during the CS administration ( Figure 4C , left ) . In the following experimental phase , TgPC3 OFF → ON and WT-doxy mice were submitted to a second session of fear conditioning , with an unvaried procedure ( i . e . , training in the same box used in the first experimental phase , followed by contextual and cued tests; see also Materials and Methods ) . Transgene activation significantly impaired the contextual memory ( Figure 4B , right; F ( 1 , 17 ) = 6 . 53; p < 0 . 05 ) , whereas no significant differences between transgenic and control mice were observed in the cued conditioning test ( Figure 4C , right; F ( 1 , 17 ) = 2 . 72; p = not significant ) . As in spatial tasks , these results further indicate that , also in a fear-related task , genetically altered neurogenesis produces a memory impairment for the contextual features mice had already experienced . Both in the first and the second behavioral session , the deficit was selective for memory contents processed mainly by the hippocampus ( i . e . , those related to spatial information ) , sparing the components of the experience whose processing appears to be peculiar to the amygdala . Long-term changes in synaptic strength have been described in several brain areas . In particular , hippocampal long-term potentiation ( LTP ) of glutamatergic synaptic transmission is believed to be the synaptic correlate of several forms of learning and memory ( for reviews , see [61 , 62] ) . To test whether the above-described deficits in memory tasks caused by early expression of the PC3 gene correlated with a change in long-term synaptic transmission , we recorded field excitatory postsynaptic potentials ( fEPSPs ) in the outer molecular layer of the dentate gyrus while stimulating perforant path afferents in WT , TgPC3 OFF , and TgPC3 ON mice . Robust LTP of excitatory synaptic transmission could be evoked by high-frequency stimulations ( HFS: four trains of 100 stimuli at 100 Hz ) in WT animals . Overall , fEPSP slopes were 1 . 5 ± 0 . 04 larger when measured 50 min after LTP induction and compared with baseline levels ( n = 13 slices , 7 mice; p < 0 . 001; Figure 5A ) . Similar levels of synaptic potentiation were observed in TgPC3 OFF animals ( normalized fEPSP slope at 50 min after HFS = 1 . 62 ± 0 . 12 , n = 9 slices , 7 mice , p > 0 . 2 when compared with WT , Figure 5A ) . In TgPC3 ON mice , HFS evoked LTP consistently ( normalized fEPSP slope was 1 . 2 ± 0 . 04 , n = 12 slices , 7 mice , p < 0 . 001 ) , but we found that the level of potentiation was significantly smaller when compared to WT and TgPC3 OFF mice ( p < 0 . 001; Figure 5A ) . This effect on LTP was not due to overall changes in synaptic transmission , as input-output curves ( fEPSP slopes versus increasing afferent fiber volley amplitudes ) were similar in WT , TgPC3 OFF , and TgPC3 ON mice ( Figure 5C ) . Hippocampal adult neurogenesis is selectively localized in the dentate gyrus . We thus examined whether LTP deficits recorded in the TgPC3 ON mice were selective for dentate gyrus synapses . We then recorded fEPSPs in the CA1 area of the hippocampus while stimulating the Schaffer collateral fibers in stratum radiatum . A single train of HFS induced robust LTP in WT ( n = 7 slices , 2 mice ) , TgPC3 OFF ( n = 8 slices , 2 mice ) , and TgPC3 ON mice ( n = 5 slices , 2 mice ) ( Figure 5B ) . No significant differences were observed between all three conditions tested ( p > 0 . 3 in all cases ) . As a whole , these results indicate that the accelerated differentiation by early PC3 expression during adult neurogenesis affects synaptic plasticity selectively in the dentate gyrus , without altering CA3–CA1 hippocampal synapses . The decrease of LTP evoked in the dentate gyrus of mice with an activated transgene prompted us to ask whether this decrease was correlated with the function of new neurons in memory circuits . It has been shown that newly generated neurons of the dentate gyrus are progressively integrated into spatial memory-related circuits after 4–6 wk of age . Such evidence was obtained by measuring c-fos expression , whose activation specifically occurs in dentate gyrus neurons of mice that have undergone spatial memory training and is thus correlated with the recruitment of new neurons into spatial memory circuits [21] . Thus , we wished to define whether the anticipated differentiation of new granule neurons of dentate gyrus had an effect on the time at which they became active in memory networks and on the extent of their activation . To this end , we measured the number of new neurons at 2 , 4 , and 6 wk of age that become activated by a spatial memory test . Mice , either control ( TgPC3 OFF ) or with the PC3 transgene activated 60 d before ( TgPC3 ON ) , were treated with five daily injections of BrdU and then trained in different groups in the Morris water maze test 2 , 4 , and 6 wk later ( Figure 6A ) . Mice were killed 1 . 5 h following the tests , and the number of new neurons , identified by positivity to BrdU and NeuN , was compared to the number of new neurons integrated into memory circuits , identified by c-fos , BrdU , and NeuN concomitant expression ( Figure 6B ) . In TgPC3 OFF mice , the fraction of new neurons expressing c-fos was null 2 wk after birth ( i . e . , after BrdU injections ) but reached about 2% after 4 wk and increased to about 5% after 6 wk ( Figure 6C ) . These data are in agreement with those obtained by Frankland's group , and they indicate a progressive functional activation of new neurons within spatial circuits , correlated to their age and maturation [21] . In contrast , in mice with the PC3 transgene activated , virtually no 2- , 4- , or 6-wk-old new neurons expressing c-fos were detectable , clearly indicating that new neurons did not undergo activation by spatial training ( Figure 6C ) . The lack of induction of c-fos expression by spatial training in 4- and 6-wk-old neurons of TgPC3 ON mice corresponded to a significant learning deficit of these mice in the Morris water maze task ( Figure S7A and S7B ) . Moreover , in the dentate gyrus , the ratio between the total number of new neurons generated ( i . e . , BrdU+/NeuN+ cells ) and the total number of neurons ( NeuN+ cells ) did not significantly differ in the same TgPC3 ON and TgPC3 OFF mice groups analyzed 2 , 4 , and 6 wk after BrdU injection ( Figure 6D ) . Also the absolute numbers of BrdU+/NeuN+ cells in the dentate gyrus of the 2- , 4- , and 6-wk groups were equivalent in the TgPC3 OFF and TgPC3 ON mice ( unpublished data ) . These results are consistent with data shown above ( Figure 1K ) and conclusively indicate that the lack of activated new neurons observed in TgPC3 ON mice ( Figure 6C ) is a genuine effect , not caused by impaired generation of new neurons . It is notable that c-fos was expressed in the neuronal population of the dentate gyrus to a similar extent in all groups of control mice , as measured by the ratio between total number of activated neurons ( c-fos+/NeuN+ cells ) and total number of neurons ( NeuN+ cells ) , indicating the existence of a basal level of activation in the neuronal population . In contrast , in TgPC3 ON mice , the ratio between activated and total number of neurons was progressively reduced in the 2- , 4- , and 6-wk groups , with a significant difference in the 4- and 6-wk groups ( Figure 6E ) . One possibility suggested by this finding is that the failure to generate new activated neurons in dentate gyrus in response to spatial behavioral training , observed above ( Figure 6C ) , leads to a progressive reduction in the total number of activated neurons .
The transgenic conditional model used in this study offers the possibility of analyzing the hippocampus-dependent learning and memory processes after a selective enhancement of the maturation rate of new adult-generated hippocampal neurons . In contrast , previous studies eliminated new progenitor cells and neurons from the adult hippocampus , using either antimitotic toxins , x-ray irradiation or virus-activated pro-drugs [24 , 26 , 28] . Our analysis of the dentate gyrus showed that activation of the PC3 transgene driven by nestin promoter led to a great increase in fully differentiated , 1–5-d-old , adult-generated hippocampal granule neurons ( stage 6; positive for NeuN and BrdU ) , accompanied by a parallel decrease in stem cells and putative transiently amplifying progenitor cells ( type-1 and −2 ) of the same age . These results clearly indicate an accelerated transition toward terminal differentiation of the newborn progenitor cells expressing the PC3 transgene . Moreover , the analysis of fully differentiated hippocampal granule neurons of about 4 wk of age showed that the final number of neurons produced is the same in both TgPC3 ON and control mice . As a whole , these findings indicate that the rate of maturation of new neurons is accelerated by PC3 , without any effect on the rate of neurogenesis—i . e . , the birth rate of new neurons—and thus on the total number of new neurons generated . Such PC3-dependent anticipated differentiation of progenitor cells is in line with the previously observed , intrinsic differentiative properties of PC3 on neural precursors [41] and might result from the prevalence , in progenitor cells type-1 and type-2 , of the asymmetric neurogenic type of division , with which the expression of PC3 is associated [37–39 , 63] . The enhanced rate of maturation of the whole population of progenitor cells of the adult dentate gyrus of TgPC3 ON mice was clearly associated with a severe impairment in hippocampus-dependent learning and memory , as indicated by the performance of transgenic mice in different hippocampus-related behavioral tasks . In fact , learning and memory deficits were observed both in the Morris water maze and in the radial maze test . Furthermore , a significant memory deficit was also observed in the contextual fear conditioning test , in which both the hippocampus and the amygdala mediate the association between context and the aversive stimulus [59] . Conversely , no significant effect was observed in the cued version of the task , mainly characterized by a greater involvement of the amygdala formation ( reviewed in [64–66] ) . Such selective impairment of hippocampal spatial learning raises questions about the role played by functional and morphological modifications occurring in newborn neurons of our mouse model . A relevant functional change observed in mice with maturation of progenitor cells enhanced by PC3 is the decrease of LTP evoked in the dentate gyrus , an effect that appears to be specific , because no alteration of synaptic plasticity in the CA1 region is seen . It is widely believed that LTP and depression of synaptic transmission underlie several forms of learning and memory [61 , 62] , and are often accompanied by structural changes of dendritic spines ( reviewed in [67–69] ) . The earliest form of excitability is shown by nestin+ non-radial precursor cells ( type-2 cells; [18] ) , and LTP begins to be observed in adult-generated , young ( 1–3-wk-old ) hippocampal neurons , where it is elicited more easily than in mature , post-mitotic dentate gyrus neurons [13 , 16] . Thus , the reduced LTP that we observe should not depend on the reduced number of progenitor cells type-2 and type-3 present in mice with activated transgene , because they are too immature to generate LTP . Rather , the reduction of LTP may depend on new neurons that are ≥2 wks old , whose numbers are unchanged in our mouse model but whose dendritic arborization and spine density are markedly decreased . Thus , bypassing the early stages of differentiation during adult neurogenesis in neurons generated following transgene activation strongly affects the ability of dentate gyrus circuits to integrate synaptic transmission . This is further suggested by the observation that basic synaptic transmission is unchanged , as input-output curve analysis shows . A second functional change that we observe in the dentate gyrus of transgenic mice is the lack of activation of new , fully differentiated neurons , 4–6 wk of age , in response to learning , as indicated by the absence of induction of c-fos expression after training . Indeed , the activation of adult-generated dentate gyrus neurons in spatial memory circuits has been linked to the induction of c-fos expression [21 , 70] . This expression is elicited in adult-generated dentate gyrus neurons , 4–6 wk of age , in mice trained in the Morris water maze , indicating that at this developmental stage the dentate gyrus neuron develops functions critical for its activation and recruitment into spatial memory circuits [21] . Hence , the absence of c-fos activation in 4–6-wk-old neurons that have expressed PC3 at birth suggests that these neurons do not attain a functional state in hippocampal circuits . The functional alterations observed by us in new , adult-generated dentate gyrus neurons that are prematurely differentiated may find a structural correlate in the morphological alterations , most evident in 4-wk-old neurons identified after infection with a GFP-expressing retrovirus , i . e . , reduced dendritic length and spine density . We can assume that the birth datings of a neuron by BrdU and retroviral infection are comparable , and we consider that a neuron analyzed 4 wk post-infection is representative of the level of mid-late maturation , when the main neuronal structures are developed [19] . Altogether , it is plausible that the faster attainment of the terminally differentiated state , observed in adult-generated dentate gyrus neurons of mice with active transgene , may ( following an initial faster development of the dendritic tree seen in neurons at 7 dpi ) lead to a premature end of the developmental stages relative to the establishment and growth of the dendritic tree during the second and third week after birth . Only at later stages , i . e . , in 10-wk-old neurons , emerges a tendency to recover , at least in part , normal values of spine density . Neuronal structure and functional activity are correlated [71–75] . Indeed , the reduced dendritic structure affects spine formation ( the latter being spatially constrained by dendrites ) and ultimately the afferent synaptic input , since each spine receives one synaptic bouton [76] . As a result , the neuron has reduced capability to activate and integrate memory patterns , as our electrophysiological and behavioral data indicate . In conclusion , the selective anticipation of differentiation of new , adult-generated dentate gyrus neurons induced in the PC3 transgenic mouse model reveals the critical role of developmental timing , even before terminal mitosis , for the generation of neurons that are fully able to integrate new spatial memories . As our data indicate , the number of new neurons necessary to integrate new spatial information appears relatively small . In fact , the number of new dentate gyrus neurons generated during the shortest period of activation of the transgene , sufficient to prevent new spatial learning ( about 20 , 000 , i . e . , 800–1 , 000 per day multiplied by 22 days elapsed before behavioral session 2 ) , should not exceed 5%–6% of the total number of neurons in the dentate gyrus ( less than one half million ) . It is worth noting that the progenitor cells born before or during the first session of spatial training in TgPC3 OFF mice will normally differentiate without being affected by activation of the transgene during session 2 , because in progenitor cells , the nestin promoter becomes physiologically inactive within 1 wk after their birth [7] . Thus , in TgPC3 OFF → ON mice , only the new neurons born in the 3–4-wk period of transgene activation preceding and during the second behavioral session appear to be responsible for the spatial learning and memory deficits observed . Moreover , given that TgPC3 OFF → ON mice were significantly impaired in spatial tasks since the beginning of the second session , the premature differentiation of adult-generated hippocampal progenitor cells could prevent not only learning new spatial tasks but also the use of information acquired previously in normal condition , i . e . , during session 1 before activation of the transgene . Therefore , the picture emerging from our data is that the correct differentiation and integration into existing memory circuits of a relatively small population of new adult-generated neurons not more than 4 wk old are crucial not only for spatial learning but also , notably , for the use of memories consolidated in tasks previously performed . Conversely , neurons older than 4 wk appear less efficient . This unique role of younger adult-generated neurons in the maintenance of key memory functions may derive from their proposed ability to link temporally proximal events [23] , and also entails the need for continuous renewal of new neurons as soon as they terminally differentiate . The late activation ( i . e . , c-fos expression ) observed after training in 4–6-wk-old neurons by us and by the group of Frankland [21] , might thus be related to subsequent steps of integration into spatial learning circuits . The significant impairment of the hippocampal function observed in our model , i . e . , the deficit of spatial learning in the Morris water and radial maze tests accompanied by deficit of dentate gyrus LTP , is not frequently observed in studies of spatial memory ( e . g . , see [77] ) . It is plausible that the ablation of new neurons , while eliminating their functional contribution , may at least in some case trigger compensatory processes , e . g . , increased proliferation of the surviving progenitors , or a plastic reassembly of the cytoarchitecture of circuits . In our model , the new neurons exist , albeit with a reduced functionality; thus , as our data strongly suggest , no compensatory processes are activated . Moreover , these altered new neurons appear to establish connections with the existing neurons and are thus integrated in previously-formed circuits . It might be the same as expanding a circuit by interspersing new elements with lower functionality . Therefore this model , assembling within spatial circuits new neurons generated in normal number but with altered maturation , can be considered the equivalent of an anatomical dominant-negative , and may give the advantage over ablative strategies of magnifying the ability of new neurons—variable depending on their maturation state—to integrate and/or recall traces of temporally proximal events from existing circuits .
The bitransgenic nestin-rtTA/TRE-PC3 mouse line is the progeny of two mouse lines , each carrying a transgene: nestin-rtTA transgene , encoding the tetracycline-regulated TransActivator driven by the rat nestin promoter , and TRE-PC3 transgene , carrying the PC3 coding region under control of the Tetracycline Responsive Elements . The nestin-rtTA and TRE-PC3 transgenic mouse lines were generated previously [78 , 41] , exploiting the tet-on system to control the activation of the PC3 transgene [79] , and they were maintained in heterozygosity in FVB background or in homozygosity in BDF1 ( C57BL/6 x DBA/2 ) background , respectively . The bitransgenic nestin-rtTA/TRE-PC3 mice used for experiments were isogenic , having been previously interbred for six or more generations . In bitransgenic mice , the TransActivator protein produced by the nestin-rtTA transgene binds and activates TRE-PC3 in the presence of doxycycline , consequently inducing the expression of PC3 transgene . Genotypization of bitransgenic nestin-rtTA/TRE-PC3 mice was performed as described [41] . Animals were housed in standard breeding cages under a 12-h light-dark schedule at a constant temperature of 21 °C and underwent behavioral testing during the second half of the light period ( between 2:00 and 5:00 p . m . ) in sound-insulated rooms . All procedures involving mice were completed in accordance with the Istituto Superiore di Sanità ( Italian Ministry of Health ) and current European ( directive 86/609/ECC ) Ethical Committee guidelines . Bitransgenic nestin-rtTA/TRE-PC3 mice ( named TgPC3 throughout this report ) were treated for immunohistochemistry according to the following experimental protocol . The PC3 transgene was activated in TgPC3 mice P30 ( termed TgPC3 ON mice ) by doxycycline hydrochloride ( 2 mg/ml; MP Biomedicals ) supplied to mice in drinking water containing 2 . 5% sucrose; at P90–94 mice received five daily bromodeoxyuridine ( BrdU; 95 mg/kg i . p . ) injections to detect dividing neurons . Immunohistochemical analyses were performed after BrdU treatment , at P95 and at P116 . TgPC3 mice untreated with doxycycline ( termed TgPC3 OFF mice ) and WT mice ( i . e . , the progeny of crosses between nestin-rtTA and TRE-PC3 transgenic mice , in which the TRE-PC3 transgene was not inherited ) were used as controls . Brains were collected after transcardiac perfusion with 4% paraformaldehyde ( PFA ) in PBS–DEPC and kept overnight in PFA . Thereafter , brains were equilibrated in sucrose 30% and cryopreserved at −80 °C . Immunohistochemistry was performed on serial sections cut transversely at 20-μm thickness at −25 °C in a cryostat from brains embedded in Tissue-Tek OCT ( Sakura ) . Sections were then processed immunohistochemically for multiple labeling with BrdU and other cellular markers using fluorescent methods . BrdU incorporation was visualized by denaturing DNA through pre-treatment of sections with 2 N HCl 45 min at 37 °C followed by 0 . 1 M sodium borate buffer pH 8 . 5 for 10 min . Sections were then incubated with a rat monoclonal antibody against BrdU ( Serotech; MCA2060; 1:150 ) together with other primary antibodies , as indicated: either mouse monoclonals raised against nestin ( Chemicon International; MAB353; 1:150 ) and NeuN ( Chemicon International; MAB377; 1:100 ) , or rabbit polyclonal antibodies against Glial fibrillary acidic protein ( GFAP; Promega Corporation; G560A; 1:150 ) and c-fos ( Chemicon International; Ab-5 PC38T; 1:500 ) , or goat polyclonal antibodies recognizing Doublecortin ( DCX ) ( Santa Cruz Biotechnology; SC-8066; 1:200 ) or NeuroD1 ( R&D Systems; AF2746; 1:100 ) . Another primary antibody used was a rabbit monoclonal antibody against Ki67 ( LabVision Corporation; SP6; 1:100 ) . These antigens were visualized with either TRITC ( tetramethylrhodamine isothiocyanate ) -conjugated donkey anti-rat ( Jackson ImmunoResearch; BrdU ) , or TRITC-conjugated goat anti-rabbit ( Sigma; Ki67 ) , or Cy2-conjugated donkey anti-rabbit ( Jackson ImmunoResearch; GFAP ) , or donkey anti-goat Cy2-conjugated ( Jackson ImmunoResearch; DCX , NeuroD1 ) or also with donkey anti-mouse Alexa 647 ( Invitrogen; nestin , NeuN , c-fos ) secondary antibodies . Terminal deoxynucleotidyl transferase-mediated biotinylated UTP nick end labeling ( TUNEL ) [80] was performed on cryostat sections using the in situ cell death detection kit ( Roche Products ) , according to the instructions of the manufacturer . Apoptotic nuclei were visualized with 0 . 5% 3 , 3'-diaminobenzidine ( DAB ) . Images of the immunostained sections were obtained by laser scanning confocal microscopy using a TCS SP5 microscope ( Leica Microsystem ) . All analyses were performed in sequential scanning mode to rule out cross-bleeding between channels . About 200 transverse sections spaced 20 μm apart and comprising the entire hippocampus were obtained from each brain; about one-in-ten series of sections ( 200 μm apart ) were analyzed to count cells expressing the indicated marker throughout the whole rostro-caudal extent of the dentate gyrus . The total estimated number of cells within the dentate gyrus , positive for each of the indicated markers or combination of markers , was obtained by multiplying the average number of positive cells per section by the total number of 20-μm sections comprising the entire dentate gyrus [81 , 2 , 21] . At least three animals per group were analyzed . c-fos was analyzed in about one-in-ten series of 40-μm free-floating sections ( 400 μm apart ) . Stereological analysis of volumes and of the absolute number of granule cells in the dentate gyrus was performed , analyzing every sixth section in a series of 40- μm coronal sections ( thus spaced 240 μm ) . Total cell number was obtained according to the optical disector principle , by systematic sampling of unbiased counting frames of 15-μm side in each section . Nuclei considered in the count ( identified by Hoechst 33258 staining ) were those appearing throughout the different focal planes of each section , excluding those nuclei that intersected the exclusion boundaries of the counting frame , as defined by the optical disector principle [82] . To obtain the absolute cell number , the average cell number per disector volume ( Nv; the disector volume being 15 × 15 × 40 μm3 ) was multiplied by the reference volume ( i . e . , the total volume of dentate gyrus; [82] ) . The reference volume was determined by summing the traced areas of dentate gyrus ( or hippocampus ) and multiplying this result by the distance between the sections analyzed ( 240 μm ) . Measurements of positive cells and areas were obtained by computer-assisted analysis using the I . A . S . software ( Delta Systems ) . To reveal the β-galactosidase activity of the β-geo reporter gene fused to the nestin-rtTA transgene [78] , transverse sections from brains fixed as described above were post-fixed with 0 . 2% glutaraldehyde in PBS for 10 min , then washed three times for 5 min in lacZ wash buffer ( 1 M MgCl2 , 1% sodium deoxycholate , 2% NP40 in PBS ) . Sections were then stained overnight at 30 °C with lacZ stain [lacZ wash buffer , 0 . 21% K4Fe ( CN ) 6 . 3H2O , 0 . 16% K3Fe ( CN ) 6 , 25 mg/ml of 5-Bromo-4-Chloro-3-indolyl- β-D-galactopyranoside ( X-Gal ) ] , washed three times 5 min with PBS and mounted . The murine Moloney leukemia virus-based retroviral vector CAG-GFP [19] was used to infect only dividing cells at the moment of in vivo delivery . Retroviruses were propagated by transiently cotransfecting CAG-GFP with pHCMV-G vector ( which expresses the VSV-G protein; [83] ) in the packaging cell line Phoenix ( human embryonic kidney cell line stably expressing the gag and pol proteins of Moloney murine leukemia virus; [84] ) . Cells at about 90% confluence in 90-mm dishes were transfected with 11 . 5 μg of CAG-GFP and with 13 . 5 μg of pHCMV-G , using calcium phosphate precipitation . Virus-containing supernatant was harvested 36 , 48 , and 60 h after the start of transfection . Frozen stocks were pooled and the virus was concentrated by centrifugation for 1 . 5 h in a Hitachi RPS40T rotor at 25 , 000 rpm . The concentrated virus solution ( 108 pfu/ml ) was infused ( 1 . 5 μl at 0 . 32 ml/min ) by stereotaxic surgery into the right and left dentate gyrus of anesthetized P95 transgenic mice ( anteroposterior = −2 mm from bregma; lateral = ±1 . 5 mm; ventral = 2 mm ) . The animal protocols were approved by the Istituto Superiore Sanità , Rome , Italy . Dendritic analysis of GFP-positive neurons was performed by acquiring z-series of 15–25 optical sections at 1–1 . 5 μm of interval with a 40X oil lens , with the confocal system TCS SP5 ( Leica Microsystem ) . Two-dimensional projections at maximum intensity of each z-series were generated with the LAS AF software platform ( Leica Microsystem ) in the TIFF format , and files were imported in the I . A . S . software ( Delta Systems ) to measure dendritic length . The number of branching points was counted manually in the same images . For each data point , 20–30 cells from two mice were analyzed ( either control or with activated transgene ) . From the same GFP-positive neurons , the spines present on dendritic processes were imaged by acquiring z-series of 25–35 optical sections at 0 . 5 μm of interval with a 63X apochromatic oil lens , and a digital zoom of 3 . The number of spines was counted manually on two-dimensional projections obtained by the LAS AF software . The linear spine density was then calculated by dividing the total number of spines by the length of the corresponding dendritic process . WT , TgPC3 OFF , and TgPC3 ON mice , 95 d old , were used for electrophysiological recordings . TgPC3 ON mice were used after animals were exposed for two months to doxycycline hydrochloride . Mice were deeply anesthetized with isofluorane inhalation and decapitated , and the brains were removed and immersed in cold “cutting” solution ( 4 °C ) containing ( in mM ) : 234 sucrose , 11 glucose , 24 NaHCO3 , 2 . 5 KCl , 1 . 25 NaH2PO4 , 10 MgSO4 , and 0 . 5 CaCl2 , gassed with 95% O2/5% CO2 . Coronal slices ( 300 μm ) were cut with a vibratome from a block of brain containing the dorsal hippocampus and then incubated in oxygenated artificial cerebrospinal fluid ( aCSF ) containing ( in mM ) : 126 NaCl , 26 NaHCO3 , 2 . 5 KCl , 1 . 25 NaH2PO4 , 2 MgSO4 , 2 CaCl2 , and 10 glucose , gassed with 95% O2/5% CO2; pH 7 . 4 , initially at 35 °C for 1 h , and subsequently at room temperature . Slices were then transferred to a submersed recording chamber and maintained at 32 ± 1 °C while being continuously perfused by fresh and oxygenated aCSF at a rate of ∼2–3 ml/min . Recordings were performed in the continuous presence of 100 μM picrotoxin to block inhibitory GABAergic transmission . Monopolar stimulating and recording electrodes consisted of glass pipettes ( 0 . 5–1 Mμ ) filled with aCSF and placed in the middle of the outer molecular layer of the dentate gyrus or in the middle of the stratum radiatum . Field excitatory post-synaptic potentials ( fEPSPs ) were amplified using a Multiclamp 700B patch-clamp amplifier ( Molecular Devices ) . A Digidata 1320 digitizer and PClamp9 ( Molecular Devices ) were used for data acquisition , analysis , and generation of stimuli . Input-output ( I/O ) curves were generated before inducing LTP . For I/O curves , stimulus intensities were adjusted to have afferent volleys of 50 , 100 , 150 , 200 and 250 μV in all tested slices , and the resulting fEPSP slopes were calculated and averaged across 3–5 sweeps . For LTP experiments , fEPSPs were stimulated at 0 . 1 Hz . If a stable baseline of at least 10 min was achieved , LTP was induced by four trains of 100 stimuli at 100 Hz repeated every 20 s for recordings in the dentate gyrus and one train of 100 stimuli at 100 Hz in the CA1 area . Results are presented as means ± SEM . Data were averaged in 0 . 5-min bins . All mice were tested in an open field and a plus maze [85 , 86] to assess locomotor abilities and anxiety levels . No statistically significant differences were found among the groups in both tasks ( unpublished data ) . The behavioral experiments were carried out in two phases , according to the schedule shown in Figure 3A . In the first phase , the following groups of mice , aged 3 mo , were used: TgPC3 ON ( n = 10 ) , in which the PC3 transgene was activated at P30 by doxycycline administration; TgPC3 OFF ( n = 8 ) , in which doxycycline was not administered so that the PC3 transgene was not activated; and WT ( n = 12 treated with doxycycline; n = 11 untreated ) . At the end of the first phase , TgPC3 OFF ( n = 8 ) and untreated control mice ( WT , n = 11 ) were administered with doxycycline and , after day 22 of treatment , these two mice groups ( labeled as TgPC3 OFF → ON and WT-doxy , respectively ) were subjected to a second behavioral testing session . The Morris water maze [54 , 55] was carried out in a circular swimming pool of 1 . 3 m in diameter , filled with opaque water at a temperature of 25 ± 1 °C and located in a room containing prominent extra-maze cues . A hidden , 15-cm diameter platform was used . In the first experimental phase , the training consisted of 18 trials ( six trials per day , lasting a maximum of 60 s , with an intertrial interval of 30 min ) , with the platform left in the same position . After 3 d of learning , the platform was moved to the opposite position and reversal learning was monitored for two additional days . Probe tests ( 60 s ) were carried out 24 h after both learning and reversal learning by removing the platform from the pool . In the second phase , TgPC3 OFF → ON and WT-doxy mice were submitted to a further session of spatial learning lasting 2 d , during which the platform was located in a different position from those used in the previous experimental phase . A probe test was carried out 24 h after learning . Behavior was evaluated by EthoVision software ( Noldus Information Technology ) . For the eight-arm radial maze [57 , 58] , mice were singly housed , with water provided ad libitum , and gradually reduced to 85% of their free-feeding body weight . Throughout the experiment , mice were maintained at their reduced weight by being fed with a premeasured amount of food on each day . The maze apparatus was constructed of gray plastic maze , with eight identical arms radiating 37 cm from an octagonal starting platform ( side 7 cm ) . On each training trial , a 20-mg food pellet was placed at the end of each arm ( baits were not replaced ) and the animal was placed on the central platform , facing a randomly selected direction . In both the first and second experimental phases , all groups of mice were submitted to one trial per day for 10 training days; each daily trial ended when eight choices were made or 15 min had elapsed . An arm choice was defined as placement of all paws on a maze arm . An error was noted when an animal entered a previously visited arm . For the contextual and cued fear conditioning [87] , the experiments were carried out in two different chambers . In both the first and second experimental phases , all mice were trained in conditioning chamber A ( 26 × 22 × 18 cm ) , made of transparent Plexiglas with a grid metal floor and located in a sound-insulated box lighted by a white tensor lamp ( 60 W ) . After an acclimatizing period lasting 120 s , a 30-s tone was administered ( CS; 3 kHz , 80 dB ) . During the last 2 s of tone presentation , a foot-shock was delivered ( US; 0 . 5 mA ) . Both CS and US ended simultaneously . Mice were left in the conditioning chamber for a further period of 30 s and then returned to their home cage . For the contextual conditioning test , 24 h after training mice were placed in the same chamber for 5 min . Two hours after the contextual test , mice were tested for cued conditioning in chamber B , made of black Plexiglas , with a floor of triangular shape , lighted by a blue tensor lamp ( 60 W ) and perfumed by a vanilla essence diffuser . The test lasted 6 min , with CS administered during the last 3 min . One-way ANOVA was used to analyze the c-fos-expressing neurons , the dendritic length , branching points , and spine density , as well as the levels of freezing in the contextual and cued fear conditioning and the electrophysiological data originating from slices from the different animal groups . Morris water maze and radial maze results were analyzed by two-way ANOVA . Individual between-group comparisons , where appropriate , were carried out by Fisher's PLSD post-hoc or Duncan multiple range test . Student's t-test was used to analyze the number of neurons and hippocampal volumes as well as data obtained in the same slices after LTP-inducing stimuli . | Previous studies have implicated adult-born hippocampal neurons in the formation of spatial and contextual memories by using mouse models where newly generated neurons are either eliminated or increased in number . Nonetheless , how new neurons are integrated in the existing circuits and contribute to memory formation still awaits clarification . Toward this end , we have developed a different approach , using a mouse model that accelerates the differentiation of the newly generated neurons without altering their number , and offers the possibility to induce the process at any chosen moment . We show that the new neurons pass through their early stages of maturation faster and , though establishing connections with the existing neuronal circuits , fail to function properly . In fact , mice are not only unable to learn new spatial information , but they are also unable to use previously acquired memories . These results demonstrate that the appropriate timing of maturation of new neurons is important for their adult performance in memory circuits , i . e . , to integrate new memory traces and recall previous events . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience"
] | 2008 | The Timing of Differentiation of Adult Hippocampal Neurons Is Crucial for Spatial Memory |
The Caenorhabditis elegans DAF-16 transcription factor is critical for diverse biological processes , particularly longevity and stress resistance . Disruption of the DAF-2 signaling cascade promotes DAF-16 activation , and confers resistance to killing by pathogenic bacteria , such as Pseudomonas aeruginosa , Staphylococcus aureus , and Enterococcus faecalis . However , daf-16 mutants exhibit similar sensitivity to these bacteria as wild-type animals , suggesting that DAF-16 is not normally activated by these bacterial pathogens . In this report , we demonstrate that DAF-16 can be directly activated by fungal infection and wounding in wild-type animals , which is independent of the DAF-2 pathway . Fungal infection and wounding initiate the Gαq signaling cascade , leading to Ca2+ release . Ca2+ mediates the activation of BLI-3 , a dual-oxidase , resulting in the production of reactive oxygen species ( ROS ) . ROS then activate DAF-16 through a Ste20-like kinase-1/CST-1 . Our results indicate that DAF-16 in the epidermis is required for survival after fungal infection and wounding . Thus , the EGL-30-Ca2+-BLI-3-CST-1-DAF-16 signaling represents a previously unknown pathway to regulate epidermal damage response .
All organisms are in constant contacts with a variety of microorganisms . The innate immune system in hosts provides the first line of defense against these microorganisms . During the last decade , studies using Caenorhabditis elegans as a model host have revealed the involvement of evolutionarily conserved signaling pathways in the innate immune response to microbial infection and injury , including the DAF-2/DAF-16 insulin-like signaling pathway [1] , [2] . C . elegans DAF-2 is orthologous to the mammalian insulin/insulin-like growth factor-1 receptor [3] and daf-2 mutants exhibit increased resistance to pathogenic bacteria , such as Pseudomonas aeruginosa and Staphylococcus aureus [4] . Under standard growth conditions , DAF-2 initiates a kinase cascade that leads to the phosphorylation and cytoplasmic retention of its downstream effector DAF-16 , the ortholog of mammalian Forkhead box O ( FOXO ) transcription factors 5 , 6 , 7 . A reduction in DAF-2 signaling leads to the dephosphorylation of DAF-16 , allowing its nuclear translocation and transcriptional activation [5] , [6] . The pathogen-resistant phenotype of daf-2 mutants is suppressed by mutations in daf-16 , suggesting a crucial role for DAF-16 in innate immunity against bacteria [4] . As a transcriptional factor , activated DAF-16 mediates a variety of genes that are positive regulators of innate immunity against pathogenic bacteria [8] , [9] . In Drosophila and human tissues , FOXOs also induce the expression of a variety of antimicrobial peptides , such as drosomycin and defensins [10] , suggesting that the role for FOXOs as innate immunity regulators is highly conserved across species . Although DAF-16 is involved in immune responses to pathogenic bacteria including P . aeruginosa , S . aureus , Enterococcus faecalis and Salmonella enterica , daf-16 mutants are not significantly more susceptible than wild-type worms to the killing mediated by these bacteria [4] , [11] . Interestingly , a previous study shows that although the knock-down of daf-16 by RNAi in wild-type worms does not affect susceptibility to P . aeruginosa PA14 , intestinal-specific knock-down of daf-16 leads to enhanced susceptibility to PA14 [7] . These results suggest that DAF-16 in the intestine , but not in the whole worms , is required for resistance to PA14 infection . One reasonable explanation is that loss of DAF-16 in the intestine , in combination with loss of DAF-16 in other tissues , has an overall neutral effect on resistance to PA14 infection . Meanwhile , two recent studies demonstrate that two bacterial pathogens enteropathogenic Escherichia coli ( EPEC ) and Bacillus thuringiensis induce DAF-16 nuclear translocation , respectively [12] , [13] . These results contradict the previous notion that DAF-16 is activated by something other than pathogens [11] , [14] . More importantly , daf-16 mutants are more sensitive to the two bacterial pathogens [12] , [15] , [16] . However , the mechanism underlying DAF-16 activation by these bacterial pathogens remains unclear . Pathogenic bacteria , including P . aeruginosa , S . aureus , E . faecalis , S . enterica , and human pathogenic yeast Candida albicans infect the nematode intestine [2] , [17] , whereas natural nematophagous fungi , such as Drechmeria coniospora and Clonostachys rosea , infect the epidermis of nematode , leading to epidermal cell damage [18] , [19] , [20] , [21] , [22] . When comparing gene expression profiles of C . elegans infected with D . coniospora [23] and predicted DAF-16 transcriptional target genes [8] , we found that there was a significant overlap between D . coniospora-upregulated genes and DAF-16 target genes . These findings prompted us to examine the role of DAF-16 in the innate immune response to fungal infection . After exposure of C . elegans to D . coniospora and C . rosea , we found that daf-16 mutants were more susceptible than wild-type worms to killing by fungi . Further studies indicated that fungal infections resulted in the activation of DAF-16 as a consequence of the production of reactive oxygen species ( ROS ) . Similar results were obtained with nematodes subjected to physical injury . Our data demonstrate that DAF-16 can act in a tissue-specific way in the epidermis as an active regulator of immune responses to fungal infection and physical injury .
Under standard growth conditions , DAF-16 is distributed predominately throughout the cytoplasm of all tissues [5] , [6] , [7] . We compared previously identified DAF-16 target genes [8] to published microarray analysis of gene expression in response to D . coniospora infection [23] . 48 of the genes up-regulated by D . coniospora are also targets of DAF-16 ( Figure 1A , Table S1 ) , significantly more than expected by chance ( Fisher's exact test , P<0 . 0001 ) . To further confirm these results , we randomly selected eight of these genes and determined their expression by qPCR ( Figure 1B ) . The expression of these eight genes was significantly elevated after D . coniospora infection . However , daf-16 mutation suppressed the up-regulation of these eight genes induced by D . coniospora . These results suggest that nematophagous fungi could activate the transcription activity of DAF-16 in wild-type worms under standard growth conditions . To test this hypothesis , we monitored the cellular translocation of DAF-16 using transgenic worms that express a functional DAF-16::GFP fusion protein . The status of DAF-16 localization was categorized as cytosolic , intermediate , or nuclear ( Figure 1C ) . We observed that exposure to D . coniospora or C . rosea induced DAF-16 nuclear localization ( Figure 1C ) . In contrast , infection with P . aeruginosa PA14 or S . aureus ATCC 25923 failed to cause increased DAF-16 nuclear accumulation ( Figure 1C ) , consistent with previous studies [7] . Recent studies have demonstrated that fungal infection and epidermal injury activate similar signaling pathways in C . elegans 19 , 24 . Infection by nematophagous fungi causes nematode cuticle damage [18] , [19] , [20] , [21] , [22] . We have previously reported a unique fungal structure , called the spiny ball , on the vegetative hyphae of the fungus Coprinus comatus that damages the nematode cuticle [25] . To investigate the response to physical wounding of the cuticle , we exposed worms to purified C . comatus spiny balls . After nematodes were added to NGM plates containing purified spiny balls ( approximately 10 , 000/plate ) , DAF-16 nuclear localization was observed ( Figure 1C ) . Meanwhile , the expression of the eight genes was significantly up-regulated in wild-type worms , but not in daf-16 ( mu86 ) mutants , after treatment with spiny balls ( Figure 1B ) . We also tested one of classic targets of DAF-16 , sod-3 , using transgenic worms that express Psod-3::GFP . We found that infection of D . coniospora or treatment with spiny balls up-regulated the expression of Psod-3::GFP ( Figure 1D ) . Knock-down of daf-16 by RNAi inhibited the expression of Psod-3::GFP induced by D . coniospora or spiny balls . It should be noted that , similar to fungal infection ( Figure S1A and S1B in Text S1 ) , mutation in daf-2 ( e1370 ) also induced DAF-16 nuclear translocation predominately both in the hypodermis and the intestine without fungal infection ( Figure S1C in Text S1 ) . Meanwhile , we found that either epidermal- or intestinal-specific knock-down of daf-16 by RNAi suppressed the expression of the eight DAF-16 target genes ( Figure S2A and S2B in Text S1 ) . Taken together , these results demonstrate that infection by nematophagous fungi and physical wounding activate DAF-16 in C . elegans . Reduced signaling in the DAF-2 pathway results in the nuclear accumulation of DAF-16 [5] , [6] . P . aeruginosa PA14 infection up-regulates the expression of the insulin-like agonist ins-7 , thus activating the DAF-2 insulin-like signaling [7] , [26] . This is one of the mechanisms by which PA14 suppresses nuclear accumulation of DAF-16 . It is tempting to speculate that in contrast to bacterial infection , fungal infection reduces expression of insulin-like agonists , thereby leading to the activation of DAF-16 . Unexpectedly , like P . aeruginosa PA14 , D . coniospora also up-regulated the expression of ins-7 ( Figure 1E ) . We thus examined the effect of ins-7 on DAF-16 translocation and the immune phenotypes , and found that mutations in ins-7 did not alter DAF-16 translocation and the survival of worms after D . coniospora infection and treatment with spiny balls ( Figure S3A–C in Text S1 ) . In addition , the expression of ins-1 , an antagonist of DAF-2 signaling [27] , [28] , was down-regulated after D . coniospora infection ( Figure 1E ) . These results suggest that similarly to bacterial infection , fungal infection also activates DAF-2 insulin-like signaling , probably by altering the expression of insulin-like peptides . Thus , the activation of DAF-16 does not result from reduced signaling in the DAF-2 pathway , suggesting that other mechanisms exist for the activation of DAF-16 following fungal infection . Since fungal infection activated DAF-16 , we determined the survival rates of daf-16 ( mu86 ) mutants after infection by D . coniospora and C . rosea . We found that daf-16 ( mu86 ) mutants exhibited enhanced susceptibility to killing by D . coniospora ( Figure 2A ) and C . rosea ( Figure S4A in Text S1 ) . Similar results were obtained from worms by daf-16 RNAi ( Figure S4B and S4C in Text S1 ) . These results indicate that DAF-16 is directly involved in controlling fungal resistance in wild-type animals . Meanwhile , we also examined the survival of worms in the presence of spiny balls . daf-16 ( mu86 ) animals were more sensitive than wild-type animals to physical injury ( Figure 2B ) . Unlike pathogenic bacteria that mainly infect the intestine , nematophagous fungi infect the epidermis [23] . To determine tissue-specific activities of DAF-16 in the regulation of immune responses to fungal infection and physical injury , we knocked down daf-16 by RNAi in the intestine , the epidermis , and muscle , respectively . We found that epidermal-specific knock-down of daf-16 resulted in enhanced sensitivity to D . coniospora infection ( Figure 2C ) and physical injury by spiny balls ( Figure 2D ) . The epidermal-specific knock-down of daf-16 did not alter DAF-16 nuclear translocation in the intestine after D . coniospora infection ( Figure S5A and S5B in Text S1 ) . In contrast , intestinal- or muscular-specific daf-16 RNAi had no effect on sensitivity to D . coniospora infection ( Figure 2E , Figure S6A in Text S1 ) and spiny balls ( Figure 2F , Figure S6B in Text S1 ) . In addition , expression of daf-16 under control of an epidermal ( dpy-7 ) promoter [29] enhanced the resistance to D . coniospora infection and physical injury in wild-type animals ( Figure S7A and S7B in Text S1 ) . We conclude that DAF-16 functions within the epidermis of nematode to promote immune responses to fungal infection and physical wounding . Accumulating evidence suggests that the levels of ROS in tissues are induced in response to physical wounding in human epithelial keratinocytes [30] , [31] , [32] , the tail fin of zebrafish larvae [30] , [31] , [32] , and the Drosophila embryo epidermis [30] , [31] , [32] . Since oxidative stress induces the activation of DAF-16 in C . elegans [33] , we hypothesized that the production of ROS is one of the mechanisms underlying DAF-16 activation by fungal infection and physical injury . To test this idea , we first determined the levels of ROS using 2′ , 7′-dichlorodihydrofluorescein diacetate ( H2DCFDA ) , a fluorescent dye that has been used to detect the ROS levels in C . elegans [34] , [35] , [36] . We found that the levels of ROS were dramatically elevated during fungal infection and treatment with spiny balls ( Figure 3A ) . Recent studies demonstrate that dual oxidases ( DUOXs ) mediate ROS production during wound responses in zebra fish larvae and Drosophila embryos [31] , [32] . In C . elegans , there are two DUOX homologs . BLI-3/Ce-DUOX-1 is the major enzyme responsible for the production of ROS [37] . Since mutations in bli-3 and the standard feeding RNAi with construct based on bli-3 result in a severe blistered phenotype [38] , [39] , we tested the worms subjected to RNAi in a 1/10 dilution as described by Chavez et al . [38] , [39] . qPCR analysis demonstrated that that knock-down of bli-3 in a 1/10 dilution reduced more than 50% bli-3 mRNA levels ( Figure S8 in Text S1 ) . We found that the knock-down of bli-3 by RNAi significantly reduced the ROS levels induced by D . coniospora and spiny balls ( Figure 3A ) , indicating that BLI-3 is involved in the increase in ROS levels in these processes . Furthermore , the nuclear accumulation of DAF-16 was markedly reduced by bli-3 RNAi after infection of D . coniospora and treatment with spiny balls , respectively ( Figure 3B ) . Similarly , knock-down of bli-3 by RNAi markedly inhibited the expression of Psod-3::GFP induced by D . coniospora and spiny balls ( Figure 3C ) . Taken together , these results suggest that ROS production is essential for the activation of DAF-16 after fungal infection and physical injury . Meanwhile , bli-3 RNAi enhanced susceptibility to killing by D . coniospora and spiny balls ( Figure 3D and 3E ) . However , in daf-16 ( mu86 ) background , knock-down of bli-3 by RNAi did not cause an increase in susceptibility to D . coniospora and spiny balls compared to daf-16 ( mu86 ) mutants alone . bli-3 is mainly expressed in the epidermis of nematodes [38] . We thus used tissue-specific RNAi to reduce bli-3 function only in the adult epidermis . As expected , after D . coniospora infection and treatment with spiny balls , epidermal-specific RNAi of bli-3 significantly suppressed the production of ROS ( Figure S9A in Text S1 ) , inhibited nuclear accumulation of DAF-16 ( Figure S9B in Text S1 ) , and reduced survival rate of worms ( Figure S9C and S9D in Text S1 ) . In contrast , intestinal-specific knock-down of bli-3 had no such effects ( Figure S9E and S9F in Text S1 ) . These results suggest that BLI-3 functions within the epidermis to promote ROS formation in response to fungal infection and physical injury . BLI-3 is a dual oxidase , which has a NADH oxidase activity and a peroxidase activity [38] . The mutant bli-3 ( e767 ) encodes a protein that lacks the peroxidase domain , but retains its ability to produce ROS . bli-3 ( e767 ) mutants exhibited similar sensitivity to killing by D . coniospora and spiny balls as did wild-type animals ( Figure S10A and S10B in Text S1 ) , indicating that the peroxidase activity of BLI-3 is not crucial for resistance to fungal infection and physical injury . How does fungal infection activate DUOX1 ? BLI-3 contains a Ca2+-responsive EF hand domain [38] , implicating that Ca2+ probably plays a role in regulating the activity of BLI-3 for ROS production in response to fungal infection . We thus determined Ca2+ release using the nematode strain carrying Ca2+ sensor GCaMP3 under the control of epidermal-specific promoters . As shown in Figure 4A , D . coniospora infection induced an increase in GCaMP fluorescence . These results indicate that fungal infection induces Ca2+ release in the epidermis . Increases in intracellular Ca2+ are initiated by the phospholipase C ( PLC ) family of enzymes , which hydrolyze phosphatidylinositol 4 , 5-diphosphate ( PIP2 ) to produce inositol 1 , 4 , 5-trisphosphate ( IP3 ) and diacylglycerol [40] . Since IP3 and its receptor IP3R/ITR-1 contribute to the epidermal Ca2+ release after needle wounding [29] , we tested the role of the IP3/ITR-1 signaling in Ca2+ release after D . coniospora infection . We used worms overexpressing N-terminal IP3 binding domains ( “IP3 sponges” ( cz12690 ) ) in the epidermis [29] . IP3 sponges function as a dominant negative regulator to disturb IP3 signaling . We observed that IP3 sponges led to a decrease in GCaMP fluorescence . GCaMP fluorescence was reduced in itr-1 ( sa73 ) mutants ( Figure 4A ) . Thus , abolishment of the IP3/ITR-1 signaling cascade inhibited Ca2+ release after fungal infection . Next , we tested whether Ca2+ release is required for the formation of ROS and DAF-16 nuclear accumulation after fungal infection and physical injury . An increase in the production of ROS and DAF-16 nuclear accumulation was essentially abolished in worms expressing IP3 sponges in the epidermis after fungal infection and physical injury ( Figure 4B and 4C ) . Meanwhile , blockage of Ca2+ release by mutations in itr-1 also suppressed the production of ROS and DAF-16 nuclear accumulation after D . coniospora infection and treatment with spiny balls ( Figure 4B and 4C ) . Finally , we found that overexpression of IP3 sponges or knock-down of itr-1 by RNAi markedly inhibited the expression of Psod-3::GFP induced by Drechmeria coniospora and spiny balls ( Figure 4D ) . These results demonstrate that the IP3/ITR-1/Ca2+ signaling cascade is genetically upstream of BLI-3 for DAF-16 activation . We asked whether blockage of Ca2+ signaling could influence the survival rate after fungal infection and physical injury . Indeed , worms expressing IP3 sponges in the epidermis were more susceptible than wild-type worms to killing mediated by D . coniospora and spiny balls , respectively ( Figure S11A and S11B in Text S1 ) . Furthermore , mutations in itr-1 ( sa73 ) shifted the survival curve to mimic the daf-16 ( mu86 ) phenotype after D . coniospora infection ( Figure 4E ) and treatment with spiny balls ( Figure 4F ) . However , the survival curve for daf-16 ( mu86 ) ; itr-1 ( sa73 ) double mutants was similar to that of daf-16 ( mu86 ) mutants . In C . elegans , itr-1 is expressed in many tissues , including the epidermis [41] . We found that epidermal-specific RNAi of itr-1 significantly reduced the survival of worms after infection of D . coniospora and treatment with spiny ball ( Figure S12A and S12B in Text S1 ) . In contrast , intestinal-specific knock-down of itr-1 had no such effects ( Figure S12C and S12D in Text S1 ) . These results indicate that the IP3/ITR-1 pathway functions within the epidermis to promote innate immunity against fungal challenge and physical injury . Since needle wounding in C . elegans triggers an EGL-30-EGL-8 signaling cascade , leading to the release of Ca2+ [29] , we tested whether the Gαq protein EGL-30 and the phospholipase C ( PLCβ ) EGL-8 were also required for the production of ROS and DAF-16 nuclear accumulation upon fungal infection and physical injury . We found that the formation of ROS was reduced in egl-30 ( n686 ) or egl-8 ( n488 ) mutants after infection of D . coniospora and treatment with spiny balls ( Figure 5A ) . To confirm the role of egl-30 and egl-8 in the activation of DAF-16 , we crossed the egl-30 or egl-8 mutations into the transgenic worms that express DAF-16::GFP fusion protein . As shown in Figure 5B , the nuclear accumulation of DAF-16::GFP was reduced in egl-30 ( n686 ) or egl-8 ( n488 ) mutants compared to control worms after infection of D . coniospora and treatment with spiny balls . Similarly , mutations in egl-30 or egl-8 significantly suppressed the expression of Psod-3::GFP induced by Drechmeria coniospora and spiny balls ( Figure 5C ) . Both egl-30 ( n686 ) and egl-8 ( n488 ) mutants were more sensitive than wild-type worms to killing by D . coniospora ( Figure 5D ) or spiny balls ( Figure 5E ) , respectively . However , mutations in egl-30 and egl-8 did not alter the daf-16 ( mu86 ) phenotype . daf-16 ( mu86 ) ; egl-30 ( n686 ) or daf-16 ( mu86 ) ; egl-8 ( n488 ) double mutants were indistinguishable from daf-16 ( mu86 ) for sensitivity to D . coniospora ( Figure 5D ) or spiny balls ( Figure 5E ) , suggesting that these genes function in a common pathway . In C . elegans , egl-30 is expressed in many tissues , including the epidermis [42] . In contrast , egl-8 , which is predominantly expressed in neurons , has been shown to act genetically downstream of egl-30 [43] . We found that epidermal-specific , rather than intestinal-specific , knock-down of egl-30 or egl-8 significantly reduced the survival rate of nematodes after D . coniospora infection and treatment with spiny balls ( Figure S13A–D in Text S1 ) . In addition , epidermal-specific expression of egl-30 or egl-8 was sufficient to rescue immune-deficient phenotypes in egl-30 ( n686 ) and egl-8 ( n488 ) mutants to D . coniospora infection and physical injury , respectively ( Figure S14A and S14B in Text S1 ) . These results suggest that the EGL-30-EGL-8 pathway functions within the epidermis to promote innate immunity against fungal challenge and wound response . It has been reported that the mammalian Ste20-like kinase-1 ( MST1 ) mediates oxidative stress-induced activation of FOXO transcription factors [44] . In C . elegans , CST-1 , the ortholog of mammalian MST1 , promotes life-span extension in a DAF-16-dependent manner [44] . Thus , we hypothesized that CST-1 might function analogously to MST1 as an activator of DAF-16 . To test this idea , we assayed the effect of cst-1 knock-down on DAF-16 activation by induced by D . coniospora and spiny balls . cst-1 knock-down by RNAi led to a significant reduction in cst-1 expression ( Figure S15 in Text S1 ) . cst-1 RNAi significantly suppressed the nuclear accumulation of DAF-16 ( Figure 6A ) , but did not influence the production of ROS induced by D . coniospora and spiny balls ( Figure S16 in Text S1 ) . Similarly , knock-down of cst-1 by RNAi significantly inhibited the expression of Psod-3::GFP induced by D . coniospora and spiny balls ( Figure 6B ) . These results suggest that CST-1 acts upstream of DAF-16 , but downstream of BLI-3 in response to fungal infection and wounding . Knock-down of cst-1 by RNAi reduced the survival of nematodes after D . coniospora infection ( Figure 6C ) and treatment with spiny balls ( Figure 6D ) . However , the survival of daf-16 ( mu86 ) ;cst-1 RNAi was comparable to that of daf-16 ( mu86 ) mutants ( Figure 6C and 6D ) . These data suggest that daf-16 is epistatic to cst-1 . cst-1 is mainly expressed in the epidermis , tail , vulva , and sensory neurons in the head [44] . We found that epidermal-specific cst-1 RNAi resulted in enhanced sensitivity after D . coniospora infection ( Figure 6E ) and treatment with spiny balls ( Figure 6F ) , whereas intestinal-specific cst-1 RNAi did not affect the survival of worms ( Figure S17A and S17B in Text S1 ) . These results indicate that cst-1 is required for innate immunity in the epidermis . A previous study demonstrated that BAR-1 , the ortholog to mammalian β-catenin , is required for oxidative stress-induced DAF-16 activity in C . elegans [33] . BAR-1 also plays a positive role in C . elegans intestinal immunity to S . aureus [45] . Thus , bar-1 might be expected to act genetically downstream of bli-3 to activate DAF-16 . However , the nuclear accumulation of DAF-16 was not altered in the bar-1 ( ga80 ) mutants after D . coniospora infection and treatment with spiny balls ( Figure S18 in Text S1 ) . These results suggest that BAR-1 is not involved in the activation of DAF-16 upon fungal infection and wound response .
In a variety of animals , the epidermis may represent a first line of defense against pathogenic infection and physical injury . The key finding in this study is that the DAF-16/FOXO transcription factor is a direct regulator of immune responses associated with epidermal damage . Our data also provides a novel molecular mechanism by which DAF-16 is activated by pathogenic fungi and wounding , and that the pathway is independent of the DAF-2 insulin-like signaling pathway . A sustained production of ROS following injury has been observed in human cells , the zebrafish and Drosophila tissues [30] , [31] , [32] . Recent studies demonstrate that these processes are mediated by DUXOs [31] , [32] . In the current study , we observe that ROS production is mediated by BLI-3 in the epidermis after fungal infection and physical injury . Our study indicates that ROS production is crucial for resistance to fungal infection and physical injury in C . elegans , supporting the idea that injury-induced ROS production is an important regulator of tissue regeneration [46] . Furthermore , our results demonstrate that ROS production is required for activation of DAF-16 , which , in turn is essential for resistance to fungal infection and physical injury in C . elegans . Two recent studies indicate that knock-down of bli-3 by RNAi leads to enhanced susceptibility to E . faecalis [39] , [47] . However , the daf-16 mutants exhibit a comparable degree of susceptibility to E . faecalis-mediated killing as wild-type worms [4] , [48] . These results indicate that the protective effect of BLI-3 on E . faecalis infection is not mediated through DAF-16 . Impairment in release of Ca2+ abolished ROS production upon fungal challenge and wounding , suggesting that BLI-3 enzymatic activity is dependent on Ca2+ . The EF-hand calcium-binding motif in C . elegans BLI-3 has a relatively low amino acid identity ( 41% ) and similarity ( 61% ) to the human DUOX1 , casting doubt as to whether calcium binding is required for C . elegans BLI-3 function [38] . However , the EF-hand calcium-binding motif in Drosophila DUOX1 also shares a relatively low identity ( 42% ) and similarity ( 66% ) to the human DUOX1 . Because ROS-producing Drosophila DUOX1 enzymatic activity depends on intracellular Ca2+ through binding to the EF-hand domains [49] , [50] , it is plausible that Ca2+ modulates the enzymatic activity of C . elegans BLI-3 . The PI3K-Akt-FOXO signaling pathway is evolutionarily conserved from nematodes to mammals [10] , [51] . In mammalian cells , protein kinase Akt , a downstream effector of the insulin-signaling pathway , phosphorylates two sites ( Thr32 and Ser252 ) on the FOXO3 protein leading to its nuclear exclusion and inactivation [52] . Likewise , P . aeruginosa suppresses the activity of DAF-16 by activating DAF-2 insulin-like signaling [7] , [26] . However , our data demonstrate that causal involvement of diminished DAF-2 insulin-like signaling in the activation of DAF-16 by fungal infection is unlikely , suggesting that alternative mechanisms are involved . A previous study has demonstrated that oxidative stress activates FOXO3 through an MST1-mediated mechanism [44] . Under oxidative stress , MST1 phosphorylates FOXO3 at Ser207 and the phosphorylation of FOXO3 in turn induces its dissociation from 14-3-3 proteins and translocation to the nucleus [44] . Although knock-down of daf-16 by RNAi completely inhibits the ability of CST-1 to extend life span in C . elegans , whether CST-1 activates DAF-16 under oxidative stress remains unclear . Our data indicate that ROS mediates activation of DAF-16 in response to epidermal damage in a CST-dependent manner . These results support a model in which the evolutionarily conserved MST/CST pathway functions in parallel with the insulin signaling pathway to regulate FOXO/DAF-16 by oxidative stress [44] . The epidermis forms a protective barrier against physical damage and pathogen entry [53] , [54]–[55] . An intimate relationship between wound repair and innate immunity is widely accepted [56] . Previous studies have shown that epidermal immune responses to fungal infection and physical wounding share some of the same signals and mediators in C . elegans 19 , 24 . For instance , Gα12/GPA-12 acts , together with the two phospholipases EGL-8 and PLC-3 , upstream of the PKC-TIR-1-p38 MAPK pathway , to induce a set of the nlp genes encoding antimicrobial peptides ( AMPs ) in response to fungal challenge and needle wounding [19] , [24] . A recent study has shown that the EGL-30-EGL-8 signaling pathway triggers epidermal Ca2+ release through IP3 and its receptor ITR-1 after wounding [29] . In this study , our results indicate that DAF-16 is activated by EGL-30-Ca2+ upon fungal infection and physical injury . Since epidermal DAF-16 is required for innate immune response to fungal infection and physical injury , it is an important immune effector of EGL-30-Ca2+ in the epidermis . However , mutations in daf-16 do not alter the expression of AMPs induced by fungal infection , which is consistent with the observation that the EGL-30-Ca2+ pathway appears not to be involved in the up-regulation of AMPs after wounding [29] . Because FOXOs are conserved from worms to humans , it is of great interest to investigate whether FOXOs are involved in epidermal innate immunity in other species ( e . g . , humans ) . FOXOs have been shown to mediate the induction of antimicrobial peptides , such as defensins , both in Drosophila and human tissues [10] . Accumulating evidence indicates that defensins , the major skin-derived antimicrobial peptides , not only act as endogenous antibiotics , but also participate in additional roles such as promoting wound repair [57] , [58] . Meanwhile , inhibition of PI3K , a component of insulin/insulin-like growth factor signaling , by LY294002 ( a specific inhibitor of PI3K ) , strongly accelerates scratch closure in human keratinocytes [59] . Because reduced signaling of the insulin/insulin-like growth factor pathway leads to the activation of FOXO transcription factors , these results imply that FOXOs are probably involved in keratinocyte wound healing . A recent study has investigated epidermal gene expression in wounded skin from three donors and examined transcription factor binding sites ( TFBS ) in the promoters of the 100 most differentially expressed genes [60] . Highly significant overrepresentations of TFBS for FOXO transcription factors are identified . These data suggest that FOXOs are possibly involved in controlling the epidermal gene expression during the proliferative phase of wound healing . Thus , the DAF-16/FOXO transcription factor that functions as an effector of innate immunity in epidermal tissues seems to be evolutionarily conserved in various animal species including worms , insects and mammals . In summary , our findings suggest that DAF-16 is directly involved in innate immunity in the epidermis . EGL-30/Ca2+/BLI-3/ROS/CST-1 signaling represents a novel pathway to regulate DAF-16 activity ( see model in Figure 7 ) , which is functionally independent of the DAF-2 insulin-like signaling pathway . Based on these findings , we propose that FOXO/DAF-16 could be a novel target for the treatment of epidermal damage .
The following C . elegans strains were used in this study: N2 ( wild-type ) , daf-16 ( mu86 ) , bli-3 ( e767 ) , itr-1 ( sa73 ) , bar-1 ( ga80 ) , egl-30 ( n686 ) , egl-8 ( n488 ) , ins-7 ( ok1573 ) , TJ356-daf-16::gfp ( zIs356 ( pDAF-16::DAF-16-GFP;rol-6 ) ) , muIs84 ( Psod-3::gfp ) , NR222 ( rde-1 ( ne219 ) ; kzIs9[pKK1260 ( plin-12::nls::gfp ) , pKK1253 ( plin-26::rde-1 ) , rol-6] ) ; and NR350 ( rde-1 ( ne219 ) ; kzIs20[pDM#715 ( phlh-1::rde-1 ) , pTG95 ( psur-5::nls::GFP ) , rol-6] ) were kindly provided by the Caenorhabditis Genetics Center ( CGC ) . The CZ13896 ( Pcol-19-GCaMP ( juIs319 ) ) , CZ12690 ( Pcol-19-Superspronge ( juEx3052 ) ) , and CZ15386 ( egl-8 ( sa47V;egl-8 ( juEX4257 ) ) strains were kindly provided by Dr . Andrew D . Chisholm ( University of California San Diego ) . The strain GR1353 ( daf-2 ( e1370 ) III; mgIs41[daf-16::gfp] ) and the strain for intestinal-specific RNAi ( sid-1 ( qt9 ) ; Is[vha-6pr::sid-1]; Is[sur-5pr::GFPNLS] ) were kindly provided by Dr . Gary Ruvkun ( Massachusetts General Hospital , Harvard Medical School ) . Mutants and transgenic strains were backcrossed three times into the N2 strain used in the laboratory . All strains were maintained on nematode growth media ( NGM ) and fed with E . coli strain OP50 . Standard conditions were used for C . elegans growth at 20°C [61] . Synchronized populations of worms were cultivated at 20°C until the mid-L4 stage . For all pathogen assays , 75 µg/ml of fivefluoro-2′-deoxyuridine ( FUdR ) was added to the assay plates to abolish the growth of progeny . Killing assays with D . coniospora: 50–60 L4 nematodes were transferred to fresh plates seeded with heat-killed E . coli OP50 , with ∼1 . 0×108 D . coniospora spores at 25°C . Killing assays with C . rosea: ∼1 . 0×108 spores of C . rosea were inoculated onto plates containing heat-killed E . coli OP50 for 1–2 days at 28°C , and the infection experiments were started by adding 50–60 nematodes to each plate at 25°C . The number of living worms was counted by using a light microscope at time intervals . Immobile nematodes unresponsive to touch were scored as dead . One-sided rank log tests were used to the statistical significance of the differences between treatments . Killing assays with P . aeruginosa: P . aeruginosa PA14 ( a gift from Dr . Kun Zhu , Institute of Microbiology , CAS ) was cultured in Luria broth ( LB ) , then seeded on slow-killing plates , which contain modified NGM ( 0 . 35% instead of 0 . 25% peptone ) . PA14 was incubated first for 24 h at 37°C and then for 24 h at 25°C . The infection experiments were started by adding 50–60 nematodes to each plate at 25°C . Killing assays with S . aureus: S . aureus ATCC 25923 ( a gift from Dr . Wen-Hui Lee , Kunming Institute of Zoology , CAS ) was cultured in tryptic soy broth ( TSB , BD , Sparks , MD ) , then seeded on plates containing modified NGM ( 0 . 35% instead of 0 . 25% peptone ) . The infection experiments were started by adding 50–60 nematodes to each plate at 25°C . Spiny balls were purified by a previously described method [25] . The spiny ball suspension was adjusted to ∼1 . 0×105 per ml . 100 µl of the spiny ball suspension was thoroughly added to on plates containing modified NGM with heat-killed E . coli OP50 . The infection experiments were started by adding 50–60 nematodes to each plate at 25°C . Mobile and immobile nematodes were counted every 12 h after their addition . RNAi bacterial strains containing targeting genes were obtained from the Ahringer RNAi library [62] . RNAi feeding experiments were performed on synchronized L1 larvae at 20°C for 40 h . L4 larvae or young adult worms were used in immunity assays . The strain NR222 was used in epidermis-specific RNAi , the strain ( sid-1 ( qt9 ) ; Is[vha-6pr::sid-1]; Is[sur-5pr::GFPNLS] ) was used in intestine-specific RNAi , and the strain NR350 was used in muscular-specific RNAi . After 12 h of fungal infection or treatment with spiny balls , the worms were immediately mounted in M9 onto microscope slides . The slides were viewed using a Zeiss Axioskop 2 plus fluorescence microscope ( Carl Zeiss , Jena , Germany ) with a digit camera . The status of DAF-16 localization was categorized as cytosolic localization , nuclear localization when localization is observed throughout the entire body from head to tail , or intermediate localization when there is a visible nuclear localization but one not as complete as nuclear [63] . The number of worms with each level of nuclear translocation was counted . Total RNA from worms was isolated using Trizol reagent ( Invitrogen , Carlsbad , CA ) . Random-primed cDNAs were generated by RT of the total RNA samples using a standard protocol . A real-time-PCR analysis was performed with the ABI Prism 7000 Sequence Detection system ( Applied Biosystems , Foster City , CA ) using SYBR Premix-ExTag ( Takara , Dalian , China ) . β-Tubulin was used for internal control . The primers used for PCR are listed in Table S2 . The dpy-7 and clo-19 genes have shown to be expressed in the epidermis [29] . The Pdpy-7:daf-16 fusion gene was chemically synthesized , and obtained from Generay Biotech Co . ( Shanghai , China ) . The DNA fragment contains a 436 bp of dpy-7 promoter fragment ( corresponding to nucleotide −436 to −1 relative to the translational start site ) , a 1530 bp of the daf-16 cDNA , a 729 bp of the GFP cDNA and a 234 bp of the 3′-UTR of unc-54 . The Pcol-19:egl-30 fusion gene was constructed as follows . A 2838 bp of col-19 promoter fragment was obtained by PCR on C . elegans genomic DNA using primers 5′-GCT CTA GAG CAT CGT CAC ATT CTG TCT-3′ and 5′-TCC CCC GGG GGC TTT CCA TCG TCT CC-3′ followed by XbaI and SmaI digestion . The fragment was inserted into XbaI and SmaI digested pPD95 . 79 , resulting in plasmid pPDegl . A 1116 bp fragment of egl-30 cDNA was amplified by PCR on C . elegans genomic DNA using primers 5′- TCC CCC GGG TTG TTC TAT TCG CTG GCT T-3′ and 5′-GGG GTA CCC CAA GTT GTA CTC CTT CAG ATT AT-3′ followed by SmaI and KpnI digestion . The fragment was inserted into SmaI and KpnI digested pPDegl vector . The Pdpy-7:daf-16 fusion gene fragment or the vector containing Pcol-19::egl-30 fusion gene was co-injected with rol-6 plasmid ( pRF4 ) into gonads of wild-type and egl-30 ( n686 ) animals by standard techniques [64] . The transgenic worms carrying Pdyp-7::daf-16 or Pcol-19::egl-30 were confirmed in prior to each pathogenesis assay . The ROS levels were detected by 2′ , 7′-dichlorodihydrofluorescein diacetate ( DCF-DA ) as a probe as described previously with modifications [34] , [35] , [36] . Briefly , after infected with pathogens or treated with spiny balls for 8 h , about 1000 worms from each group were collected in M9 buffer and washed three times to eliminate conidia . Then , the worms were transferred to a 1 . 5-mL tube containing 150 µl PBS with 1% Tween 20 , and immediately frozen in liquid nitrogen . After thawing at room temperature , the worms were subjected to sonication ( Branson Sonifier 250; VWR Scientific , Suwanee , GA ) . Samples were vortexed , and supernatants were collected after centrifugation . The supernatant containing 10 µg protein was transferred into 96-well plates , and incubated with 15 µL of 100 µM DCF-DA in PBS at 37°C in a Spectra Max M5 fluorescent microplate reader ( Molecular Devices , Sunnyvale , CA ) for quantification of fluorescence at excitation 485 nm and emission 530 nm . Samples were read kinetically every 20 min for 2 . 5 h . To analyze Ca2+ in the epidermis , GCaMP fluorescence imaged was obtained using confocal microscopy ( Zeiss LSM-510 ) with a 40×objective , as described previously [29] . Briefly , average fluorescence was determined in ten equivalent regions of interest ( ROI ) , five centered on the epidermal cell and five in the background . Baseline fluorescence ( F0 ) and induction fluorescence ( Ft ) were obtained by averaging fluorescence in five ROIs in the epidermis then subtracting the average of five ROIs in the background before and after fungal infection or injury . GCaMP fluorescence was normalized to an internal control , Pcol-19-tdTomato . The change in fluorescence ΔF was expressed as the ratio of change with respect to the baseline [ ( Ft–F0 ) /F0] . Raw data from fluorescent microscopy were then analyzed using ImageJ . Differences in survival rates were analyzed using the log-rank test . Differences in gene expression , distribution of DAF-16 , and fluorescence intensity were assessed by performing a one-way ANOVA followed by a Student-Newman-Keuls test . Data were analyzed using SPSS11 . 0 software ( SPSS Inc . ) . To test for significant overlap between different gene lists , a Fisher's exact test was used . | In the natural environment , animals encounter different pathogens . Thus , different tissues within an organism must develop specific immune systems for survival . The epidermis acts as a physical barrier and represents a first line of defense against infection and physical injury in a variety of animals . Natural nematophagous fungi , such as Drechmeria coniospora and Clonostachys rosea , infect the epidermis of the roundworm Caenorhabditis elegans by producing conidia . Here we demonstrated that the DAF-16/FOXO transcription factor in the epidermis has a direct role in C . elegans defense against fungal infection and physical injury . We found that the EGL-30/EGL-8/IP3/ITR-1 signaling pathway triggers epidermal Ca2+ release through IP3 and its receptor ITR-1 after fungal infection . Ca2+ release induces the production of reactive oxygen species ( ROS ) by activating a dual-oxidase BLI-3 . ROS in turn mediate DAF-16 activation in a Ste20-like kinase-1/CST-1-dependent manner . Thus , DAF-16 could act in a cell-autonomous way in the epidermis as an active regulator of immune responses to fungal infection and physical injury . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The DAF-16/FOXO Transcription Factor Functions as a Regulator of Epidermal Innate Immunity |
Plague in Brazil is poorly known and now rarely seen , so studies of its ecology are difficult . We used ecological niche models of historical ( 1966-present ) records of human plague cases across northeastern Brazil to assess hypotheses regarding environmental correlates of plague occurrences across the region . Results indicate that the apparently focal distribution of plague in northeastern Brazil is indeed discontinuous , and that the causes of the discontinuity are not necessarily only related to elevation—rather , a diversity of environmental dimensions correlate to presence of plague foci in the region . Perhaps most interesting is that suitable areas for plague show marked seasonal variation in photosynthetic mass , with peaks in April and May , suggesting links to particular land cover types . Next steps in this line of research will require more detailed and specific examination of reservoir ecology and natural history .
Plague arrived in Brazil during the Third Pandemic , in October 1899 , imported by ship traffic to Santos , in São Paulo state , and was rapidly diffused to other coastal cities . By 1906 , it had dispersed by means of land and sea commerce more broadly , and had become established in native rodent populations , particularly in the northeastern sector of the country [1] , [2] . Nonetheless , records of plague in Brazil are sparse through the 1920s , making detailed tracking of the pattern of spread of the disease in the region difficult or impossible [1] , [3] . Only by around 1936 were data on plague and its control in Brazil regularly collated and archived . Based on analyses of these data ( for 1936–1966 ) , Baltazard [1] identified numerous distinct plague foci occurring in different environmental contexts , a viewpoint that was updated by Vieira & Coelho [4] , based principally on elevation . These foci appear to exist independently of one another in time and space [1] , and overall numbers of human cases varied from 20 to 100 until the 1970s . Since that time , all of the foci entered a period of relative inactivity , with few or no human cases [5] , [6] , [7] , [8] . The last significant outbreak in Brazil was in the late 1980s in Paraíba [9] . The purpose of this contribution is to present a first range-wide analysis of the geography and ecology of plague transmission in northeastern Brazil using tools drawn from the emerging field of ecological niche modeling , which is beginning to see application to plague biology [10] , [11] . Although no recent plague transmission to humans has been recorded in this region , plague remains as a zoonosis across much of northeastern Brazil [12] , making a thorough understanding of its geographic distribution an ongoing priority . Here , we marshal new tools from quantitative biogeography in the form of ecological niche modeling approaches , which related known points of occurrence to raster geospatial GIS data layers to estimate the ecological niche of a species or other biological phenomenon , such as transmission of a disease [11] . The result is both a spatial prediction of areas of potential transmission and a first-order evaluation of environmental correlates of plague transmission in northeastern Brazil .
Methods and approaches for estimating ecological niches from species' occurrence data have seen considerable exploration in recent years [23] , [24] . Outcomes of these tests have been mixed , with some serious criticisms of the algorithm used herein , the Genetic Algorithm for Rule-Set Prediction ( GARP ) [25]—these criticisms [23] , [26] , however , have been based either on misunderstandings of how to use the algorithm [27] or on artifactual differences in performance measures [28] , [29] . In reality , and when properly used and evaluated , GARP offers estimates of species' ecological niches that are highly robust to small sample size and to broad gaps in spatial coverage of landscapes in terms of input data [28] , [29]—for this reason , we used this approach throughout this study . GARP is an evolutionary-computing method that estimates niches based on non-random associations between known occurrence points for species and sets of GIS coverages describing the ecological landscape . Occurrence data are used by GARP as follows: 50% of occurrence data points are set aside for an independent test of model quality ( extrinsic testing data ) , 25% are used for developing models ( training data ) , and 25% are used for tests of model quality internal to GARP ( intrinsic testing data ) . Distributional data are converted to raster layers , and by random sampling from areas of known presence ( training and intrinsic test data ) and areas of ‘pseudoabsence’ ( areas lacking known presences ) , two data sets are created , each of 1250 points; these data sets are used for rule generation and model testing , respectively . The first rule is created by applying a method chosen randomly from a set of inferential tools ( e . g . , logistic regression , bioclimatic rules ) . The genetic algorithm consists of specially defined operators ( e . g . crossover , mutation ) that modify the initial rules , and thus the result are models that have “evolved”—after each modification , the quality of the rule is tested ( to maximize both significance and predictive accuracy ) and a size-limited set of best rules is retained . Because rules are tested based on independent data ( intrinsic test data ) , performance values reflect the expected performance of the rule , an independent verification that gives a more reliable estimate of true rule performance . The final result is a set of rules that can be projected onto a map to produce a potential geographic distribution for the species under investigation . Because each GARP run is an independent random-walk process , following recent best-practices recommendations [30] , for each environmental data set ( see above ) , we developed 100 replicate random-walk GARP models , and filtered out 90% based on consideration of error statistics , as follows . The ‘best subsets’ methodology consists of an initial filter removing models that omit ( omission error = predicting absence in areas of known presence ) heavily based on the extrinsic testing data , and a second filter based on an index of commission error ( = predicting presence in areas of known absence ) , in which models predicting very large and very small areas are removed from consideration . Specifically , in DesktopGARP , we used a “soft” omission threshold of 20% , and 50% retention based on commission considerations; the result was 10 ‘best subsets’ models ( binary raster data layers ) that were summed to produce a best estimate of geographic prediction . We took as a final ‘best’ prediction for each species that area predicted present by any , most , or all 10 of these best-subsets models . Predictive models of disease occurrence may be good or bad , but model quality can be ascertained only via evaluation with independent testing data , preferably which are spatially independent of the training data to avoid problems caused by spatial autocorrelation and nonindependence of points [28] . Because only data documenting presence of plague cases were available for this study ( i . e . , no data were available to document that plague was absent at particular sites ) , we used a binomial probability approach to model validation: we compared observed model performance to that expected under a null hypothesis of random association between model predictions and test point distribution . Because such tests require binary ( i . e . , yes-no ) predictions , our first step was to convert raw ( continuous ) predictions to binary predictions . We considered three distinct thresholds: areas predicted as suitable by any ( i . e . , ≥1 ) of the 10 replicate best-subsets models ( ANY ) , areas predicted as suitable by most ( i . e . , >5 ) of the 10 replicate best-subsets models ( MOST ) , and areas predicted as suitable by all of the 10 replicate best-subsets models ( ALL ) . In the binomial tests , the number of test points was used as the number of trials , the number of correctly predicted test points as the number of successes , and the proportion of the study area predicted present as the probability of a success if predictions and points were associated at random [31] . All testing was carried out in a series of spatially stratified tests that are detailed below . These tests evaluated the ability of models to anticipate plague case distributions across unsampled areas , considering a model as validated if it predicts case distributions better than a “model” making random predictions . As such , these tests are considerably more stringent than simple random partitions of occurrence data or cross-validation exercises . In view of the odd , focal distribution of northeastern Brazilian plague cases ( Figure 1 ) , we carried out a series of tests of predictive abilities of models among the five foci that are easily discernable . In each case , we examined model predictivity in a k – 1 framework: with k = 5 foci , we tested all combinations of 4 foci by means of their ability to predict spatial distributions of plague cases in the fifth focus . Tests were developed within two spatial contexts—within 50 km and within 200 km—surrounding the known occurrences within the target focus . Finally , we wished to develop a single overall model that represents the best-available picture of plague case-occurrence risk across northeastern Brazil , albeit not including statistical testing as above . This model was built using all occurrence data available . To assess uncertainty in these predictions based on all case-occurrence information , we built 100 models each based on a random 50% of the occurrence data chosen at random without replacement . These models thus capture the degree to which plague case-occurrence data availability may drive the results of the analyses , and we consider areas that are predicted consistently in all of these replicate analyses as most certain . We projected this model onto environments across eastern Brazil to provide a broader-extent visualization of the ‘niche’ of plague in northeastern Brazil . To explore environmental factors associated with positive and negative predictions of suitability for DF transmission , we explored further the environmental correlates of the model based on all points . We plotted 1000 points randomly across areas of the municipalities predicted as absent or present by this model . We then assigned the value of each input environmental and topographic layer to each of the random points , and exported the associated attributes table in DBF format , which was then used for comparisons of environmental characteristics of areas predicted as suitable and unsuitable .
The focal and discontinuous nature of plague case distributions in northeastern Brazil is at once visible in the raw distribution of the occurrence points derived at the outset of this study ( Figure 1 ) . The discontinuities that have been assumed based on the clusters of known occurrences are supported by our ecological niche models , many of which show relatively small areas of highly suitable conditions separated by less-suitable areas ( see , e . g . , Figure 2 ) . What is more , this result is manifested with or without elevation included in the analysis , and thus is not a simple consequence of topographic differences; it is also manifested in analyses based on both surface reflectance ( NDVI ) and climatic variables . As such , we interpret the discontinuity of plague distributions in northeastern Brazil as dependent on a multidimensional suite of environmental variables . The model predictions in general performed quite well in anticipating plague case distributions in areas not included in model training . That is , plague not only occurs in discontinuous foci , but it also occurs under predictable and circumscribed environmental conditions , which is the basis for the success of the niche model predictions . The broadest panorama of results shows significant results dominating in the southwestern and northwestern foci ( Table 1 ) . However , the frequency of significant results in these tests is clearly and linearly related to sample size on a log10 scale ( P<0 . 05 ) , suggesting that predictivity would be excellent throughout the region were sample size distributions to be more adequate . Finally , visualizing plague distributions in environmental dimensions ( Figure 3 ) , we see clear differences in the seasonal pattern of variation in greenness between areas predicted as suitable ( i . e . , suitability value of 10 ) and those predicted as unsuitable ( value 0 ) . That is , no marked seasonal variation is notable in areas predicted as unsuitable , whereas areas predicted as suitable show a marked elevation in greenness in April and May , and lower values thereafter , probably corresponding to patterns of rainfall ( i . e . , rainy season beginning in March , and ending by August ) . Extending the model predictions across broader areas—namely all of northeastern and eastern Brazil—yields a picture of potential plague distribution across the region ( Figure 4 ) . Because plague transmission to humans in Brazil is currently nil , and no broad-extent data are available regarding circulation among mammals , we have few means of testing the reality of these model projections . However , at least in the case of models based on climatic dimensions , the area predicted as suitable includes the Serra dos Orgãos sites from which plague has been documented [5] , [32] .
The models that we developed for Brazilian plague offer several intriguing insights into plague distribution , ecology , and natural history in Brazil . However , understanding the limitations of these models is critical , prior to any detailed interpretation or exploration . First and foremost among the limitations of this study are the occurrence data used as input: we relied on human case-occurrence reports accumulated by the Serviço Nacional de Referência em Peste do Centro de Pesquisas Aggeu Magalhães and published in diverse scientific publications [6] , [7] , [8] , [9] , [13] . Our use of these data thereby assumes that human case-occurrences are representative of the ecological and environmental situations under which plague is maintained in the zoonotic world , which may not be the case , given the long chain of events necessary for a zoonotic occurrence to be represented in our data set ( i . e . , transmission to human , correct diagnosis , international reporting ) . On a finer scale , we also make the not-completely-satisfactory assumption that that place of residence ( at the level of the ranch or settlement ) is representative of the site of infection , which may be variably true depending on the particular social network and local economy . One point that became clear in our analyses , confirming previous opinions , is that plague has a highly discontinuous and focal distribution in northeastern Brazil . Our initial suspicions that elevation played a significant role in creating these ‘islands’ were not supported , as analyses with and without elevation included as a predictor variable reconstructed the insular nature of the distribution . The NDVI-based analyses are particularly instructive , as they have no direct , mathematical relation to elevation [as do climate interpolations; 21]—rather , the discontinuous plague distribution in northeastern Brazil appears to reflect multidimensional qualities of the landscape and environment ( which of course may be related biologically to elevation ) , rather than any simple univariate causation . Previous studies had attributed the cause of plague focality in Brazil to elevation [1] . Baltazard [1] emphasized that Brazilian plague foci are independent—that is , that transmission appears to occur in uncorrelated patterns in different foci . Baltazard [1] also pointed out that these foci are all in elevated areas , and that they are subject to distinct precipitation regimes . Although plague has frequently shown long periods of apparent inactivity ( i . e . , no human cases ) , its reappearance at intervals nonetheless indicates its long-term persistence . The foci are limited geographically , although their footprint can appear to expand during major outbreaks . These expansions appear to correspond to periods of particularly favorable conditions for plague transmission in the highland area , spreading out via valleys into the surrounding lowland areas . If these favorable conditions persist , taking the form of a prolonged winter , rodent host reproduction may be elevated , and plague may be able to spread beyond the limit of the highland areas into the dry sertão per se . This line of thinking led Baltazard [1] to consider the plague foci of Serra da Ibiapaba , Serra do Baturité , Serra do Machado , Serra de Uruburetama , Serra da Pedra Branca , Serra das Matas in northern Ceará ( see Figure 1 ) as a single focus . Vieira and Coelho [4] , in contrast , argued that these foci should be treated as isolated and independent . Our analyses suggest that these foci are dependent on a broad suite of conditions , and are not simple or direct correlates of elevation . Another factor that may play in the picture of focality is the presence of key rodent hosts for plague , including Necromys lasiurus ( formerly placed in Bolomys and Zygodontomys ) . Necromys is the rodent that is most abundant in northeastern Brazilian plague foci , and was considered as responsible for causing epizootics , from which the infection spreads to other species [1] . Given the distribution of this species , other species of rodents must be involved in plague maintenance farther south , for example in the Serra dos Órgãos , Rio de Janeiro state , Brazil . The relative roles of the distribution of the rodent hosts and the fleas ( Polygenis spp . ) remain to be evaluated in detail . | We analyzed the spatial and environmental distributions of human plague cases across northeastern Brazil from 1966-present , where the disease is now only rarely transmitted to humans , but persists as a zoonosis of native rodent populations . We elucidated environmental correlates of plague occurrences by way of ecological niche modeling techniques utilizing advanced satellite imagery and geospatial datasets to better understand the ecology and geography of the transmission of plague . Our analysis indicates that plague foci in Brazil are indeed insular as previously suggested . Furthermore , distribution of such foci are likely not directly dependent on elevation , and rather are contigent on climate and vegetation . Seasonality of zoonotic plague transmission is linked to variations of these ecological parameters- particularly the increase in precipitation and primary production of the rainy season . Spatial analysis of transmission events afford a broad view of potential plague foci distributions across northeastern Brazil and indicate that the epidemiology of plague is driven by a dynamic array of environmental factors . | [
"Abstract",
"Introduction",
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] | [
"infectious",
"diseases/neglected",
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] | 2011 | Ecology and Geography of Plague Transmission Areas in Northeastern Brazil |
Microbial pathogens impose selective pressures on their hosts , and combatting these pathogens is fundamental to the propagation of a species . Innate immunity is an ancient system that provides the foundation for pathogen resistance , with epithelial cells in humans increasingly appreciated to play key roles in innate defense . Here , we show that the nematode C . elegans displays genetic variation in epithelial immunity against intestinal infection by its natural pathogen , Nematocida parisii . This pathogen belongs to the microsporidia phylum , which comprises a large phylum of over 1400 species of fungal-related parasites that can infect all animals , including humans , but are poorly understood . Strikingly , we find that a wild C . elegans strain from Hawaii is able to clear intracellular infection by N . parisii , with this ability restricted to young larval animals . Notably , infection of older larvae does not impair progeny production , while infection of younger larvae does . The early-life immunity of Hawaiian larvae enables them to produce more progeny later in life , providing a selective advantage in a laboratory setting—in the presence of parasite it is able to out-compete a susceptible strain in just a few generations . We show that enhanced immunity is dominant to susceptibility , and we use quantitative trait locus mapping to identify four genomic loci associated with resistance . Furthermore , we generate near-isogenic strains to directly demonstrate that two of these loci influence resistance . Thus , our findings show that early-life immunity of C . elegans against microsporidia is a complex trait that enables the host to produce more progeny later in life , likely improving its evolutionary success .
Infectious disease is one of the strongest drivers of evolution , generating diversification in hosts and pathogens through a dynamic co-evolutionary process of adaptation and counter-adaptation . The dynamism of these relationships is apparent in emerging infectious diseases , whereby an interaction between organisms changes from being benign to being harmful for the host [1] . Emerging diseases can have devastating effects on their hosts , and understanding how infectious diseases emerge is therefore a major goal for medicine , agriculture , and evolutionary biology . Microsporidia are emerging pathogens that comprise a diverse phylum of more than 1400 species of fungal-related obligate intracellular parasites that are able to infect virtually all animals [2–5] . Encephalitozoon intestinalis is one of the many species known to infect humans , and stands out as having the smallest eukaryotic genome identified to date [6] . One consequence of the genomic reduction observed in microsporidia is their reliance on host metabolic machinery for propagation . Microsporidia commonly infect intestinal epithelial cells and can be transmitted via a fecal-oral route , although tissue tropism varies broadly . Incidences of microsporidia infection in humans were previously thought to be restricted to immunodeficient patients , but several recent studies have found an unexpectedly high prevalence among healthy people in developed countries , although the overall impact of microsporidia on the health of immunocompetent people is poorly defined [7–9] . In addition to their previously underappreciated disease-causing potential in humans , microsporidia are considered emergent pathogens of agriculturally important animals including fish and honeybees [10–12] . Despite such ubiquity , little is known about the genetic and molecular basis for pathogen defense in any host-microsporidia interaction . Immune defense against pathogens such as microsporidia will provide evolutionary benefit if it enables hosts to produce more progeny . As such , evolutionary theory predicts that there will be less selection for immunity in post-reproductive animals [13] . The decline of immune function is termed immunosenescence , and has been observed in post-reproductive animals ranging from humans to invertebrates [14–16] . In the human population immunosenescence has been shown to be a complex trait regulated by several genetic loci [17] . Several outstanding questions remain regarding the process of immunosenescence , including its precise timing over the lifetime of an organism and how it has been shaped by pathogens over evolutionary time . We use the nematode Caenorhabditis elegans as a convenient host to characterize resistance to a natural microsporidian pathogen . This pathogen is called Nematocida parisii , or nematode-killer from Paris , because it was isolated from wild-caught C . elegans from a compost pit near Paris [18] . The life cycle of N . parisii is similar to those of other microsporidia species , which use a specialized infection apparatus called a polar tube to invade directly into host cells , where they undergo their life cycle ( S1 Fig . ) . In the case of N . parisii , spores are consumed by C . elegans , enter the intestinal lumen , and then invade intestinal cells . This N . parisii ‘sporoplasm’ becomes a meront , which replicates in direct contact with host cytosol , and then differentiates back into spores . These spores enter the host trafficking system , exit host cells via apical exocytosis back into the intestinal lumen , and return to the outside environment via defecation [19] ( S1 Fig . ) . Wild-caught nematodes infected with Nematocida species have been isolated from many distinct environmental locations [18 , 20] , suggesting that microsporidia have imposed widespread evolutionary pressure on the defense system of C . elegans . C . elegans has no known professional immune cells and relies predominantly on epithelial cells as ‘non-professional’ immune cells for defense against infection [21 , 22] . The C . elegans intestine is a relatively simple structure composed of just 20 non-renewable epithelial cells that share structural and functional similarity with human intestinal epithelial cells [23] . Thus , the natural C . elegans-N . parisii host-pathogen pair provides an excellent system in which to investigate epithelial defenses shaped over evolutionary time . Here , we show that there is natural variation in C . elegans defense against microsporidia . We find that a C . elegans strain from Hawaii has enhanced resistance to N . parisii compared to other C . elegans strains . Interestingly , immunity in the Hawaiian strain occurs via clearance of intracellular infection from intestinal epithelial cells . This clearance of N . parisii represents an impressive example of non-professional immune cells being able to not just resist but eliminate microbial infection . Intriguingly , only very young ( first larval stage L1 ) animals are able to clear infection; Hawaiian animals rapidly lose this ability even before they reach reproductive age . We find that N . parisii infection impairs progeny production only when animals are inoculated at the L1 stage , and not when they are inoculated at the later L4 stage , providing a likely evolutionary explanation for why there is enhanced resistance only in L1 animals . Enhanced resistance confers a selective advantage , allowing Hawaiian animals to outcompete a susceptible host strain in only a few generations . Finally , we determine that Hawaiian resistance to N . parisii is a complex multigenic trait that maps to at least four quantitative trait loci ( QTL ) , and we show with near-isogenic lines ( NILs ) how two of these loci contribute to resistance . These results demonstrate that intestinal epithelial cells in a wild C . elegans strain can eliminate intracellular microsporidia infection . Interestingly , this complex trait acts only at a developmental stage in which it promotes progeny production , and thus likely provides an evolutionary benefit to the host .
To determine whether there is natural variation in the ability of C . elegans to defend against its natural intracellular pathogen N . parisii , we investigated infection in a collection of geographically diverse C . elegans strains . N . parisii has been shown to shorten the lifespan of C . elegans due to a lethal intestinal infection [18] , and so we first examined survival upon infection using six strains that represent diverse haplotypes from a global collection of C . elegans [24] . We infected populations of synchronized first larval stage ( L1 ) animals with N . parisii spores and quantified the percentage of animals alive over time . In these experiments , we observed variation in the survival during infection with time to 50% of animals dead ( TD50 ) ranging from 90 to 120 hours among the various C . elegans strains ( Fig . 1A ) . The standard C . elegans N2 laboratory strain from Bristol , England had a relatively short TD50 of about 90 hours . This strain has been passaged under laboratory conditions for decades , and interestingly , did not have significantly different longevity than the C . elegans wild-caught strain ERT002 from Paris , France , which has been passaged very little under laboratory conditions . Also of note , ERT002 harbored the original isolate of N . parisii [18] , indicating that it had been exposed to pressure from microsporidia in the wild in the recent past . Strains JU778 from Portugal and JU258 from Madeira had intermediate levels of survival upon infection . By contrast , strain ED3046 from South Africa and strain CB4856 from Hawaii , USA ( hereafter designated HW ) survived significantly longer than the other strains . Furthermore , we observed that all strains lived longer in the absence of infection ( S2 Fig . ) . N2 , HW , and JU258 had similar lifespans in the absence of infection , which were on average slightly longer than those of ERT002 , JU778 , and ED3046 . Thus , there is natural variation in survival of C . elegans upon infection by its natural intracellular pathogen , N . parisii . Variation in survival upon infection could be due to variation in resistance ( the ability to control pathogen load ) or tolerance ( the ability to cope with infection ) . To discriminate between these possibilities , we measured pathogen load 30 hours post-inoculation ( hpi ) , which corresponds to the meront stage of N . parisii development , before spores have formed ( see S1 Fig . for N . parisii life cycle ) . To measure pathogen load , we developed a quantitative PCR assay whereby levels of N . parisii small subunit ribosomal RNA are measured and normalized to levels of C . elegans small subunit ribosomal RNA as a control . Using this assay , we observed variation in pathogen load among strains ( Fig . 1B ) and found that most strains that survived longer had lower pathogen load ( S3 Fig . ) . These results demonstrate that there is natural variation in C . elegans resistance against infection , i . e . the ability of C . elegans to control levels of N . parisii pathogen load . Given the phenotypic extremes exhibited by N2 and HW , we further investigated the variation in pathogen resistance between these two strains . In the experiments described above , we found that HW was highly resistant to a strain of N . parisii that was isolated in the state of Hawaii ( strain ERTm5—See Materials and Methods ) . We next infected N2 and HW with a strain of N . parisii that was isolated in Paris , France ( strain ERTm1 ) to determine whether HW C . elegans were also more resistant to a strain of N . parisii isolated from a distant geographical location . Indeed , we found that HW also lived longer and was more resistant than N2 when infected with the N . parisii strain from France ( S4 Fig . ) . All subsequent experimentation was performed with the N . parisii strain from Hawaii . To confirm via a different assay that HW animals are more resistant to infection than N2 animals , we examined pathogen load in N2 and HW animals using a fluorescence in situ hybridization ( FISH ) assay with a fluorescent probe that targets the N . parisii small subunit rRNA . Consistent with the qPCR results ( Fig . 1B ) , we found that pathogen load 30 hpi was much lower in HW animals compared to N2 animals ( Fig . 2A–B ) . One potential reason for decreased pathogen load of HW animals is that they may simply feed less than N2 animals and thereby ingest a lower initial inoculum of N . parisii spores . To examine this possibility , we compared the feeding rate between N2 and HW at the L1 stage by inoculating animals with GFP-labeled E . coli together with N . parisii spores and measuring fluorescent accumulation in the intestinal lumens of individuals over time . These experiments revealed that HW L1 animals did not feed less than N2 L1 animals and in fact fed slightly more ( S5 Fig . ) . Thus , lower pathogen load in HW animals is not simply caused by a lower rate of feeding and a lower initial inoculum of pathogen . The experiments described above were performed with animals infected at the first larval stage of development , although previously we had described that N2 C . elegans are susceptible to infection by N . parisii at all four larval stages ( L1 through L4 ) , as well as the adult stage [18] . Interestingly , we found that the difference in pathogen load between N2 and HW was vastly reduced when animals were inoculated with N . parisii at the L2 stage , compared to animals that were inoculated at the L1 stage ( Fig . 2C–D ) . We quantified pathogen load by FISH staining and COPAS Biosort analysis of a population of animals 30 hpi at each of the four larval stages and found that the differences between N2 and HW were restricted to infections initiated at the L1 stage ( Fig . 2E ) . These results indicate that young HW animals are much more resistant to infection than young N2 animals , but this enhanced pathogen resistance of HW animals is rapidly lost with age . The pathogen resistance of HW animals could be caused by an inability of N . parisii to invade and establish an infection in these animals , or by the ability to limit or clear an infection once it has been established . The results from our feeding experiments with fluorescent E . coli indicated that HW animals receive a similar initial inoculum of pathogen in their intestinal lumens ( S5 Fig . ) , but it remained possible that the pathogen may be less able to invade and establish an infection inside intestinal cells of HW animals . To investigate this possibility , we analyzed intracellular infection at a very early stage . Previously , we had identified the earliest signs of N . parisii invasion and intracellular growth at 8 hpi [25] ( and see life cycle in S1 Fig . ) , and here we show that intracellular N . parisii parasite cells can be identified even earlier at 3 hpi , by visualization of small , mono-nucleate N . parisii ‘sporoplasms’ inside C . elegans intestinal cells ( Fig . 3A ) . These sporoplasms then develop into larger , multi-nucleate meronts by 20 hpi ( Fig . 3B ) . We quantified this infection and found that approximately 90% of animals in a population of either N2 or HW animals had at least one intracellular pathogen cell in their intestines ( Fig . 3C ) . To further quantify this initial invasion and infection , we counted the number of parasite cells per animal at 3 hpi and found that this number was slightly lower in HW animals ( Fig . 3D ) . The fact that a similar percentage of N2 or HW animals is infected at 3 hpi lends further support to the hypothesis that the variation in resistance is not caused by differences in the rate of pathogen exposure or invasion but rather by an enhanced resistance in HW animals that acts post-invasion to mediate clearance of infection . Next , we directly assessed whether HW C . elegans can clear an infection by comparing infection at different time points . Because it is necessary to fix and stain infected animals to conclusively identify pathogen cells , we cannot track a single parasite cell over time in the same animal . Instead , we analyzed animals sampled from the same infected population over time . In the previously described experiments analyzing infection at 30 hpi ( Figs . 1 and 2 ) , C . elegans animals were inoculated with infectious N . parisii spores and were continuously exposed to these spores throughout the course of the experiment . Under these conditions , all animals in a population will eventually become infected . To create conditions in which it may be possible to observe an animal clear an infection that has already been established , we developed a ‘pulsed-inoculation’ assay . Specifically , we took half of the animals from a population at 3 hpi that had been analyzed as described above and re-plated them in the absence of spores . We then harvested these animals at 20 hpi , fixed , FISH-stained to label pathogen cells and then determined the percentage of animals exhibiting infection , where 0% means no animals in the population had infection and 100% means that all animals in the population had at least one pathogen cell present . Strikingly , the percentage of HW animals that showed any evidence of infection was dramatically decreased from 90% at 3 hpi to only about 20% at 20 hpi , indicating that most animals that were infected at 3 hpi were able to clear the infection by 20 hpi ( Fig . 3C ) . By contrast , N2 animals did not show a lower percentage of animals infected at 20 hpi , indicating they were not able to clear infection . Furthermore , when animals were inoculated at the L4 stage , neither N2 nor HW animals were able to clear the infection ( Fig . 3C ) . Thus , it appears that young HW animals can clear an intracellular N . parisii infection from their intestinal epithelial cells , but they lose this ability before reaching a reproductive age . One potential driver of age-specific resistance could be variation in the selective pressure that is applied by infection at different ages . Thus , we investigated the relative fitness of N2 and HW animals exposed to pathogen at different ages , focusing first on survival as a measure of fitness . In our results described above , HW animals lived about 33% longer than N2 animals during infection ( Fig . 1A ) . In these experiments , animals were inoculated as L1 animals and then exposed to pathogen throughout their lifetimes . In order to more closely compare differential immunity to exposure at different ages , we performed the ‘pulsed-inoculation’ for three hours , removed animals from pathogen and then measured lifespan . With this ‘pulsed-inoculation’ introduced at the L1 stage , HW animals lived two and half times longer than N2 animals ( Fig . 4A ) . Strikingly , HW animals inoculated as L1 animals had relatively little decrease in survival compared to uninfected HW animals ( Fig . 4A ) . By contrast , N2 animals inoculated as L1 animals had dramatically decreased survival compared to uninfected N2 animals . Thus , the early life immunity of HW L1 animals was sufficient to nearly eliminate the negative impact of pathogen exposure on survival during this time . Interestingly , no significant difference in survival between N2 and HW animals was observed when pathogen inoculation occurred at the L4 stage . In this experiment , both N2 and HW animals died much more quickly than uninfected controls . To further investigate how age-specific resistance of HW animals may affect fitness , we investigated overall progeny production , which is a key driver of evolutionary success . We found that progeny production for both the N2 and HW strains was not significantly different in animals inoculated with pathogen at the L4 stage compared to animals that were not exposed to pathogen . However , inoculation at the L1 stage led to a significant reduction in lifetime fecundity . In particular , N2 had drastically fewer progeny , while HW had only slightly fewer progeny ( Fig . 4B ) . Thus , HW immunity at the L1 stage improves lifetime fecundity and is likely to improve evolutionary success . By contrast , resistance at the L4 stage does not appear to be important for evolutionary success , given that progeny number is not significantly reduced by pathogen inoculation at this stage . Both N2 and HW have reduced lifetime fecundity when infected at the L1 stage , but infected HW animals have significantly more progeny than infected N2 animals . We tested to see if this difference confers a competitive advantage to HW in an environment shared with N2 . L1 stage animals were inoculated with spores for three hours and then grown to the L4 stage in the absence of spores , followed by plating of equal numbers of N2 and HW animals on a shared plate . The population was then expanded to saturation and analyzed for the relative abundance of each C . elegans strain within the population . In uninfected populations the ratio of N2 to HW animals was 58% to 42% , respectively ( Fig . 4C ) . In the presence of pathogen , the ratio of N2 to HW animals shifted to 19% and 81% , respectively ( Fig . 4C ) . Taken together , these experiments demonstrate that the enhanced resistance of young HW animals confers a selective advantage over N2 animals in a laboratory setting . Having established phenotypic variation in resistance to microsporidia infection , we sought to characterize the underlying genetic variation . Several phenotypic differences between N2 and HW have previously been investigated , and the causative genes responsible for those differences have been identified [26–34] . In particular , a variant in the npr-1 gene , which encodes a neuropeptide Y-like G-protein-coupled receptor , is known to mediate several fitness-related differences between N2 and HW in a laboratory setting , including lifetime fecundity and avoidance of the human pathogen Pseudomonas aeruginosa [26 , 34] . To determine whether npr-1 is responsible for the differences in resistance to microsporidia , we measured pathogen load in N2 and HW strains harboring an introgressed npr-1 locus from the other strain and found no significant differences between the introgressed strains and the parental strains ( S6 Fig . ) . Furthermore , a deletion mutation for npr-1 in the N2 background had similar pathogen load as the N2 strain ( S6 Fig . ) . Thus , the npr-1 gene does not appear to be responsible for the enhanced resistance to microsporidia infection of HW animals compared to N2 animals . The increased resistance of HW animals to infection by N . parisii could be caused by the absence of a host factor important for N . parisii growth or by the presence of an increased host immune response . To distinguish between these two models , we examined whether the HW resistance phenotype was dominant or recessive to the N2 phenotype . We tested the F1 heterozygous progeny from a cross between N2 and HW for pathogen load by FISH and found that heterozygotes were as resistant as HW homozygotes ( S7 Fig . ) , indicating that resistance is dominant . Together with the data on clearance of infection , these results support the model that HW has an increased immune response to N . parisii infection compared to N2 . Next , we sought to identify the number and location of the genetic regions contributing to the variation in immunity between N2 and HW animals . We used quantitative genetic analyses to map the causal quantitative trait loci ( QTL ) by infecting 179 recombinant inbred advanced intercross lines ( RIAILs ) between the N2 and HW strains [35] and measuring pathogen load 30 hpi by qRT-PCR ( S1 Table ) . Pathogen load values for RIAILs varied continuously and were generally well bounded by the parental values ( S8 Fig . ) . Replicate data from the parents and all RIAILs indicated that the broad-sense heritability of resistance was 69% , signifying that much of the variation in resistance is caused by genetic factors . Single-marker regression revealed four loci on chromosomes II , III , and V that are associated with variation in resistance ( Fig . 5A and S2 Table ) . RIAILs bearing the HW allele at these loci had significantly lower pathogen loads than those carrying the N2 allele . We named these loci Resistant Against Microsporidia Infection ( rami ) : rami-1 , rami-2 , rami-3 , and rami-4 . Together , these four genetic loci account for 51% of the phenotypic variance . Thus , the rami QTL appear to explain the majority ( 51/69 = 74% ) of the N2-HW genetic variance . To confirm that the genetic loci identified by QTL analysis could influence pathogen resistance , we made and tested NILs , which bear either an interval from the N2 strain introgressed into the HW strain or an interval from the HW strain introgressed into the N2 strain . We investigated the rami-1 and rami-4 loci , which should account for about 15% and 12% of the phenotypic variance respectively ( S2 Table ) . We generated NILs for rami-1 and rami-4 where the N2 interval was introgressed in the HW strain and vice versa . We then infected these strains with N . parisii and quantified pathogen load by qRT-PCR . Compared to the N2 strain , NILs in the N2 background with the rami-1 or rami-4 locus from HW had on average a 44% or 36% reduction in pathogen load , respectively ( Fig . 5B ) . When both rami-1 and rami-4 from HW were present in the N2 background there was a 62% reduction in pathogen load compared to N2 . The opposite effect was seen for NILs that were made in the HW background with rami-1 or rami-4 from N2; i . e . , these animals were more susceptible than HW . Compared to the HW strain , NILs with rami-1 or rami-4 from N2 were 39% and 31% more susceptible , respectively , and 67% more susceptible when rami-1 and rami-4 were combined ( Fig . 5B ) . Additionally , we tested the rami-4 NIL in the N2 background for pathogen load with the FISH assay and found that with this assay as well , the rami-4 locus made N2 significantly more resistant to infection ( S9 Fig . ) . Altogether , our results indicate that the rami-1 and rami-4 loci both additively promote C . elegans resistance to N . parisii .
Our findings demonstrate that there is natural variation in C . elegans host defense against microsporidia infection . We used variation between the N2 and HW strains to characterize the phenotypic and genetic basis of resistance to N . parisii . Surprisingly , we found that intestinal epithelial cells can clear intracellular infection in the HW strain but only when infection occurs at a young age . We observed that infection has a large negative impact on progeny production if it occurs at a very young age but not at a later pre-reproductive age , delineating one potential evolutionary reason for the age-specific resistance we identified . We used RIAILs generated from crosses between the susceptible N2 strain and the resistant HW strain to identify four QTL that contribute to a complex genetic basis of resistance to a natural intracellular pathogen . Age-related decline in immune response has been widely observed among animals [15] , although our findings of loss of immune function at such an early , pre-reproductive stage are unusual . Most studies of immunosenescence focus on reproductive or post-reproductive animals . For example , a master regulator of immune defense in C . elegans is the p38 MAPK PMK-1 , which has been shown to functionally decline around day six of adulthood [16] , after reproduction has ended . In addition , the C . elegans JNK-like MAPK KGB-1 has a reversal in protective function beginning in adulthood [36] . Here , we made the surprising observation that the enhanced resistance of the HW strain to N . parisii infection is limited to very young animals , and that immunity to this pathogen declines well before animals have begun adulthood and production of progeny . Our analysis suggests that the absence of enhanced immunity in older , albeit pre-reproductive , HW animals may have been shaped by weakened selective pressure . Employing a strong immune response may have negative consequences , including metabolic costs and the potential for self-damage . In the absence of selective pressure imposed by infection on progeny production that we observed in older larvae , maintaining a robust immune response may be superfluous and costly to the evolutionary success of the individual . It is surprising that infection of older pre-reproductive animals led to sharp decreases in lifespan but not to significant decreases in production of progeny . Because older animals were not able to clear infection , perhaps resources at older age are reallocated from immunity to reproduction . Our data indicate a drastic decline in immune responses to pathogens that occur earlier than those results described in other studies . As wild C . elegans strains infected by microsporidia have been isolated from around the world [18 , 20] , it is likely that co-evolution has contributed to genetic diversity and natural variation in resistance . Researchers have isolated strains of C . elegans from six continents , and the genetic diversity among these strains was recently documented [24] . The N2 and HW strains are highly divergent from each other , and we found that they vary in resistance to N . parisii . The enhanced resistance of the HW strain may incur costs that make it less fit in the absence of infection . Our data on relative fitness support this idea , in that HW has a shorter lifespan than N2 in the absence of infection ( Fig . 4A ) . However , this difference may be explained by variation in the npr-1 gene [26] , while the difference we see in resistance to N . parisii cannot ( S6 Fig . ) . Regardless , variation between these two strains may not necessarily capture variation that is relevant to evolution in a natural setting due to adaptations that may have occurred in a laboratory setting . A case in point is the variation between N2 and HW in NPR-1-mediated behaviors , which were originally believed to be naturally derived but have since been convincingly shown to be due to a laboratory adaptation in N2 [32 , 35] . As discussed above , variation in NPR-1 between N2 and HW gives N2 a fitness advantage in standard laboratory conditions . This variation confounds our ability to assess the potential costs of immunity that may be part of the resistance of the HW strain . We tested four additional wild isolates that span the geographic and genetic range of strains characterized so far and found equal proportions of relative resistance and susceptibility . The data from this limited set of strains suggest that natural variation in resistance to N . parisii is an ecologically relevant trait . Genetic association studies with additional strains may identify the resistance alleles that are segregating in the global population . We found that increased survival upon N . parisii infection among different C . elegans strains generally correlated with increased pathogen resistance ( ability to control N . parisii pathogen load ) . However , the C . elegans strain JU778 survived infection as long as the JU258 strain despite having higher pathogen load 30 hpi , suggesting that both tolerance and resistance vary among wild strains ( S2 Fig . ) . Also supporting the variation in tolerance is the observation that the JU778 strain slightly outlived the N2 strain when infected but died faster in the absence of infection ( S1 Fig . ) . These observations indicate that the longevity advantage of the JU778 strain may be specific to the context of N . parisii infection . Although there may be variation among strains in their ability to tolerate N . parisii infection , we focused on variation in resistance . We found that the enhanced resistance of HW is mediated by an active clearance of infection from intestinal epithelial cells . To our knowledge , clearance of intracellular pathogens by intestinal epithelial cells has not previously been demonstrated in any animal host . The cell-intrinsic immune capabilities of epithelial cells are increasingly appreciated in mammals [37] . For example , autophagy in epithelial cells can limit invasion and dissemination of bacterial pathogens [38] . Microsporidia commonly infect intestinal epithelial cells in humans . Interestingly , studies of microsporidia infection in humans suggest that intestinal infections by microsporidia might be cleared by immunocompetent people [2 , 39] . It is known that the adaptive immune system is important for clearing microsporidia infections in humans , but it is attractive to speculate that human intestinal epithelial cells may also play a role in clearing infections , similar to C . elegans intestinal epithelial cells . Identifying the mechanisms of clearance in C . elegans may help elucidate the immune capacity of epithelial cells in general , which are the first line of defense against many microbial infections . Although previous studies in C . elegans found that variation in resistance to the human pathogen P . aeruginosa was a simple trait determined predominantly by a single gene [34] , we found that C . elegans resistance to N . parisii infection is a complex genetic trait . We mapped four loci that explain a large fraction of the genotypic variance and used NILs to directly confirm the effects of rami-1 and rami-4 . Immunity-related genes have undergone exceptional amounts of positive selection in humans and other organisms [40] . For example , genes encoding major histocompatibility locus ( MHC ) proteins , immune signaling proteins and antimicrobial peptides have increased in diversity over recent evolutionary time . Hundreds of genes fall within the rami loci , and one approach to identifying candidates for further study may be to screen for genes that display signatures of positive selection . For example , the ubiquitin-dependent proteasome adaptors encoded by F-box and MATH-BTB genes are among the most rapidly diversifying genes in the C . elegans genome and have unparalleled rates of birth-death evolution [41] . These genes are under strong positive selection in their substrate-binding domains but not in their Cullin-binding domains , suggesting that they have evolved to detect and degrade foreign proteins as an immune defense mechanism [41] . Ubiquitin-mediated proteolysis has been implicated in host-pathogen interactions in both plants and animals [42] and is an attractive hypothesis for how C . elegans might combat an intracellular invasion such as N . parisii infection . In support of this hypothesis , we recently found that components of the ubiquitin-proteasome system are upregulated during infection and that disrupting the ubiquitin-proteasome system or autophagy in C . elegans during N . parisii infection increases pathogen load in the N2 strain [43] . Further refinement of our infection assays , together with genetic and molecular analyses , should uncover the specific genetic polymorphisms that have evolved to produce enhanced epithelial resistance in the HW strain . Epithelial cells are critical sites of host interactions with pathogens , and we find that they can directly eliminate intracellular infection based on several genetic loci that are tailored to the success of propagating the species .
C . elegans strains were maintained on nematode growth media ( NGM ) seeded with E . coli OP50–1 ( which is a streptomycin-resistant OP50 strain ) as previously described [44] . For simplicity , this strain is referred to as OP50 throughout . To obtain starved and synchronized L1 larvae , gravid adults were bleached to isolate eggs , which then were allowed to hatch overnight at 20°C [45] . The C . elegans strains N2 , CB4856 ( HW ) , JU778 , JU258 , and ED3046 were obtained from the Caenorhabditis Genetics Center . ERT002 is derived from strain CPA24 , which was previously isolated from a compost pile in Franconville , France and was the original strain of C . elegans isolated with N . parisii ERTm1 infection [18 , 25] . Strain CPA24 was subsequently bleached to remove the infection and renamed ERT002 to conform to C . elegans nomenclature conventions . For mapping , we used a set of advanced intercross recombinant inbred lines generated previously with the N2 and CB4856 strains [35] . The N . parisii strain we used in all infection experiments except S4 Fig . was ERTm5 , a N . parisii strain isolated from JU2055 , a Caenorhabditis briggsae strain found in a rotting breadfruit sampled in early April 2011 by Christopher Nelson in Limahuli Gardens , Haena , Kauai ( Hawaii 22 . 219 North , -159 . 5763 West ) . Spores were prepared and quantified as previously described [46] . For survival measurements in the six C . elegans strains during infection ( Fig . 1 ) , synchronized L1 larvae were plated on 6 cm NGM plates seeded with OP50 and inoculated with 2 × 106 N . parisii spores at 25°C . At 48 hpi , 30 animals were transferred to 3 . 5 cm plates seeded with OP50 with three plates per experiment . Live animals were quantified every 24 hours and transferred to fresh plates . For survival in N2 and HW during infection and in the absence of infection ( Fig . 4 ) , 1200 synchronized L1 larvae were inoculated with 100 μl of a 10x concentrate of an overnight OP50 culture and 2 × 106 N . parisii spores on 6 cm NGM plates at 25°C for three hours . Animals were then washed several times to remove spores and re-plated with OP50 until 48 hpi at 20°C . Uninfected animals followed the same conditions in the absence of spores . L4-infected animals followed the same conditions but were infected for three hours at the L4 stage . 20 individuals from each condition were then plated on 3 . 5 cm NGM plates seeded with OP50 , incubated at 20°C , and transferred to fresh plates every 24 hours until death or progeny production stopped . Mortality was recorded every 24 hours . Data were analyzed in Prism 6 with the Log-rank ( Mantel-Cox ) test . Animals were infected in liquid culture or on solid media . For liquid culture infections , 2000 synchronized L1 larvae in 0 . 5 mL of M9 buffer were plated per well in a 24-well plate . 0 . 5 mL of M9 buffer containing OP50 and 2 × 106 N . parisii spores was then added to each well . Plates were incubated on a rocker at 25°C . For solid media infections , 1200 synchronized L1 larvae were inoculated with 100 μl of a 10x concentrate of an overnight OP50 culture and 2 × 106 N . parisii spores on 6 cm NGM plates . When plating , media was evenly distributed across the entire plate . Plates were then incubated at 25°C . For infections initiated at stages other than the L1 stage , animals were plated for 24 hours at 20°C before adding spores for the L2 stage , plated for 24 hours at 25°C before adding spores for the L3 stage or plated for 24 hours at 20°C followed by 24 hours at 15°C before adding spores for the L4 stage . Samples were fixed different times post-inoculation with Tri-Reagent ( Molecular Research Center ) to extract RNA or with acetone to stain by FISH . RNA was isolated by extraction with Tri-Reagent and bromochloropropane ( BCP ) ( Molecular Research Center ) . 250 ng of RNA from each sample was used to synthesize cDNA with the RETROscript kit ( Ambion ) . cDNA was quantified with iQ SYBR Green Supermix ( Bio-Rad ) on a CFx Connect Real-time PCR Detection System ( Bio-Rad ) . We measured pathogen load by measuring the relative abundance of an N . parisii rDNA transcript normalized to a C . elegans rDNA transcript with the following primer sets: Np_rDNAF1: aaaaggcaccaggttgattc , Np_rDNAR1: agctctctgacgcttccttc , Ce18S_F1: ttgcgtacggctcattagag , Ce18S_R1: agctccagtatttccgcagt . Primer efficiencies were measured , and fold difference was calculated using the Livak comparative Ct method ( 2-ΔΔCt ) . We used the MicroB probe conjugated to a red Cal Fluor 610 dye ( Biosearch Technologies ) to stain infected animals for an N . parsii ribosomal RNA small subunit sequence as previously described [18] . Pathogen load was measured with the COPAS Biosort ( Union Biometric ) or by microscopy . For analysis with the COPAS Biosort , greater than 600 animals per condition were measured for time of flight ( TOF , a measure of size ) and red fluorescence . Pathogen load per individual was determined by normalizing the red signal to TOF . For microscopy , samples were mounted on agarose pads with VECTASHIELD mounting medium containing DAPI ( Vector Labs ) and imaged using fluorescent microscopy on a Zeiss AxioImager M1 upright microscope with a 10x or 100x oil immersion objective equipped with an AxioCam digital camera and AxioVision software . Sporoplasms at 3 hpi were imaged by confocal microscopy acquired on a Zeiss LSM700 at 630x magnification using ZEN2010 software . For L1 experiments , 1200 synchronized L1 larvae were inoculated with 100 μl of a 10x concentrate of an overnight OP50 culture and 2 × 106 N . parisii spores on 6 cm NGM plates at 25°C for three hours . Animals were then washed several times to remove spores and half were fixed in acetone while the other half was re-plated with OP50 and incubated at 25°C until fixing 20 hpi . L4 experiments followed the same procedure , but infections were initiated at the L4 stage . Sample were stained by FISH and analyzed by microscopy . For the 3 hpi samples , 100 animals per condition were imaged at 1000x to count the number of infected individuals and the number of parasite cells per animal . For the 20 hpi samples , 100 animals were imaged at 100x to count the number of infected individuals . 1200 synchronized L1 larvae were inoculated with 100 μl of a 10x concentrate of an overnight OP50 culture and 2 × 106 N . parisii spores on 6 cm NGM plates at 25°C for three hours . Animals were then washed several times to remove spores and re-plated with OP50 until 48 hpi at 20°C . Uninfected animals followed the same conditions in the absence of spores . L4-infected animals followed the same conditions but were infected for three hours at the L4 stage . Twenty individuals from each condition were then plated on 3 . 5 cm NGM plates seeded with OP50 , incubated at 20°C , and transferred to fresh plates every 24 hours until death or progeny production stopped . Progeny per animal were counted every 24 hours following the L4 stage . After transferring to fresh plates , the source plates were incubated at 20°C for 24 hours to allow all eggs to hatch , then incubated at 15°C for 24 hours before counting . Data were analyzed in Prism 6 by one-way ANOVA and Tukey’s multiple comparison test . Infection was initiated as in the lifetime fecundity experiments . Once animals had reached the L4 stage , 15 N2 and 15 HW animals were added to 15 cm NGM plates seeded with 3 mL of a 10x concentrate of an overnight OP50 culture and incubated at 20°C . Animals were harvested once food was nearly depleted , which was approximately five days post-plating for uninfected populations and approximately seven days post-plating for infected populations . Each condition was repeated in triplicate per experiment over three total experiments . Genomic DNA was obtained by phenol-chloroform extraction . To determine the ratio of N2 to HW genomic DNA in the samples , we used qPCR to measure the relative abundance of a transcript in the zeel-1 locus ( deleted in HW ) normalized to a snb-1 transcript ( present in both ) with the following primer sets: zeel1_N2F1: gggcaattttcaaaagcaga , zeel1_N2R1: gttggtgtgctgaattttct , snb-F1: ccggataagaccatcttgacg , snb-R1: gacgacttcatcaacctgagc . Standard curves of measuring N2 and HW genomic DNA independently and at different known combined concentrations over several biological and technical replicates revealed that on average the observed ratio was 5% off from the expected ratio . For each condition , 2000 synchronized L1 larvae in 0 . 5 mL of M9 buffer were plated per well in a 24-well plate . 0 . 5 mL of M9 buffer containing unlabeled OP50 or GFP-labeled OP50 and 2 × 106 N . parisii spores was then added to each well . Plates were incubated on a rocker at 25°C . Animals were collected each hour for three hours post-plating and mounted on agarose pads for imaging using fluorescent microscopy at a constant exposure time on a Zeiss AxioImager M1 upright microscope with a 40x oil immersion objective equipped with an AxioCam digital camera and AxioVision software . The relative amount of GFP-labeled bacteria in the intestinal lumens of animals was quantified by outlining individual animals and calculating the mean fluorescent intensity with AxioVision software . For analyzing infection in F1 progeny of N2 and HW crosses , 50 L4 stage males were set up with 30 L4 stage hermaphrodites overnight . Gravid hermaphrodites were bleached on 3 . 5 cm plates to yield eggs that hatched in the absence of food to obtain synchronized F1 progeny . OP50 was added , and synchronized larvae were inoculated with 7 × 105 N . parisii spores and incubated at 25°C for 16 hours . Animals were fixed and stained by FISH , mounted on agarose pads , and imaged on a Zeiss AxioImager M1 upright microscope with a 10x objective equipped with an AxioCam digital camera and AxioVision software . 30 animals per condition were outlined and measured for mean fluorescent intensity in the red channel . 179 recombinant inbred advanced intercross lines ( RIAILs ) from a cross between the Bristol ( N2 ) and Hawaii ( CB4856 ) strain were phenotyped by isolating RNA from infected animals 30 hpi and measuring pathogen load by qRT-PCR ( see above ) . N2 and HW were phenotyped in parallel for each experiment , and pathogen load in the RIAILs was normalized to N2 . 21 RIAILs were phenotyped on solid media and 158 RIAILs were phenotyped in liquid media ( see above for setup , data shown in S1 Table ) . The normalized fraction of N . parisii DNA of each RIAIL and the respective genotype data [35] were entered into the R statistical programming environment and processed using the qtl package [47] . The phenotypic distribution of the RIAILs had a long right tail , so QTL were mapped using non-parametric marker regression . The 5% genome-wide significance threshold was calculated based on 10 , 000 permutations of the phenotype data [48] . The most significant marker was used as a covariate to identify additional QTL until no more significant QTL were detected . The total phenotypic variance explained was calculated by squaring the rank-sum correlation of genotype and phenotype for each QTL . Broad-sense heritability was calculated as the fraction of phenotypic variance explained by strain from fit of a linear mixed-model of repeat phenotypic measures of the parents and some recombinant strains [49] . The total variance explained by each QTL was divided by the broad-sense heritability to determine how much of the heritability is explained by each QTL . Confidence intervals were defined as the regions contained within a 1 . 5 LOD drop from the maximum LOD score . RIAILs were selected that contained N2 or CB4856 genomic regions spanning the QTL intervals for chromosome II or chromosome V . We backcrossed these regions to the appropriate parental strain at least 12 times for each line , genotyping at SNPs bounding the interval at each cross . To generate strain ERT246 jyIR1[CB4856 > N2] II , Qx228 males were crossed to N2 hermaphrodites and the F2’s that segregated CB4856 markers at SNPs corresponding to the physical locations 1 , 373 , 016 and 2 , 090 , 144 were selected and homozygosed . Male progeny homozygous for CB4856 markers were crossed to N2 hermaphrodites , which was repeated until the F12 generation . NILs were then genotyped at markers across the arms and centers of all autosomes to confirm that they were N2 outside of the interval . The same basic strategy was followed for generating the other three single NILs , with the chromosome V interval genotyped at physical locations 16 , 734 , 456 and 17 , 917 , 291: the Qx88 strain was used to generate strain ERT247 jyIR2[N2 > CB4856] II , the Qx217 strain was used to generate strain ERT248 jyIR3[CB4856 > N2] V , and the Qx239 strain was used to generate strain ERT249 jyIR4[N2 > CB4856] V . Double NILs bearing both QTL intervals from one parent in the reciprocal background were generated by crossing single NILs and genotyping at the bounding markers listed above for homozygotes in the F2 progeny . Double NIL strains are ERT250 and ERT251 . | Infectious diseases caused by microbes create some of the strongest forces in evolution , by killing their hosts , and impairing their ability to produce progeny . Microsporidia are very common microbes that cause disease in all animals , including roundworms , insects , fish and people . We investigated microsporidia infection in the roundworm C . elegans , and found that strains from diverse parts of the world have differing levels of resistance against infection . Interestingly , a C . elegans strain from Hawaii can clear infection but only during the earliest stage of life . This resistance appears to be evolutionarily important , because it is during this early stage of life when infection can greatly reduce the number of progeny produced by the host . Consistent with this idea , if the Hawaiian strain is infected when young , it will ultimately produce more progeny than a susceptible strain of C . elegans . We find that this early life resistance of Hawaiian animals is due to a combination of genetic regions , which together provide enhanced immunity against a natural pathogen , thus enabling this strain to have more offspring . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Wild C. Elegans Strain Has Enhanced Epithelial Immunity to a Natural Microsporidian Parasite |
TAF4b is a gonadal-enriched subunit of the general transcription factor TFIID that is implicated in promoting healthy ovarian aging and female fertility in mice and humans . To further explore the potential mechanism of TAF4b in promoting ovarian follicle development , we analyzed global gene expression at multiple time points in the human fetal ovary . This computational analysis revealed coordinate expression of human TAF4B and critical regulators and effectors of meiosis I including SYCP3 , YBX2 , STAG3 , and DAZL . To address the functional relevance of this analysis , we turned to the embryonic Taf4b-deficient mouse ovary where , for the first time , we demonstrate , severe deficits in prophase I progression as well as asynapsis in Taf4b-deficient oocytes . Accordingly , TAF4b occupies the proximal promoters of many essential meiosis and oogenesis regulators , including Stra8 , Dazl , Figla , and Nobox , and is required for their proper expression . These data reveal a novel TAF4b function in regulating a meiotic gene expression program in early mouse oogenesis , and support the existence of a highly conserved TAF4b-dependent gene regulatory network promoting early oocyte development in both mice and women .
One of the most fundamental aspects of germline regulation is meiotic progression , in which germ cell chromatin acquires a new configuration [1] , genetic material is exchanged between homologous chromosomes , and germ cells are ultimately reduced to half the genetic material of the parental cell [2] . In mice , oogonia enter into meiosis I at embryonic day 13 . 5 ( E13 . 5 ) after retinoic acid ( RA ) production activates the Stimulated by Retinoic Acid 8 ( Stra8 ) gene [3–6] . Oocytes progress through the stages of prophase I of meiosis I with the acquisition of meiotic double strand breaks in leptotene , assembly of the synaptonemal complex in zygotene , completion of synapsis by pachytene , and finally arrest in diplotene , after the resolution of meiotic recombination [7 , 8] . Meiotic onset also occurs in human oocytes within the fetal human ovary around gestational weeks 11–12 , after which diplotene arrest begins around 16–20 weeks [8–10] . Remarkably , these arrested oocytes remain in diplotene until the first meiotic division just prior to ovulation [7] , which will not occur until months later in mice and decades later in humans . In women , these oocytes may not be utilized for conception until four to five decades after their initial formation and arrest . Therefore , these early embryonic meiotic events must not only take place with great fidelity , but must ensure long-term genomic integrity to confer proper fertilization and development . Intricate regulation of gene expression is critical for both the meiotic program and the subsequent packaging of meiotically arrested oocytes into primordial follicles . A number of transcription factors are known to play crucial roles in these processes as well as in germ cell cyst breakdown including Factor in the Germline Alpha ( FIGLα ) [11 , 12] and Newborn Ovary Homeobox Gene ( NOBOX ) [13–15] . Another promising candidate for meiosis and ovarian reserve regulation is TAF4b , a gonadal-enriched subunit of the TFIID complex that is critical for female fertility in the mouse [16] . TFIID is a multi-protein general transcription factor complex composed of the TATA-box binding protein ( TBP ) and 14 TBP-associated factors ( TAFs ) . Taf4b-deficient female mice suffer from hallmarks of primary ovarian insufficiency ( POI ) including persistent estrous , elevated serum follicle stimulating hormone ( FSH ) [17] and accelerated primordial follicle depletion [18] . We have recently shown that TAF4b is required for proper germ cell cyst breakdown , primordial follicle assembly and oocyte survival during the window of meiotic progression in the perinatal mouse ovary [19] . In the context of human oocyte preservation , a report by the Dutch premature ovarian failure ( POF ) Consortium linked single nucleotide polymorphism ( SNP ) variation in the human TAF4B gene to POI [20] , while a report of human oocyte quality has reported TAF4B expression as a positive correlate of increased oocyte quality [21] . Even though the crucial developmental functions of TAF4b in the developing murine ovarian reserve have been established , the precise functions of TAF4b in the early oocyte and its potential mechanisms of oocyte-specific gene regulation remain poorly understood . To get a better understanding of TAF4B’s potential roles in human oocyte development , we utilized a data set profiling global gene expression in the human fetal ovary [22] . From our analysis , we found that human TAF4B expression is highly correlated with the expression of critical meiotic regulators including SYCP3 , Y Box Binding Protein 2 ( YBX2 , also known as MSY2 ) , and Deleted in Azoospermia-Like ( DAZL ) . Furthermore , to elucidate fundamental molecular mechanisms associated with healthy meiotic progression and ovarian reserve establishment , we have analyzed prophase I events in the context of Taf4b-deficient ovaries at E16 . 5 . Importantly , while Taf4b-deficient ovaries experience accelerated germ cell death immediately after birth , they possess normal germ cell densities during late embryogenesis , allowing us to compare these cell populations [19] . The present analysis of meiosis I in embryonic Taf4b-deficient ovaries revealed altered prophase I progression and abundant asynapsis as well as altered diplotene arrest in Taf4b-deficient oocytes . We have also observed deficits in the ability of Taf4b-deficient oocytes to perform meiotic homologous recombination . Finally , chromatin immunoprecipitation ( ChIP ) assays demonstrated direct occupancy of TAF4b at the proximal promoter regions of critical meiotic and oogenesis regulators including Stra8 , Dazl , Figla , and Nobox , placing TAF4b upstream of these essential regulators of oogenesis . Together , these data help explain the critical role of TAF4b in the establishment of the postnatal primordial follicle pool , and identify novel functions for TAF4b in the orchestration of early meiotic events .
To gain a better understanding of the potential molecular functions of TAF4B in human oogenesis , we examined coordinate TAF4B gene expression profiles in the human fetal ovary over gestational time [22] , reasoning that the most essential functions of TAF4B may be highly conserved between mice and humans . We identified the genes that are most correlated with TAF4B expression during human ovarian development ( S1 Table ) . To test if the list of genes highly correlated with TAF4B is enriched for specific functions , we evaluated the top 624 genes with Pearson correlations >0 . 85 for enriched pathways . Enrichment determined using Ingenuity Pathway Analysis ( IPA ) found that TAF4B expression is most highly correlated with the expression of a network of meiotic regulators and effectors during human fetal ovarian development ( Fig 1A ) . Pearson Coefficient values for a number of notable genes implicated in meiosis were calculated ( Fig 1B ) , including SYCP3 ( R2 = 0 . 87 , P-value = 0 . 004 ) , YBX2 ( R2 = 0 . 90 , P-value = 0 . 004 ) , DAZL ( R2 = 0 . 95 , P-value = 0 . 004 ) , the human ortholog of Dazl , MAEL ( R2 = 0 . 93 , P-value = 0 . 00398 ) , a piRNA-pathway regulating gene essential for prophase progression and oocyte survival [23] , and STAG3 ( R2 = 0 . 92 , P-value = 0 . 00393 ) , a cohesin required for proper synapsis [24–26] . Interestingly , genes involved in primordial follicle formation were also highly correlated including TEX14 ( R2 = 0 . 91 , P-value = 0 . 004 ) , a component of intercellular bridges in germ cell cysts [27 , 28] , DDX4 ( R2 = 0 . 92 , P-value = 0 . 004 ) , the human ortholog of Vasa [29 , 30] , and FMR1 ( R2 = 0 . 97 , P-value = 0 . 004 ) , an RNA-binding protein associated with premature ovarian failure [31 , 32] , among others . Together these data suggest that TAF4B may execute a highly conserved role in mammalian meiosis I regulation that is critical for healthy oocyte development and ovarian aging in women . As many genes coordinately expressed with human TAF4B in the human fetal ovary are critical for the fidelity of meiosis I , we analyzed prophase I progression in Taf4b-deficient mouse oocytes . Strikingly , when visualized by two different immunofluorescence methods , E16 . 5 Taf4b-deficient oocytes exhibit high degrees of asynapsis and aberrant meiosis . One means to test synapsis is by visualizing chromosomal co-localization of Synaptonemal Complex Proteins 1 and 3 , which coat the central and lateral elements , respectively [33] . Complete co-localization indicates faithful synapsis , as seen in wild-type oocytes ( Fig 2A–2D ) , while regions of asynapsis only stain positively for SYCP3 and lack SYCP1 , as observed in most Taf4b-deficient oocytes ( Fig 2E–2H ) . Another means of testing synapsis is by counting chromosomal centromeric foci marked by Centromere Protein A ( CENP-A ) staining [34] . Complete synapsis of XX pachytene chromosomes will result in the appearance of 20 centromeric foci , as seen in wild-type oocytes ( Fig 2I–2L ) , while regions of asynapsis will result in non-overlapped centromeres and the appearance of greater than 20 CENP-A foci as seen in most Taf4b-deficient oocytes ( Fig 2M–2P ) . Persistent double strand breaks are also evident in Taf4b-deficient oocytes as visualized by the presence of the histone variant γH2AX on pachytene chromosomes ( Fig 2Q and 2R ) . Notably , nearly 75% of Taf4b-deficient oocytes exhibit some degree of asynapsis during pachytene , a percentage significantly greater than that ever observed in wild-type oocytes ( Fig 2S , p < 0 . 05 ) . In addition to clear deficits in synapsis during pachytene , Taf4b-deficient oocytes also experience a prophase I progression defect . Meiotic stages of oocytes were determined by the configuration of SYCP3 staining at E16 . 5 ( S1 Fig ) . While nearly 90% of wild-type oocytes have reached pachytene by E16 . 5 , Taf4b-deficient oocytes persist in the earlier stages of leptotene and zygotene with only about 50% reaching pachytene at the same time ( Fig 2T ) . Moreover , using MSY2 as a marker of diplotene arrest at PND0 , we found that while about 40% of wild-type oocytes ( visualized by TRA98 staining ) have reached this developmental milestone , fewer than 10% of Taf4b-deficient oocytes have reached this arrest ( Fig 3 ) . Strikingly , just one day later , at PND1 , most Taf4b-deficient oocytes undergo apoptotic cell death [19] . Finally , as proper synapsis is known to initiate at sites of recombination , the fidelity of meiotic recombination was tested by visualizing MLH1 foci on pachytene chromosomes . While wild-type oocytes possess one or two strongly-stained foci per homologous pair , Taf4b-deficient oocytes lack many of these sites as well as an overall decreased intensity of staining ( Fig 4A ) . Quantification revealed a significant reduction of overall number of MLH1 foci on chromosomes from Taf4b-deficient oocytes ( p<0 . 0001 ) ( Fig 4B ) . Wild-type oocytes average about 23 foci per cell , while Taf4b-deficient oocytes average about 8 foci per cell with increased variability including many oocytes with no quantifiable foci . Given the loss of Taf4b-deficient oocytes just days after these phenotypes are observed , the data suggest a TAF4b-dependent link between the ability to correctly progress and arrest in prophase I during embryogenesis and postnatal oocyte survival . While TAF4b is evidently required for proper regulation of meiosis I progression , the nature of this regulation was unclear . To test if TAF4b directly occupies the proximal promoters of essential meiosis genes , we performed ChIP from wild-type E18 . 5 ovaries using a validated anti-TAF4b antibody ( S2 Fig ) or a negative control IgG antibody , and performed quantitative PCR . Strikingly , immunoprecipitated chromatin bound by TAF4b included the proximal promoters of Stra8 and Dazl ( Fig 5A ) . As oogenesis regulators Figlα and Nobox are known downstream targets of DAZL [35] , these promoters were also tested and found to be directly bound by TAF4b ( Fig 5A and 5B ) . TAF4b occupancy at these important loci is specific , as genomic regions not expected to be occupied by TAF4b , including a non-genic region 50kb upstream of Nobox were not enriched for TAF4b ( Fig 5B ) . Quantitative PCR results were validated by gel electrophoresis and visualization of amplified proximal promoters ( S3 Fig ) . To determine whether TAF4b occupancy at some of these promoters correlates with differences in their protein expression , E18 . 5 Taf4b-heterozygous and Taf4b-deficient ovaries were stained by immunohistochemistry for NOBOX protein . Although there are no statistically significant differences in germ cell density at this time point [19] , NOBOX expression is dramatically reduced in Taf4b-deficient oocytes ( Fig 6A and 6B ) . Additionally , PND0 Taf4b-heterozygous and Taf4b-deficient ovaries were stained by immunofluorescence for DAZL protein , which was also observed to be dramatically reduced in Taf4b-deficient oocytes ( S4A and S4B Fig ) , a result which was confirmed by immunoblotting ( S4C Fig ) . To better understand if meiotic onset may also be affected in Taf4b-deficient oocytes , mRNA expression of Stra8 was tested at E13 . 5 , the developmental time at which it is first activated by RA [3] . Notably , Stra8 expression in Taf4b-deficient ovaries is approximately 25% of that expressed in wild-type ovaries at the same time ( Fig 7 ) . Genes downstream of Stra8 , including Sycp1 and Sycp2 , exhibit similar reduced expression , suggesting that TAF4b may be important for meiotic onset as well as progression through direct transcriptional modulation of Stra8 . In contrast to these oocyte-specific genes , Wnt4 expression [36] is not significantly changed in Taf4b-deficient ovaries at this time , suggesting that TAF4b is specifically promoting oocyte-specific gene expression programs . Given that oocytes are not mitotic after E13 . 5 , and as we have previously demonstrated equivalent oocyte densities in Taf4b-deficient and wild-type ovaries at E18 . 5 [19] , these data likely reflect alterations in gene expression and not reduced germ cell numbers . Accordingly , MVH-stained E13 . 5 ovaries display relatively equivalent numbers of germ cells across Taf4b genotypes ( S5 Fig ) . Proper TAF4b regulation thus leads to the proper expression of downstream genes regulating multiple stages of meiosis I including onset , synapsis , recombination and arrest .
Recent work has converged on the mid- to late-embryonic period in the mouse ovary and mid-gestation development in the human ovary as one of the first critical regulatory periods ensuring the survival of high quality oocytes [4] . Fetal development represents the timeframe in which female germ cells are expanded by mitotic divisions and then dramatically reduced , ultimately producing the apparently finite ovarian reserve . This dynamic proliferation followed by meiotic arrest and significant germ cell apoptosis prior to assembly of primordial follicles coincides with prophase I progression , suggesting that this may represent a quality control mechanism in which only the highest quality oocytes are selected for long-term survival [37] . As such , the regulation of this window of time must not only be finely tuned , but must also result in highly stable oocytes that can maintain genomic integrity and gene expression throughout the reproductive lifespan . Just prior to the establishment of this finite reserve , faithful progression through prophase I of meiosis ensures genomic integrity by pairing homologous chromosomes , facilitating meiotic crossing over , and ultimately arresting oocytes in a highly stable chromosomal conformation that will persist until ovulation [4] . Disruption of these early meiotic events , as well as altered gene expression , are known causes of accelerated ovarian follicle depletion which can lead to premature reproductive senescence and associated infertility [38] . Here we demonstrate that TAF4b is required in the ovary to properly regulate expression of essential meiosis and oogenesis genes , as well as ensure faithful prophase I progression and recombination , which leads to the proper establishment of the ovarian reserve . These data support a novel role for TAF4b in not only promoting timely initiation of the meiotic program during embryonic development , but also directly occupying the promoters and modulating the expression of genes known to be essential regulators of oogenesis . ChIP assays revealed a role for TAF4b in directly occupying the promoters of a number of genes that are known “master regulators” of oogenesis including the transcription factors Figlα and Nobox . Remarkably , Figlα and Nobox mutant ovaries strongly phenocopy Taf4b-deficient ovaries with Figlα-deficient mice experiencing germ cell death immediately after birth [12] , and Nobox-deficient mice exhibiting cyst breakdown defects as well as primordial follicle loss [13 , 15] . Our studies support a role for TAF4b in regulating the proper expression of these genes , likely through direct transcriptional activation . Our studies suggest that meiotic errors may underlie the rapid perinatal oocyte loss observed in Taf4b-deficient mice . First , we have revealed differential mRNA and protein expression of meiotic effectors including Sycp1 , Sycp2 , and MSY2 in Taf4b-deficient oocytes . As these effectors all play essential meiotic roles [39–42] , we investigated meiotic progression in Taf4b-deficient oocytes and observed a lag in the timely progression between the steps of prophase I as well as incomplete synapsis and persistent γH2AX in a majority of Taf4b-deficient oocytes . In addition to these striking meiotic errors , the diplotene stage is only reached by a small percentage of Taf4b-deficient oocytes at PND0 , one day prior to the majority of their excessive attrition . Furthermore , Stra8 mRNA expression is approximately 4-fold reduced in E13 . 5 Taf4b-deficient ovaries compared to wild-type , indicating that TAF4b partially controls its expression . As meiotic onset and progression is likely very sensitive to Stra8 dosage , this diminished Stra8 expression may underlie the reduced expression of downstream synaptonemal complex protein-encoding genes and the observed meiotic deficits and asynapsis . Interestingly , low levels of Stra8 expression are evidently sufficient for some degree of meiotic entry . While we are currently unable to discriminate between a prophase I defect as a result of delay in meiotic onset versus other mechanistic errors , the reduction in Stra8 expression suggests that onset may be affected in Taf4b-deficient oocytes . Furthermore , while we cannot exclude altered meiotic onset , our data demonstrating reduced recombination in pachytene Taf4b-deficient oocytes suggests that equally-staged oocytes exhibit onset-independent meiotic phenotypes compared to wild-type oocytes . Therefore , we can conclude that in addition to TAF4b playing a role in the timely progression through prophase I , it is also required for other independent and essential meiotic events including homologous recombination . Interestingly , reduced expression of the RNA-binding protein DAZL may play a key role in the many ovarian phenotypes evident in Taf4b-deficient mice . First , DAZL has been shown to bind the 3’ UTR of Sycp3 transcripts , stabilizing the mRNA and allowing for proper translation and function [43] . Furthermore , Dazl-deficient mice also phenocopy Taf4b-deficient mice with accelerated germ cell loss around the time of birth . It has also been shown that Dazl-deficient mice fail to properly express both Figlα and Nobox [35] , suggesting that in addition to direct occupancy of Figlα and Nobox promoters , TAF4b may promote their expression and that of Sycp3 through direct regulation of Dazl . Finally , Dazl has recently been implicated in the regulation of Stra8 as well as downstream Stra8 targets , leading to proper meiotic progression [44] . Therefore , TAF4b-dependent regulation of Dazl may help amplify an oocyte-specific gene regulatory network essential for proper meiosis I events . Our work here agrees with previous research suggesting that meiotic fidelity during the mid-to-late embryonic period is essential for perinatal oocyte survival . For example , other models of meiotic failure , including Spo11-deficient mice [45 , 46] and Msh4-deficient mice [47] , demonstrate accelerated late embryonic or early postnatal oocyte loss . Indeed , in Taf4b-deficient mice as well , we have observed a rapid decline in oocyte density immediately after birth . We propose a model ( Fig 8 ) for TAF4b function in which TAF4b occupies the proximal promoters of a subset of essential meiosis- and oogenesis-regulating genes , including Stra8 , Figlα , Nobox , and Dazl , distinguishing TAF4b as a novel upstream “regulator of meiotic regulators” . Proper expression of these proteins can then lead to the faithful activation of downstream meiosis and oogenesis genes , including Sycp1/2/3 , Msy2 , and Figlα and Nobox target genes . Ultimately , we propose that TAF4b is required for a gene regulatory network essential for successful prophase I progression and for the establishment of a healthy primordial follicle reserve . In support of our findings , recent work from Cloutier et al 2015 demonstrates a role for proper meiotic progression and synapsis in perinatal oocyte survival , with asynapsis irrespective of chromosome identity resulting in diplonema oocyte loss [48] , as similarly observed in Taf4b-deficient mice . These authors suggest that oocyte death results from reduced expression of essential oogenesis genes positioned on asynapsed chromosomes due to persistence of γH2AX in pachytene , as seen in Taf4b-deficient oocytes . As we have demonstrated a role for TAF4b in the proper expression of a number of meiotic regulators including synaptonemal complex genes , our data suggests that reduced expression of these factors results in an inability to properly synapse , γH2AX-dependent meiotic silencing , and diplotene oocyte loss . These data further highlight the fine-tuning of transcription that must be achieved for healthy oogenesis and folliculogenesis . We propose that TAF4b regulates this process by two potentially independent mechanisms: first , directly occupying and promoting transcription of oogenesis genes , and second , playing a role in the timely and healthy progression through prophase I . Remarkably , human TAF4B may play an analogous role to mouse TAF4b in the establishment of a woman’s ovarian reserve while she is still developing in utero . As we have demonstrated coordinate regulation of human TAF4B and human SYCP3 , YBX2 , and DAZL expression ( along with that of many other meiosis genes ) , TAF4B may possess a conserved function in regulating meiotic progression and oocyte survival in both mice and women .
Wild-type ( Taf4b ( +/+ ) ) and Taf4b-deficient ( -/- ) or heterozygous ( +/- ) mice were generated by mating heterozygous Taf4b ( +/− ) male and female mice as described previously [16] . Offspring were genotyped by PCR analysis of tail-snip genomic DNA amplifying the region targeted by homologous recombination . All animal protocols were reviewed and approved by Brown University Institutional Animal Care and Use Committee and were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals ( # 1503000130 ) . Ovaries were removed , cleaned of excess fat , and fixed in 4% formaldehyde solution overnight before embedding in Optimal Cutting Temperature ( OCT ) Compound . Ovaries were serially sectioned at 8 μm on a Leica Cryostat onto glass slides and washed in 1x Phosphate-Buffered Saline ( PBS ) containing 0 . 1% Triton-X ( CalBiochem ) . The entire tissue was sectioned and the median , adjacent two sections chosen for staining and quantification . Tissue sections were then incubated in blocking buffer [3% Goat Serum ( Sigma ) , 1% Bovine Serum Albumin ( Sigma ) , and 0 . 5% Tween-20 ( Fisher Scientific ) in 1X PBS] and stained by incubation with primary antibodies against TRA98 and MSY2 ( Abcam ) for diplotene analysis , primary antibody against DAZL ( Abcam ) for expression analysis , or primary antibody against Mouse Vasa Homolog ( Abcam ) for E13 . 5 germ cell analysis . A secondary antibody-only control was included to compare background staining . Sections were further stained with DAPI to visualize nuclei and analyzed on an Epifluorescent Zeiss Axioplan microscope . Percentages of MSY2-positive oocytes were determined by counting cells positive for both TRA98 and MSY2 and dividing by the total number of TRA98-positive cells per section . Results were averaged and significance determined by two-tailed unpaired t-test . Error bars represent standard error of the mean . Chromosome spreads were prepared by the “drying down technique” as previously described [49] . Briefly , ovaries were collected from E16 . 5 mice and incubated in warmed PBS until their use . Ovaries were then incubated in hypotonic extraction buffer [30 mM Tris , 50 mM sucrose , 17 mM trisodium citrate dihydrate , 5 mM EDTA , 0 . 5 mM DTT , and 0 . 5 mM phenylmethylsulphonyl fluoride ( PMSF ) , pH 8 . 2] for 30 minutes , then teased apart in 100 mM sucrose . The ovarian single cell suspension was then pipetted onto slides wetted in 1% PFA with 0 . 2% Triton X-100 and allowed to settle overnight in a humid chamber at 37°C . The next day , slides were air-dried , incubated in 0 . 4% Photo-Flo ( Kodak ) for 2 minutes , air-dried again and then either stained or stored at -80°C . Spreads were stained by immunofluorescence as described above using primary antibodies against MLH1 ( BD Pharmigen ) , SYCP1 ( Abcam ) , CENP-A ( Abcam ) , γH2AX ( Millipore ) or SYCP3 ( Santa Cruz ) . To determine prophase I stage , SYCP3 configuration ( as shown in S1 Fig ) was used . To determine percent oocyte asynapsis , >20 CENP-A centromeric foci was used as a quantitative assessment . For both analyses , four animals yielding approximately 50 oocytes and 10–20 pachytene oocytes each were utilized . To quantify meiotic recombination , 25 or more pachytene oocytes per animal were scored for MLH1 foci on chromosome cores as visualized by SYCP3 staining . The N-terminal TAF4b amino acids 1–255 were fused to a 6xHis tag by PCR amplification and subcloned into the pETRB-1P expression vector ( a generous gift from Dr . Rebecca Page , Brown University ) . The 6xHis-TAF4b recombinant protein was expressed in E . coli BL21 ( DE3 ) Rosetta cells ( EMD Millipore , Billerica , MA ) and the cell pellet was resuspended in protein purification buffer ( 8M Urea , 100 mM NaCl , 20 mM HEPES pH 8 . 0 and Complete Protease Inhibitor Cocktail ( Roche , Indianapolis , IN ) ) . The cells were lysed by freeze-thawing at -80°C and 37°C 3 times , sonicated and insoluble material was removed by centrifugation . The 6xHis-TAF4b protein was purified from the soluble protein lysate using Ni-agarose affinity chromatography and elution with imidazole . Protein purification was assayed by SDS-PAGE and Coomassie staining ( S4A Fig ) . Recombinant 6xHis-TAF4b were concentrated to 1–2 mg/ml by dialysis , lyophilization and resolubilization in 6M Urea . Polyclonal antibodies against mouse TAF4b were then generated by immunizing rabbits and chickens ( Cocalico Biologicals Inc . , Reamstown , PA ) . Recombinant 6xHis-TAF4b protein was crosslinked to AminoLink Coupling Resin ( Life Technologies , Grand Island , NY ) and the polyclonal antibodies were affinity-purified from both the rabbit and chicken antisera ( S4 Fig ) . Antibody specificity was assayed by immunoblot analysis following antibody pre-incubation with recombinant 6xHis-TAF4b protein and immunoprecipitation capability was tested ( S4B–S4E Fig ) . ChIP from wild type E18 . 5 ovaries was performed on a pooled sample of ten ovaries from five embryos . Tissue was cross-linked using 1 . 5% formaldehyde for 20 minutes at room temperature , then quenched with 0 . 125 M glycine for 5 minutes . Tissue was then washed twice in ice-cold PBS and dounced into a single cell suspension in PBS with 1 mM PMSF and 1X Complete Mini Protease Inhibitor ( Roche ) . Dissociated tissue was then spun and resuspended in ChIP Lysis Buffer ( 1% SDS , 10 mM EDTA , and 50 mM Tris , pH 8 . 1 ) . Chromatin was sheared into 200-1000bp fragments using a Covaris sonicator . Resulting chromatin was spun , the supernatant saved , and used for the remainder of the procedure . Chromatin was pre-cleared using Protein A Agarose beads ( GE Healthcare ) in ChIP Dilution Buffer ( 0 . 01% SDS , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris-HCl pH 8 . 1 , and 167 mM NaCl ) , rotating for one hour . Immunoprecipitation was then performed using 20 μg of affinity-purified anti-rabbit TAF4b antibody or 20 μg of anti-IgG antibody ( Santa Cruz ) , rotating overnight . A third sample was rotated overnight with no primary antibody for a beads-only control . The next day , Protein A Agarose beads were added back to the sample , rotating for one hour before spinning and collecting the beads . Protein-DNA complexes were then washed in sequence with Low Salt Immune Complex Wash Buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris-HCl pH 8 . 1 , and 150 mM NaCl ) , High Salt Immune Complex Wash Buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris-HCl pH 8 . 1 , and 500 mM NaCl ) , LiCl Immune Complex Wash Buffer ( 0 . 25 M LiCL , 1% IGEPAL CA630 , 1% deoxycholic acid , 1 mM EDTA , and 10 mM Tris-HCl pH 8 . 1 ) , and TE Buffer ( 10 mM Tris-HCl pH 8 . 1 and 1 mM EDTA ) . Protein-DNA complexes were eluted from the beads by incubating in Elution Buffer ( 1% SDS and 100 mM NaHCO3 ) for 30 minutes and collecting the supernatant . DNA was purified by reversing crosslinks overnight at 65°C with the addition of 5 M NaCl , treatment with RNase A for 30 minutes at 37°C , and Proteinase K for 1 hour at 45°C . DNA was collected by binding to and eluting from Qiagen DNA-binding spin columns . Purified DNA was then analyzed by standard PCR amplification of specific regions of candidate TAF4b target promoters: Stra8 , Dazl , Figlα , Nobox , Sycp2 , Sycp3 , and Msy2 . Purified DNA was also analyzed by PCR amplifications of regions that are not considered candidate TAF4b target regions including a non-genic region 50 kb upstream of the Nobox promoter . Primer sets were designed to amplify 100–175 base pair-sized products ( S2 Table ) . The ABI 7900H Real-Time PCR system ( Applied Biosystems ) and the Power SYBR Green qPCR Master Mix with ROX ( Invitrogen , Carlsbad , CA ) were used for qPCR data acquisition . Data from each primer set were normalized to the E18 . 5 mouse ovary genomic DNA input levels and represented as a percentage of that DNA input . Each qPCR reaction was performed in triplicate and averaged . Error bars indicate the normalized standard deviation resulting from experimental triplicate qPCR reactions . Proximal promoter regions were identified using the GRCm38 . p3 Mus musculus genomic assembly and performing a chromosome coordinate sequence download from NCBI . Sequences 0 to 500 base pairs upstream of the NCBI reported gene contigs were called as proximal promoters . All sequences were then independently confirmed for genomic position using UCSC Genome Browser . Whole ovaries were collected from E13 . 5 Taf4b-wild-type and -deficient embryos and total RNA was extracted by Trizol extraction and lithium chloride precipitation . Total RNA from all experiments was quantified and checked for purity , and 50 ng was used to prepare 20 μl of cDNA with an iScript cDNA Synthesis Kit ( Bio-Rad ) . Real-time PCR was performed in technical triplicate using 1 μl of DNA template , 10 μl of ABI SYBR green PCR master-mix ( Applied Biosystems ) , and 0 . 5 μM custom oligos ( Invitrogen ) for Stra8 , Sycp1 , Sycp2 , Sycp3 , Wnt4 , or 18S rRNA in a 20 μl reaction in an ViiA 7 Real Time PCR machine ( Life Technologies ) . Data were analyzed by the ΔΔCt method , and relative expression levels were normalized to 18S rRNA . Error bars represent the standard deviation of the fold-change over wild-type of normalized gene expression values . P values were determined by two-tailed unpaired Student t-test . Primer sequences corresponding to genes of interest can be found in S3 Table . Gene expression from human ovaries was analyzed in the GEO dataset , GSE15431 . Pearson correlation coefficients and P-values were calculated in Microsoft Excel . Pathway enrichments were determined using Ingenuity Pathway Analysis ( IPA ) ( Qiagen ) . | Proper regulation of early oogenesis is essential for long-term ovarian health and fertility , as female mammals ( and women ) possess a finite pool of oocytes at birth . Meiotic progression during these early stages of oogenesis ensures genomic integrity and proper chromosome segregation in the reproductive years and decades to come . We investigated the role of transcription factor TAF4b in proper expression of meiosis genes and in the proper progression through prophase I . We have identified a novel function for TAF4b in promoting appropriate expression of critical meiosis genes including Stra8 , Sycp1 , Sycp2 , and Msy2 . Furthermore , we have demonstrated TAF4b occupancy at the proximal promoters of Figla , Nobox , and Dazl . This occupancy is crucial for the chromosomal events of prophase I , as Taf4b-deficent oocytes experience defects in meiosis I , a high incidence of asynapsis , and disrupted recombination . These data identify TAF4b as a novel upstream transcriptional regulator of the early meiotic program that is essential for healthy oogenesis . | [
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"rese... | 2016 | TAF4b Regulates Oocyte-Specific Genes Essential for Meiosis |
We evaluated the effect of Trypanosoma cruzi infection on fertility , gestation outcome , and maternal-fetal transmission in guinea pigs ( Cavia porcellus ) . Animals were infected with T . cruzi H4 strain ( TcI lineage ) before gestation ( IBG ) or during gestation ( IDG ) . Tissue and sera samples of dams and fetuses were obtained near parturition . All IBG and IDG dams were seropositive by two tests , and exhibited blood parasite load of 1 . 62±2 . 2 and 50 . 1±62 parasites/μl , respectively , by quantitative PCR . Histological evaluation showed muscle fiber degeneration and cellular necrosis in all infected dams . Parasite nests were not detected in infected dams by histology . However , qPCR analysis detected parasites-eq/g heart tissue of 153±104 . 7 and 169 . 3±129 . 4 in IBG and IDG dams , respectively . All fetuses of infected dams were positive for anti-parasite IgG antibodies and tissue parasites by qPCR , but presented a low level of tissue inflammatory infiltrate . Fetuses of IDG ( vs . IBG ) dams exhibited higher degree of muscle fiber degeneration and cellular necrosis in the heart and skeletal tissues . The placental tissue exhibited no inflammatory lesions and amastigote nests , yet parasites-eq/g of 381 . 2±34 . 3 and 79 . 2±84 . 9 were detected in IDG and IBG placentas , respectively . Fetal development was compromised , and evidenced by a decline in weight , crow-rump length , and abdominal width in both groups . T . cruzi TcI has a high capacity of congenital transmission even when it was inoculated at a very low dose before or during gestation . Tissue lesions , parasite load , and fetal under development provide evidence for high virulence of the parasite during pregnancy . Despite finding of high parasite burden by qPCR , placentas were protected from cellular damage . Our studies offer an experimental model to study the efficacy of vaccines and drugs against congenital transmission of T . cruzi . These results also call for T . cruzi screening in pregnant women and adequate follow up of the newborns in endemic areas .
American trypanosomiasis , also known as Chagas disease , is caused by a flagellate protozoan Trypanosoma cruzi ( T . cruzi ) . The clinical course of Chagas disease is divided into the acute and chronic phases . The acute infection is presented with blood parasitemia and is often mildly symptomatic . Infected individuals then evolve into chronic phase . While many remain in an indeterminate phase without any clinical symptoms , ~30% progress to develop clinically relevant Chagas disease . Chronic Chagasic Cardiomyopathy is a complex disease that includes a wide-spectrum of manifestations , ranging from minor myocardium involvement to left ventricular ( LV ) systolic dysfunction , dilated cardiomyopathy , arrhythmias , thromboembolic events , and terminal cardiac failure [1] . Gastrointestinal ( GI ) manifestations , such as mega-syndromes involving tubular structures of the GI tract , though not commonly recorded , are frequent in certain geographic areas [2] . The vectorial transmission of the parasite by hematophagous triatomines ( kissing bugs ) is the most commonly recognized route of infection in endemic areas [3] , though other routes of infection including transfusion of contaminated blood [4] and transplantation of infected organs [5] have also been noted . Congenital transmission ( CT ) of T . cruzi is also an important public health problem [6] . Though underestimated and underreported , recent estimates indicate that congenital transmission of T . cruzi occurs at a rate of 1–12% in endemic Latin American countries [7–9] . If left untreated , neonates may develop cardiac or enteric disease at delivery or weeks’ later [10] . Severity of the disease in infected children varies greatly from mild symptomatic cases to fatal cases [11 , 12] . Several factors can be involved in CT of T . cruzi and disease development; among them the genetic variability of the parasite may have considerable effect [13 , 14] . The CT of T . cruzi can occur at any time during gestation [15] , though some investigators have suggested that maximal likelihood of congenital infection occurs during the third trimester of gestation [10] when the placenta undergoes a series of physiological and metabolic transformations that favor the invasion of infectious agents [16 , 17] . Trophoblastic detachment [18] and apoptosis [19] with disorganization of basal lamina and collagen destruction [20 , 21] in placenta of woman with chronic Chagas disease is documented . Studies of CT of T . cruzi in rats and mice demonstrated the presence of parasite in the gravid uterus and amniotic fluid [10] . Guinea pigs ( Cavia porcellus ) are an important natural reservoir host for T . cruzi , and mimic reproducible and comparable human phases of acute infection and chronic disease [22 , 23] . Importantly , guinea pigs share several anatomical aspects with the placenta of humans since there is an extensive invasion of the trophoblast ( hemo-monocortical placentation ) [24 , 25] . Because of their longer time of gestation compared with murine models and a potentially higher surface of contact of the pathogen with the placenta , guinea pigs offer an excellent model for the study of congenital transmission of T . cruzi infection . In this study , our main goal was to establish the experimental guinea pig model of congenital transmission of T . cruzi . We also measured the effects of T . cruzi exposure at various stages of gestation on fertility , pregnancy outcome , and heart and colon pathology in mother and pups . We discuss the potential utility of guinea pig model in screening new drugs and vaccines against CT of T . cruzi .
All animal experiments were performed according to the National Institutes of Health Guide for Care and use of Experimental Animals , and approved by the bioethics committee at the Campus of Biological and Agricultural Sciences , Autonomous University of Yucatan ( No . CB-CCBA-M-2016-001 ) . The maintenance of the animals was performed under the current official Mexican standards ( NOM-062-ZOO-1999 ) , “Technical specifications for the production , care and use of laboratory animals . ” Female SPF guinea pigs ( Cavia porcellus ) weighing 650 ± 50 g ( 4–5 months old ) were purchased from The Mexican Center for Research and Advanced Studies of the National Polytechnic Institute ( CINVESTAV ) , and confirmed seronegative for T . cruzi by two tests before use in this study . Animals were kept in individual cages under controlled conditions of temperature , humidity and light cycles according to the requirements of the species , with food and water ad libitum . Pregnancy was diagnosed by day 25 post-mating using an ultrasound with a micro-convex 7 . 5 MHz transducer . T . cruzi H4 strain of TcI lineage [26] was propagated by in vitro passage in C2C12 cells . Three study groups were established with five female guinea pigs per group . Animals were inoculated with 100 trypomastigotes of H4 strain via intraperitoneal ( IP ) route . Female guinea pigs in Group A were infected , and 30 days later mated with males . Infection before gestation ( IBG ) was performed to study CT in indeterminate phase of infection . Animals in Group B were mated , and at mid-term of pregnancy ( i . e . , 30 ± 3 days of gestation ) , infected with T . cruzi . Infection during gestation ( IDG ) was performed to study the effect of acute infection during pregnancy on CT . Animals in Group C were mated but not infected , and used as a control group . Line diagram of infection and mating schedule is presented in Fig 1A . Animals from all groups were euthanized at the end of the pregnancy according to the official Mexican standards ( NOM-033-SAG/ZOO-2014 ) , “Methods for killing domestic and wild animals . ” The prolificacy ( total number of fetuses obtained by each female per gestation ) and the size and weight of fetuses were recorded . Likewise , macroscopic alterations in fetuses indicating the location and severity of damage were recorded . Fetal tissue sections ( heart , liver , skeletal muscle , and spleen ) were fixed in 10% buffered formalin for histology and stored at -20°C for DNA purification . Blood samples were taken from the dams and fetuses for isolating DNA or for collecting the serum , and all samples were stored at -20°C until analysis . Sera samples were monitored for T . cruzi-specific IgG antibody response by an enzyme-linked immunosorbent assay ( ELISA ) by using a Wiener Chagatest-ELISA recombinant v . 4 . 0 kit . The kit detects antibody response to six recombinant proteins that are expressed in T . cruzi . The assay was carried out following the manufacturer's recommendations , except that 2nd antibody was replaced with goat anti-guinea pig IgG conjugated with HRP ( sc2903 , Santa Cruz Biotechnology , Dallas TX ) . Briefly , 96-well plates were coated with recombinant proteins , and then sequentially incubated with 20-μl sera samples ( 1:100 dilution ) and HRP-conjugated guinea pig anti-IgG ( 1: 5000 dilution ) diluted in phosphate buffer ( 137 mM NaCl , 2 . 7 mM KCl , 4 . 3 mM Na2HPO4 , and 1 . 4 mM KH2PO4 , pH 7 . 4 ) . The color was developed with tetramethylbenzidine and hydrogen peroxide substrates , and reaction was stopped by acidification of the reaction medium . The optical density was recorded at 450 nm by using an xMark microplate absorbance spectrophotometer ( Bio-rad , Hercules , CA ) . The cut-off was determined from the mean value of the negative control sera samples ± 3 standard deviations . The sensitivity and specificity of the test was recorded at 99% and 98 . 3% , respectively . Western blotting ( WB ) and Indirect Immunofluorescence assay ( IFA ) have been previously described by us [27] . Briefly , epimastigotes of H4 strain parasites were washed in PBS , lysed with Laemmli sample buffer containing protease inhibitor cocktail ( Sigma , St . Louis , MO ) , and protein samples ( 20 μg ) were resolved on 10% polyacrylamide gels . Proteins were wet-transferred to nitrocellulose membranes , and membranes were blocked for 30 min with 1% non-fat milk in Tris-Buffered Saline—Tween 20 ( TBST; 10 mM Tris HCl , 150 mM NaCl , 5 mM Tween 20 , pH 8 . 0 ) , and incubated overnight at 4°C with polyclonal serum from infected and non-infected animals ( 1:100 dilution in TBST-1% non-fat milk ) . Membranes were then incubated for 1 h with alkaline phosphatase—conjugated secondary antibody and color was developed with nitro-blue tetrazolium chloride and 5-bromo-4-chloro-3’-indolyl phosphate p-toluidine salt ( NBT and BCIP , respectively ) . All sera samples were analyzed in triplicate , and a serum sample was considered positive when it recognized at least five antigenic bands; the results were considered indeterminate when the sample recognized one to four antigenic bands , and negative when the serum sample showed no reactivity . For IFA , H4 epimastigotes were suspended in PBS ( 5x106 parasites/mL ) , and added to 8-well glass slides ( 10 μl/per well ) . Slides were air-dried for 2 h , blocked for 30 minutes with PBS-5% horse serum ( PBS-HS ) , and incubated for 45 min sera samples from guinea pigs ( 1:32 to 1:2048 in PBS-HS ) . Slides were then incubated for 45 min with FITC-conjugated secondary antibody ( 1:80 dilution in PBS-HS , from Sigma ) and 0 . 01% Evan’s blue . Finally , slides were covered with Vectashield ( Vector Laboratories , Burlingame , CA ) mounting medium and microscopically examined using a Nikon TE300 ( Nikon , Tokyo , Japan ) microscope . Sera samples exhibiting high anti-IgG antibodies titer by IFA at 1: 32 dilution ( or more ) were considered positive . The DNeasy Blood and Tissue Kit ( 69504 , Qiagen , Germantown MD ) was used to isolate the genomic DNA from the blood and tissue samples by following the manufacturers’ instructions . Total DNA was examined for quality ( OD260/OD280 ratio of 1 . 7–2 . 0 ) and quantity ( [OD260 –OD320] x 50-μg/ml ) by using a DU 800 UV/visible spectrophotometer . All samples were evaluated by quantitative PCR ( qPCR ) for glyceraldehyde-3-phosphate dehydrogenase gene ( GAPDH ) to document the absence of any kind of inhibitors in the reaction mix and to establish equal amounts of DNA samples are used for all reactions as described . The parasite load in all samples was measured by using a SYBR Green-based quantitative PCR as described [28] . Briefly , total DNA ( 25 ng ) was used as template with 0 . 5 μM of T . cruzi nuclear satellite DNA-specific oligonucleotides ( TCZ-F 5'-GATCTTGCCCACAMGGGTGC-3' and TCZ-R 5'-CAAAGCAGCGGATAGTTCAGG-3' ) and SsoAdvanced Universal SYBR Green Supermix ( 172–5271 , Bio-Rad ) in a final volume of 20 μl . The cycling parameters were one step of 15 min of denaturation at 95°C; 50 cycles of PCR amplification ( 95°C for 10 s , 55°C for 15 s and 72°C for 10 s ) . Fluorescence data collection was performed at 72°C at the end of each cycle . After quantification , a melt curve was made with 74–85°C rising by 0 . 5°C each step and waiting for 4 seconds afterwards acquiring data on Green channel . Melting temperature ( Tm ) of the amplicon was 81°C . Finally , data were analyzed with CFX Manager Software V 2 . 1 . Standard curve was prepared by using epimastigotes of T . cruzi DTU VI ( CL Brener ) , as described [29] . Briefly , 200 μl of blood from a seronegative C . porcellus was spiked with 107 parasites/ml , and total DNA was extracted . Ten-fold dilutions of the extracted DNA , corresponding to 106–0 . 1 parasite/ml , were used in qPCR , as above . Likewise , different standard curves were prepared for evaluation of parasites in placental , heart , skeletal muscle , and spleen tissues . Total DNA extracted from blood and tissues of uninfected guinea pigs were used as negative controls . All samples were tested in triplicate , and parasite standard curve , negative controls , and no-template DNA control were included in all qPCR experiments . The data are presented as parasite equivalent/μL blood or parasite equivalent/g tissue , respectively . Heart tissue sections of female guinea pigs and fetal tissue samples ( utero-placental unit , heart and skeletal muscle ) were fixed in 10% buffered formalin for 24 hours , dehydrated in absolute ethanol , cleared in xylene , and embedded in paraffin . Five-micron tissue sections were mounted on glass slides , deparaffinized , and subjected to Hematoxylin & Eosin staining . In general , we analyzed each tissue-section for randomly selected , 10-microscopic fields ( 100X magnification ) , and examined three different tissue sections/organ/animal . The tissue sections were evaluated by two individuals in a blinded manner . The presence of inflammatory cells in H&E stained sections was scored as 0 ( absent ) , 1 ( focal or mild , 0–1 foci ) , 2 ( moderate , ≥2 foci ) , 3 ( extensive inflammatory foci , minimal necrosis , and retention of tissue integrity ) , and 4 ( diffused inflammation with severe tissue necrosis , interstitial edema , and loss of integrity ) [30] . Inflammatory infiltrates were characterized as diffused or focal depending upon how closely the inflammatory cells were associated . Tissue ( muscle ) degeneration was qualified based on the presence of swelling , fragmentation and rupture of fibers , loss of striation , and/or vacuolation . All experiments were conducted at least twice ( n = 5 guinea pigs/group/experiment ) , and all samples were analyzed in triplicate . Data are expressed as mean ± standard deviation ( SD ) . Comparisons of means between and within groups were performed using the Student's t test and ANOVA , considering a significance of 95% ( i . e . , p <0 . 05 ) . The CT was considered when amastigote nests or T . cruzi DNA were detected in any of the fetal tissues and histological lesions were observed .
All dams in IBG and IDG groups were seropositive for T . cruzi-specific antibodies at the end of the study . The sera levels of IgG antibodies , monitored by an ELISA ( Fig 1B ) and Western blotting ( Fig 1C ) , were similar in dams of both infected groups . Infection by T . cruzi was also confirmed by qPCR detection of parasites in blood and heart tissue of the dams . Dams in IBG group exhibited low but detectable blood parasites , and a high variation in the blood parasitic load was observed in dams of the IDG group ( Fig 1D ) . Yet , dams of IDG group exhibited >40-fold higher level of blood parasitemia that was statistically significant ( p<0 . 05 ) when compared to that detected in IBG dams ( Fig 1D ) . Heart tissue parasites were detected at similar level in all infected dams ( Fig 1E ) . Histological evaluation of the heart tissue of dams from the IBG and IDG groups showed inflammatory infiltrate with necrosis of the cardiac myocytes ( Fig 2B & 2C ) . The extent of tissue necrosis was slightly more severe in the IDG group ( Table 1 ) where cytoplasmic vacuoles , indicative of hypoxic tissue degeneration , were also noticed . Fiber degeneration evidenced by disorganized/fragmented striations was also seen in heart tissue of infected dams in the IBG and IDG groups ( Table 1 ) . No inflammatory infiltrate , necrosis or muscle degeneration was noted in the heart tissue of control dams ( Fig 2A ) . Together the results presented in Figs 1 and 2 suggest that a ) T . cruzi H4 isolate is virulent , and it replicates and disseminates to tissues in guinea pigs , b ) acute parasitemia is presented by IDG dams , and c ) indeterminate phase of infection is presented in IBG guinea pigs . Further , tissue parasite burden resulted in infiltration of inflammatory infiltrate and heart tissue necrosis in all infected dams . Several mating attempts were needed to get the dams in infected groups ( IBG and IDG ) pregnant , while all control females became pregnant in first attempt . Once pregnant , no statistically significant differences were observed in the number of fetuses carried by females in different groups . The average number of fetuses carried by IBG and IDG females were 2 . 8 ± 0 . 8 and 3 . 0 ± 0 . 0 respectively , and comparable to that noted in control group ( Fig 3A ) . However , significant differences were observed in the overall growth of the fetuses of the IBG and IDG females when compared to the control group . Fetuses carried by IBG ( vs . control ) females were 26% , 14% , and 15% smaller in weight , length , and width , respectively ( all , p<0 . 05 ) , while fetuses carried by IDG ( vs . control ) females were 11% and 15% lesser in weight and width ( p<0 . 05 ) , and 13% lower in length ( p>0 . 05 ) ( Fig 3B–3D ) . These results suggest that T . cruzi infection of dams before or during gestation results in decreased frequency of pregnancy and decreased growth of the fetuses . Next , we determined if differences in fetal size correlates with congenital transmission of T . cruzi . All sampled fetuses were seropositive for T . cruzi-specific IgG antibodies by an ELISA ( Fig 4A ) and IFA ( Fig 4B ) . A quantitative measure of tissue parasite load in fetuses was obtained by qPCR ( Fig 4C ) . The qPCR data showed that all fetuses carried by dams in IBG and IDG groups were infected . Tissue parasite load in fetal tissues of IBG and IDG groups was observed in the order of skeletal muscle > heart > spleen > liver . Parasite load in fetal heart , spleen and skeletal tissue did not show statistical differences between IBG or IDG groups . However , placental parasite burden in fetuses of IDG dams was 4-fold higher than that observed in placental tissue of IBG dams ( Fig 4C ) . Together , these results suggest that maternal-fetal transmission of T . cruzi occurred at high rate in guinea pigs exposed to the parasite before or during gestation . Exposure during gestation poses higher risk of congenital transmission because of high parasitemia in placental tissue . Finally , fetal tissues were evaluated by histology for T . cruzi induced inflammation and tissue damage . Representative images of H&E stained fetal tissue sections ( heart , skeletal muscle and placenta ) are presented in Fig 5 . Frequency and severity of lesions in different tissues of fetuses from both infected groups compared with the control group is presented in Table 1 . No amastigote nests were detectable by histological analysis of tissue sections ( heart , placenta and skeletal muscle ) of fetuses carried by dams in IBG and IDG groups . The heart of fetuses from the IBG group showed moderate inflammatory infiltrate with cellular necrosis in 25–58% of infected fetuses , whilst in the IDG group more severe lesions were detected in 16 . 6 to 50% of infected fetuses ( score 1 . 5±0 . 7 ) , and characterized by multifocal necrosis of myocytes and degeneration seen as multiple intra-cytoplasmic vacuoles ( Fig 5B & 5C , Table 1 ) . The skeletal muscle of fetuses from both IBG and IDG groups showed infrequent interstitial edema with lymphocyte inflammatory infiltrate scored as 1 ( Fig 5E & 5F , Table 1 ) . No pathological changes were noted in the placenta of the dams in control as well as infected groups ( Fig 5G–5I ) . Only structural changes of the placentas , such as dystrophic calcification , that are commonly observed in normal advanced gestations , were seen in all groups . These results suggest that all fetuses carried by infected dams are at risk of T . cruzi induced tissue damage . The extent of fetal tissue damage is more pronounced when the parasite exposure occurs during pregnancy and results in increased parasitemic status . The placental tissues , despite the presence of high degree of parasite burden , are protected from infection induced cellular damage .
Results of the present study demonstrate the high capacity of congenital transmission of T . cruzi strain H4 ( DTU I ) in a guinea pig model even when using a low dose of inoculum . Guinea pigs , like humans , have a deep invasion of the trophoblast into the maternal decidua , which is limited in most other rodent models [31] , and guinea pigs mimic the human pregnancy by the progesterone profile; progesterone is produced in the placenta by the end of gestation that does not decline at term [32] contrary to what happens in most rodents and other mammals . Further , the long gestation period of 72 days provides an opportunity to study the effect of parasite replication in dams during gestation on fetal development . Thus , guinea pigs offer the best available model for studying the human congenital infection . Cencig et al , [33] observed a very low rate of congenital infection , occurring in approximately 4% of living pups born to acutely infected mice . In another study , Wistar rats acutely infected with T . cruzi ( DTU II ) also exhibited a very low rate of parasite congenital transmission though a substantial decline in weight and an increase in acute myocarditis was noted in the pups that were congenitally infected [34] . Mjihdi et al , [35] observed infertility and fetal death in mice infected with a T . cruzi DTU I strain . The authors noted a massive invasion of the decidua , ischemic necrosis of placental tissue , poor fetal growth , and significant fetal losses in mice; however , no evidence of congenital transmission in fetuses was noted . Likewise , Badra [36] , Sala [37] and coworkers , evaluating the effect of T . cruzi strains in pregnant mice , observed a decline in fetal weight and length , placental weight and umbilical cord length , but no or low frequency of infection of the fetuses . Altogether , these studies indicate that fetal loss and a decline in fetal growth in mice occurs due to placental infection and infection induced pathologies in the placental tissue . Yet , mice did not provide a true indication of congenital infection of pups . Our guinea pig model captured all the benefits that can be useful in studying the efficacy of therapies against congenital transmission . One , all guinea pig dams , irrespective of their exposure to infection before or during gestation , carried fetuses that were congenitally infected by T . cruzi , and these fetuses also exhibited growth retardation and histological lesions in placental , cardiac and skeletal tissues . Two , with inoculation of low dose of parasite , all dams still became infected , and a decline in number of fetuses carried by infected ( vs . normal ) dams , still-birth , or fetal reabsorption was not observed at a significant level . Thus , it was feasible to obtain power for analyzing the effect of congenital infection in small number of guinea pig dams . Three , all fetuses of infected dams were serologically positive indicating the trans-placental transfer of maternal antibodies and functionality and transfer capacity of the placenta in guinea pigs , as is reported in human newborns of infected mothers [38] . Thus , we surmise that guinea pigs provide a true model of maternal-fetal transmission of T . cruzi and Chagas disease , and also offer opportunity for efficacy testing of therapies to prevent fetal infection . There is a high correlation between fetal heart rate in early pregnancy and the crown-rump length of the human neonates [39] . When first trimester embryos develop a low heart rate , there is a dismal prognosis of pregnancy resulting from embryo underdevelopment [40] . Alterations in the blood flow due to reduced heart rate and cardiac output is shown to affect the growth and development of chicken embryos [41] . Trypanosoma cruzi ( DTU TcI ) is able to produce high parasitemia rates and because of their high tropism to cardiac tissue during the acute and chronic phases of the disease can lead to myocarditis , lymphocyte and monocytes infiltrations and production of amastigote nests [42] . In the present study a high tropism towards cardiac tissue of the dams and damage of the cardiac tissue of fetuses was seen where the parasites probably replicated leading to impaired function and affecting the embryo viability and growth . Placenta is the key fetal organ responsible for protecting the fetus from any infectious pathogen [15] . Congenital transmission of T . cruzi is suggested to occur when the phagocytic capacity of the placenta is compromised [43] and a significant amount of blood parasites are present in the infected women [44] . Others have suggested that the maternal-fetal transmission of T . cruzi occurs when parasites cross the chorionic plate at the marginal sinus where the trophoblastic covering is incomplete . This is considered as a “sensible zone” where infected fibroblasts and macrophages can potentially transport trypomastigotes to a fetus . Reported lesions in the placenta induced by T . cruzi include inflammatory processes of the chorion with infiltration of polymorphonuclear immune cells and umbilical edema . However , lesions in the placenta are discrete , not very specific and with a low number of parasites so the placental examination is not a good diagnostic tool for the maternal-fetal transmission of Chagas disease [45] . In our model , we did not detect evidence of placental damage in the infected dams , and we also did not see a strong correlation between the qPCR detection of parasites in blood or placental tissue to transmission of parasite to fetuses . Thus , the route of transmission to the embryos is not clearly established . We suspect that due to the low dose of the parasite used , the placental response and rearrangement was not produced , and hematogenous trans-placental transmission occurred silently . Another probable route of fetal infection is through the invasion of marginal zones of the placenta , which is devoid of trophoblasts , and thus allow the passage of parasites especially during a moderate parasitemia . A trans-uterine route of transmission has also been suggested [15] , but it is less probable and difficult to probe . Parasite is primarily detected in the heart during the chronic phase of the disease . However , tropism of T . cruzi is not exclusive to cardiac tissue [46] . Other organs such as kidney , lung , pancreas , gastrointestinal tissue , skeletal muscle , spleen , and liver etc . are normally infected during experimental acute infection in mouse models [47] . Our finding of T . cruzi in multiple fetal organs suggests that pups develop an acute-like infection after exposure to the parasite from mother . It is likely that parasite replication occurs in the placenta , and when infected placental cells release the trypomastigotes , the parasite reaches fetus through the trophoblast or aspiration of amniotic fluid by the fetuses , and then spreads by active penetration through skin , mucosal membranes or organ-to-organ dispersion [48 , 49] . The degree of invasion to fetal tissues and disease severity may also depend on the virulence and dose of the strain of T . cruzi involved [50 , 51] . In the present study , a very low dose ( 100 parasites ) of a high virulent strain of T . cruzi was able to invade diverse tissues of the fetuses during the congenital transmission . In summary , we have demonstrated a guinea pig model of T . cruzi infection and maternal-fetal transmission . Even very low dose of T . cruzi DTU I was sufficient to establish high rate of congenital transmission to fetuses . The frequency of transmission to fetuses was high irrespective of whether the mother was exposed to infection before or during gestation , though the extent of fetal tissue damage tended to be higher when dams were exposed to parasite during gestation . Guinea pigs share several anatomical aspects with the human placenta and have a longer gestation than other rodent models , and thus offer an excellent model for the study of congenital transmission of the diverse linages of T . cruzi . Our findings also call for T . cruzi screening in pregnant women and adequate follow up of the newborns in endemic areas . | Chagas disease is endemic in Latin America where the vector , hematophagous triatomine , is widely distributed . The exposure of women to T . cruzi may result in birth defects . However , congenital transmission of Trypanosoma cruzi from infected mothers to fetuses is scantily studied . Herein , we have developed a guinea pig model of congenital transmission of T . cruzi and evaluated the pathology in fetuses born to mothers that were exposed to T . cruzi before or during gestation . We also show high virulence of a T . cruzi DTU I strain and its capacity to invade fetal tissues even at a very low dose . | [
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... | 2018 | Quantitative and histological assessment of maternal-fetal transmission of Trypanosoma cruzi in guinea pigs: An experimental model of congenital Chagas disease |
Oomycete pathogens cause diverse plant diseases . To successfully colonize their hosts , they deliver a suite of effector proteins that can attenuate plant defenses . In the oomycete downy mildews , effectors carry a signal peptide and an RxLR motif . Hyaloperonospora arabidopsidis ( Hpa ) causes downy mildew on the model plant Arabidopsis thaliana ( Arabidopsis ) . We investigated if candidate effectors predicted in the genome sequence of Hpa isolate Emoy2 ( HaRxLs ) were able to manipulate host defenses in different Arabidopsis accessions . We developed a rapid and sensitive screening method to test HaRxLs by delivering them via the bacterial type-three secretion system ( TTSS ) of Pseudomonas syringae pv tomato DC3000-LUX ( Pst-LUX ) and assessing changes in Pst-LUX growth in planta on 12 Arabidopsis accessions . The majority ( ∼70% ) of the 64 candidates tested positively contributed to Pst-LUX growth on more than one accession indicating that Hpa virulence likely involves multiple effectors with weak accession-specific effects . Further screening with a Pst mutant ( ΔCEL ) showed that HaRxLs that allow enhanced Pst-LUX growth usually suppress callose deposition , a hallmark of pathogen-associated molecular pattern ( PAMP ) -triggered immunity ( PTI ) . We found that HaRxLs are rarely strong avirulence determinants . Although some decreased Pst-LUX growth in particular accessions , none activated macroscopic cell death . Fewer HaRxLs conferred enhanced Pst growth on turnip , a non-host for Hpa , while several reduced it , consistent with the idea that turnip's non-host resistance against Hpa could involve a combination of recognized HaRxLs and ineffective HaRxLs . We verified our results by constitutively expressing in Arabidopsis a sub-set of HaRxLs . Several transgenic lines showed increased susceptibility to Hpa and attenuation of Arabidopsis PTI responses , confirming the HaRxLs' role in Hpa virulence . This study shows TTSS screening system provides a useful tool to test whether candidate effectors from eukaryotic pathogens can suppress/trigger plant defense mechanisms and to rank their effectiveness prior to subsequent mechanistic investigation .
Plants face constant attacks by a wide array of microorganisms including bacteria , fungi and oomycetes . Obligate biotrophic pathogens are particularly interesting because they can effectively evade or suppress host recognition , thus thwarting host defenses and enabling pathogen growth and reproduction [1] . In natural environments , plant disease is rare because plants activate a multilayered defense to most potential pathogens [2] . Relatively conserved molecules , called pathogen ( or microbe ) -associated molecular patterns ( PAMPs ) , are recognized by the plants via pattern recognition receptor proteins ( PRRs ) [3] , [4] . This interaction results in pattern-triggered immunity ( PTI ) . Successful pathogens target effector proteins to the host cell cytoplasm to suppress PTI [5] . To counteract this , plants have evolved a second line of defense comprising resistance ( R ) proteins that recognize particular effectors either directly or through their activities on plant targets . This recognition leads to effector-triggered immunity ( ETI ) [2] , [5] . It has been proposed that the “effector repertoire” of a given pathogen specifies its ability to infect a given host genotype [6] , [7] , [8] . Recent publications report many effector candidates predicted in the genomes of filamentous obligate biotrophs [9] , [10] , [11] . Comparison of effector sets of phylogenetically related species of obligate biotrophs that grow on different hosts reveals little overlap , suggesting host species-specific adaptation [10] . However , there are few studies about the functionality of obligate biotroph effectors on their hosts [12] . The downy mildew Hyaloperonospora arabidopsidis ( Hpa ) is an obligate biotroph that can only grow on Arabidopsis thaliana [13] . The Hpa-Arabidopsis pathosystem has been used to study host/parasite co-evolution and allowed the identification of cognate avirulent ( AVRs ) and resistance ( R ) proteins involved in specific Arabidopsis/Hpa interactions [14] , [15] . The sequencing of the Hpa isolate Emoy2 genome revealed its potential to encode at least 134 candidate effectors ( HaRxLs ) [9] . We report here assessments of the contribution of many of these HaRxLs to Arabidopsis immunity suppression . Filamentous pathogens likely secrete their effectors from intercellular hyphae or haustoria [16] . Several studies have defined apoplastic and cytoplasmic effectors , based on their target sites in the host [17] , [18] , [19] . Cytoplasmic effector proteins have been inferred from either their localization inside the host cell ( e . g . Uromyces fabae RTP1 protein ) [20] or their recognition by host cytoplasmic R proteins; examples include Melampsora lini AVRs ( AvrL567 , AvrM , AvrP123 , AvrP4 ) , Leptosphaeria maculans ( AvrLm1 ) and Blumeria graminis f . sp . hordei ( AVRa10 , AVRk1 ) [21] , [22] , [23] . In oomycetes , the cloning of four AVR genes , Avr1b-1 ( Phytophthora sojae ) , Avr3a ( Phytophthora infestans , P . i . ) , ATR1 and ATR13 ( Hpa ) ( [24] , [25] , [26] , [27] ) revealed a common N-terminal organization with signal peptides , enabling secretion from the pathogen , followed by a region that includes the amino acid motifs RxLR ( for arginine ( Arg ) , any amino acid , leucine ( Leu ) , Arg ) and EER ( for glutamine ( Glu , Glu , Arg ) [28] . Functional analysis of Avr3a demonstrated that it accumulates in and is secreted from P . i . haustoria before its translocation into the host cell and its RxLR and EER motifs are required for delivery [29] . Avr1b requires its RxLR and EER motifs for uptake independently of the presence of the pathogen [30] . Binding of the RxLR EER and RxLR-like motifs of several fungal and oomycete proteins to phosphatidyl-inositol 3-phosphate ( PI-3-P ) has been proposed to mediate their entry into host cells [31] . In summary , the oomycete and fungal RxLR-like motifs , and the recently described LXLFLAK motif in Crinkler proteins [32] are conserved sequences involved in effector translocation into the host [33] , [34] . For Hpa , no apoplastic effectors have been reported and the few effector candidates of Hpa that have LXLFLAK motifs , also carry overlapping RxLR motifs . For that reason we focused our “effectoromics” studies on predicted HaRxL-type effector candidates . Unlike Phytophthora spp . , Hpa is not transformable [35] , [36] . Previous reports indicate that the bacterial type-three secretion system ( TTSS ) can be used to study how non-bacterial effectors can manipulate host cell functions [37] , [38] . The phytopathogenic bacterium Pseudomonas syringae possesses a TTSS that translocates effectors to the host cell cytoplasm [39] via signals located on their N-termini [40] . P . syringae pv tomato DC3000 ( Pst DC3000 ) grows on multiple Arabidopsis accessions [41] . Its growth in planta increases in PTI-compromised mutants like fls2 , cerk1 , sdf2 , and crt3 [42] , [43] , [44] , [45] , and decreases due to ETI when it delivers bacterial AVRs in plants carrying the cognate R proteins [46] , [47] , [48] , [49] . The Hpa effectors ATR1 and ATR13 can be delivered from P . syringae using fusions to the N-terminus of the bacterial effectors AvrRps4 and AvrRpm1 [37] , [50] . This technique has enabled the study of Hpa cytoplasmic effectors by monitoring growth in planta of P . syringae delivering different alleles of ATR1 and ATR13 into Arabidopsis accessions that carry ( or not ) the cognate R proteins RPP1 and RPP13 . Although enhanced pathogen growth due to interference with host defence can be detected , it is likely that effectors whose prime role is to promote the elaboration of haustoria would be missed in this kind of assay . By genomic and expression analysis of the Hpa isolate Emoy2 we defined 140 HaRxLs that carry a signal peptide and RxLR motif , and ranked them taking into account allelic diversity and expression level . Our aim was to survey a broad set of candidate HaRxLs to investigate if they might play a role in suppressing PTI and/or ETI . For this purpose the Effector Detector Vector ( EDV ) system [37] , with a luciferase-expressing Pst DC3000 strain ( Pst-LUX ) , was used for an initial assessment of whether 64 of these HaRxLs could enhance Pst-LUX growth on at least some Arabidopsis accessions . The majority of HaRxLs were found to increase host susceptibility on multiple accessions revealing a correlation with increased callose suppression . Interestingly , many HaRxLs were not effective on all accessions , implying that host effector targets might evolve to be refractory to effector action . However , although a few HaRxLs reduced bacterial growth on certain accessions , avirulence was rare . Selected HaRxLs were studied in more detail in transgenic plants , confirming their disease-promoting activities . On turnip , a non-host plant for Hpa , fewer HaRxLs enhanced Pst-LUX growth , and more reduced it , providing interesting clues into mechanisms that underpin non-host resistance . In addition to providing novel insights into how parasites impose host susceptibility , these data reveal several high priority HaRxLs for future mechanistic investigations .
To establish an inventory of the RxLR effector secretome of Hpa , we scanned the draft genome of Emoy2 ( http://vmd . vbi . vt . edu ) for all possible open reading frames ( ORFs ) encoding putative proteins longer than 100 amino acids . We then searched these sequences for the presence of signal peptides and from those we extracted gene models carrying RxLR-like motifs ( RxLR/Q; RxL ) with the sequence and positional constrains defined in Figure 1 ( see Materials and Methods ) . Different sets of HaRxLs were identified depending on the version of the genome used ( versions 3 . 0 , 6 . 0 and 8 . 3 . 2 ) . We merged different lists to define a set of 191 HaRxL genes that included the known effector genes ATR1 [26] and ATR13 [27] . Most of the encoded proteins were smaller than 300 amino acids . Approximately 37% had an acidic motif ( EE/EER ) after the RxLR and ∼6% had a predicted nuclear localization signal ( data not shown ) . This collection included HaRxLs identified by others using similar search algorithms [9] , [28] . We next tested which HaRxLs were expressed during the oomycete life cycle and whether these were correctly predicted . A set of Sanger ESTs from germinating Hpa spores ( [9] and two different cDNA libraries from infected Arabidopsis plants at 3 and 7 days post inoculation ( dpi ) were used ( see Materials and Methods ) . We verified expression of 140 of the 191 predicted HaRxLs . Ninety of them were expressed from the asexual spore stage , perhaps ensuring their early availability upon initiation of infection , and remained expressed in planta until 7 dpi . The remaining 51 HaRxLs were expressed either at 3 dpi , 7 dpi or both . Data in column M of Table S1 illustrate the expression pattern of the sub-sets of effectors tested in this work . Of those for which we could confirm expression , the majority ( 90% ) of the HaRxLs was correctly predicted and none had introns ( data not shown ) . Highly expressed and accurately predicted HaRxLs were prioritized for cloning . We then looked for evidence of polymorphism in effector candidates between seven Hpa isolates ( Cala2 , Emco5 , Emoy2 , Hind2 , Maks9 , Noco2 , Waco9 ) . Single Nucleotide Polymorphisms ( SNPs ) were detected either on PCR products ( Baxter et al . , unpublished data ) or partial assemblies of Illumina short reads ( N . Ishaque , unpublished data ) . Our results indicated that 12% of the HaRxLs were not polymorphic , 56% had between 1 and 10 SNPs , and 31% showed more than 10 and up to 38 SNPs . We classified them as not polymorphic ( 0 SNPs ) , low ( ≥1 SNPs ≤5 ) , medium ( ≥6 SNPS ≤15 ) and high polymorphic ( >16 SNPs ) candidates ( Column L , Table S1 ) . For some HaRxLs it was difficult to distinguish heterozygosity from paralogous family members . In consequence , the real level of polymorphism might be underestimated . Recognized Hpa effectors like ATR1 and ATR13 show high levels of polymorphism [26] , [27] while we hypothesize that non-recognized virulent effectors , adapted to interact with a specific host target , might have low sequence variability . Hence , candidates belonging to all four above described categories were used in this study . The Effector Detector Vector ( EDV ) delivers individual effector candidates to host plant cells using the TTSS of Pseudomonas syringae [37] . Seventy-four HaRxLs were cloned into pENTR/pEDV vectors ( pEDV-HaRxLs ) ( Table S1 ) . We obtained 71 fusion proteins ( AvrRPS4N1–136–HA tag-HaRxL ) . Two candidate effectors could not be cloned correctly in pEDV , and another one was truncated and further used as a negative control ( NC2 , Table S1 ) . Correct in-frame constructs were introduced by conjugation into Pst DC3000 and derivative strains , particularly one expressing the luciferase ( luxCDABE ) operon of Photorhabdus luminescens ( Pst-LUX ) [41] ( see Materials and Methods for full details ) . No differences in bacterial growth ( either in liquid or solid media ) were observed in Pst-LUX clones carrying any of the 71 AvrRPS4N1–136–HA tag-HaRxL fusion proteins regarding the growth of Pst-LUX harbouring AvrRPS4N1–136–HA tag-GFP/AvrRPS4AAAA ( data not shown ) . We performed in vitro secretion assays to check that the 71 fusion proteins obtained were made in bacteria and secreted to the medium in TTSS-inducing conditions . Secreted protein could be detected as illustrated in Figure S1A . Proteins of the expected size were produced by Pseudomonas for 64 of the pEDV5/6-HaRxLs cloned . No proteins , or protein bands of incorrect size , were observed for the remaining 7 HaRxLs , which were not used in further assays ( Table S1 , column H ) . Thus , our library comprised 64 Emoy2 pEDV-HaRxLs . In Pst DC3000 , the TTSS , encoded by the hrp-hrc ( hypersensitive response [HR] and pathogenicity-hr conserved ) gene cluster , is required for elicitation of HR in non-host plants like tobacco and for full pathogenicity in host plants like tomato [51] , [52] . To verify that the HaRxLs proteins did not alter Pseudomonas growth in planta by blocking the TTSS , we performed HR cell death tests in tobacco ( Nicotiana tabacum cv . Petit Havana ) and disease assays in tomato ( Solanum lycopersicum cv . Moneymaker ) . Of the 64 pEDV-HaRxLs delivered by Pst-LUX in tobacco , only 1 attenuated HR in tobacco while four reduced disease symptoms in tomato . No candidate impaired both activities or completely abolished HR or disease ( Table S2 columns C , D and representative examples in Figure S1 B , C ) . We infer from these results that none of the pEDV-HaRxLs constructs blocked Pst-LUX TTSS translocation of effectors . The Pst-LUX strain was designed for screening multiple Arabidopsis mutants/accessions that vary in resistance to PstDC3000 [41] . To evaluate the sensitivity of this system , we carefully validated the correlation between the level of bacterial bioluminescence and bacterial growth in planta using ATR1 and ATR13 ( Figure S2 ) . ATR1Emoy2/Cala2 and ATR13Emoy2/Emco5 conferred enhanced growth to Pst-LUX in the susceptible genotype Col-0 , as did ATR13Emoy2 on Nd-0 plants . This phenotype could be detected as an increase in bioluminescence that correlated with higher numbers of bacteria ( colony forming units ( cfu ) /cm2 ) ( Figure S2 ) . We were also able to detect decreased growth conferred to Pst-LUX by ATR13Emco5 or ATR1Emoy2 in Nd-0 plants ( Figure S2 ) . Using spray inoculation , a protocol was developed to assay bacterial growth in a sub-set of the 96 accessions described by Nordborg et al . , [53] , selected to maximize variability ( Bay-0 , Br-0 , Col-0 , Ksk-1 , Ler-0 , Nd-0 , Oy-0 , Shakdara , Ts-1 , Tsu-0 , Wei-0 , Ws-0 ) ( Figure 2 ) . Plants were spray-inoculated with Pst-LUX carrying EDV constructs that delivered either a control protein or an HaRxL via TTSS . At 3 dpi , the bioluminescence ( photons/fresh weight ) emitted by the bacteria was quantified as a measure of bacterial growth ( Figure 2 , see details in Materials and Methods ) . The growth of Pst-LUX in planta carrying each HaRxL was compared to a control ( see below ) and expressed as a ratio . This assay allowed us to establish whether a given HaRxL was able to enhance or decrease Pst-LUX growth , manifested as quantitative differences in bioluminescence , on multiple host accessions in parallel . Sixty-four pEDV-HaRxLs and 3 control proteins ( EDV5:HA-AvrRPS41–136 , EDV6:HA-YFP , EDV6:HA-AvrRps4-AAAA ) were delivered via Pst-LUX to 12 different Arabidopsis accessions . At three days post spray-inoculation , the photons/second/g fresh weight ( CPS/Fw ) were scored for five plants of each accession and averages , standard deviations and errors calculated . The ratio of increase or decrease in the CPS/Fw emitted by a Pst-LUX strain delivering a given pEDV-HaRxL , versus control ( in the corresponding EDV5 or EDV6 backbone ) was determined , as well as its statistical significance ( one tailed T-test , unequal variances assumed ) ( Figure 2 , Table S2 ) . Experiments were repeated at least three times . Given the variability between experiments , the final outcome of each pEDV-HaRxL effect per accession was assessed across experiments and categorized according to the following criteria: i ) a reproducible ratio higher or lower than one , showing the same trend on at least two experiments with a minimum statistical significance of p<0 . 05 on each of them , was considered as either “Enhanced” or “Decreased” growth and labeled with ( + ) or ( − ) respectively; ii ) a non-reproducible ratio showing opposite statistically significant trends or the same trend but not statistically significant was considered as “No Change” and scored as ( = ) ( see Table S2 , columns R , S , T ) . A graphical synopsis of the screening outcome per effector across the 12 host accessions is presented in Figure 3 , with the most effective pEDV-HaRxL ( HaRxL62 ) at the top , conferring enhanced Pst growth on all 12 accessions . To distinguish the effector-driven Enhanced/Decreased Pst-LUX growth patterns from the random phenotypes that can be obtained by delivering any given protein into the plant via the EDV system , we included four internal controls ( negative controls , NCs ) . These constructs were truncated versions of HaRxLs ( NC2 and NC3 ) , non-secreted proteins with an RxLR-like motif ( NC1 ) or a small bacterial protein with similarity to a xylosidase ( NC4 , genebank: AP12030 . 1 ) . NC1 is part of a larger Hpa ORF encoding a putative transposase . NC2 is an early C-terminal truncated version of HaRxL143 ( before the RxLR motif ) , while NC3 is a frame-shift version of HaRxL77 ( Table S1 ) . Functional ATR13Emoy2 and ATR13Emco5 alleles were also included . The pattern shown by these internal controls allowed us to establish threshold levels to assess whether a given HaRxL had a credible effect on Pst-LUX growth ( Figure 3 , Table S2 ) . NC3 and NC4 did not impact Pst-LUX bioluminescence . We attributed the residual effect of NC1 and NC2 on Pst-LUX growth to the random variability of the system ( Figure 3 ) and therefore we set the thresholds as follows: for an effector to be considered as an “enhancer” of Pst-LUX growth it had to show an increased significant change in bioluminescence in four or more accessions . Conversely , as the control ATR13Emco5 was recognized in only 1 accession out of the 12 tested , any effector that decreased the growth of Pst-LUX on one or more accessions was classified as capable of being recognized ( Figure 3 ) . Our results indicate that 43 pEDV-HaRxLs enhanced Pst-LUX growth in planta , while 28 decreased Pst-LUX activity ( Figure 3 ) . The majority ( 72% ) of the pEDV-HaRxLs that increased growth in ≥4 accessions did not decrease it in any accession ( Green/black bars , Figure 3 ) . This suggests most pEDV-HaRxLs can suppress plant defenses and avoid recognition by the host , although their effectiveness varies between accessions . We found only one pEDV-HaRxL capable of enhancing Pst-LUX growth in all accessions tested ( HaRxL62 ) ; we infer that its plant target ( s ) might have little natural variation between accessions and that no R gene ( s ) recognize it in these accessions . In addition , pEDV-HaRxL9 , 17 , 21 , 45b , 53 , 73 and HaRxLL464 were able to increase Pst-LUX growth in 8 or more accessions and had no negative effect on the remaining ones . The pEDV-HaRxLs that decreased Pst-LUX growth did so mainly in ≤3 accessions ( 68% ) . This pattern of accession-specific Pst-LUX growth reduction was observed for both alleles of ATR13 , and also for pEDV-HaRxL4 , 44 , 45 , 57 , 106 , 108 , HaRxLL445 , 483 and 495 . Only nine pEDV-HaRxLs conferred decreased bacterial growth in >3 and <8 accessions . Given the lack of accession-specificity of their phenotype we speculate these HaRxLs might affect the virulence activity of Pst effectors ( Magenta areas , Figure 3 ) . We extended the concordance analysis developed with ATR1Emoy2/Cala2 and ATR13Emoy2/Emco5 ( Figure S2 ) to 13 other pEDV-HaRXLs delivered from Pst-LUX by conducting growth curve assays using seven Arabidopsis accessions ( Table S3 ) . For this test we selected some HaRXLs representative of the different patterns we observed in Figure 3 . Briefly , we tested HaRxL62 because it enhanced Pst-LUX luminescence in all accessions; HaRxL14 , HaRxL21 , HaRxLL60 , HaRxLL464 , and HaRxLL492 because they enhanced Pst-LUX luminescence in ≥6 , but did not decreased it in any accession . HaRxL44 , 45 , 57 and 106 also increased bacterial luminescence in ≥6 accessions but decreased it in 1–3 accessions . HaRxL70 was selected among the group of “non-effective” effectors , and HaRxL79 because it reduced Pst-LUX bioluminescence in >3 accessions . For 32 of 35 combinations ( pEDV-HaRXL×accession ) we confirmed the correlation between enhanced bioluminescence and increased bacterial growth . These data verified that Pst-LUX bioluminescence reveals the effect of HaRXLs on Pst-LUX growth . We also observed that some HaRXLs have a substantial positive effect on bacterial growth on multiple accessions , and can increase Pst-LUX growth ∼10-fold ( Table S3 and Figure S3 ) . In particular , we confirmed that HaRxL62 and HaRxL14 render multiple host accessions more susceptible to bacterial infection ( Figure S3 ) . Accession-specific effects were verified for HaRxLL464 and HaRxL21 while putative recognition events , leading to a decrease in bacterial growth , were verified for HaRxL44 in Ler-0 , HaRxL57 in Ksk-1 ( Figure S3 , Table S3 ) , and HaRxL106 in Col-0 ( Table S3 ) . No effect was observed for HaRxL70 in Col-0 while the decrease in bacterial growth caused by HaRxL79 was only observed when plants were spray inoculated ( Table S3 ) . These data reinforced the usefulness of the EDV Pst-LUX assay for selecting candidates for further work , and confirmed several candidates as a high priority for further investigation . To evaluate if host genotypes influenced the pattern of Arabidopsis responsiveness to the set of HaRxLs tested , the spectrum of effective HaRxLs per accession was analyzed . We found that an average of 42% of the pEDV-HaRxLs enhanced Pst-LUX growth on any given accession , while only ∼11% reduced Pst-LUX growth . Many combinations ( 46% ) did not cause any change in Pst-LUX growth ( Figure S4 ) . Enhancement or decrease of susceptibility was not restricted to a particular set of accessions , and did not correlate with those accessions showing resistance or susceptibility to the infection by the Hpa isolate Emoy2 ( Figure S4 ) . The only deviations from this pattern were Nd-0 , in which most of the pEDV-HaRXLs ( 73% ) increased Pst growth and only ATR13Emco5 was able to decrease it , and Br-0 in which fewer pEDV-HaRXLs in total were effective ( 31% compared to the average of 42% for all other accessions ) ( Figure S4 ) . These results are consistent with the idea that some effector targets are widely conserved while others vary between accessions . The level of polymorphism of HaRxLs did not correlate with the capacity to enhance Pst-LUX growth . Among the 64 candidates tested , 11 were highly polymorphic , 21 had a medium level and 32 showed low polymorphism . HaRxLs categorized in these three groups showed ability to increase bacterial luminescence in an average of 6±2 . 54 , 6±3 . 15 or 5±2 . 66 accessions , respectively . For example , HaRxLL464 and HaRxL57 showing low or no polymorphism , and the highly polymorphic HaRxL106 and HaRxL21 were all capable of increasing Pst-LUX growth in 8 or more host accessions . Isolate Emoy2 is recognized by certain Arabidopsis accessions , indicating effector recognition by R protein ( s ) . In order to identify avirulent HaRxLs in the library , we analysed in detail Pst-LUX growth assays in each of the 12 Arabidopsis accessions ( Figure 3 , Figure S4 ) . Possible recognition of pEDV-HaRxL strains in our assays was indicated by the decrease in Pst-LUX growth , usually in an accession-specific manner ( Figure 3 , Table S2 ) . Potentially novel ATR proteins may have been detected in interactions with accessions Col-0 , Ler-0 , Br-0 and Ksk-1 ( Figure S4 ) . ETI is strongly correlated with HR-like cell death [54] , [55] although HR is not always required for resistance [49] , [56] . We tested possible recognitions using a weakly virulent Pst DC3000 ΔCEL ( Pst-ΔCEL ) strain and a modified P fluorescens carrying a functional TTSS ( Pf0-1 ) [57] to deliver potentially recognized HaRxLs to the corresponding “resistant” accessions . We performed localized leaf infiltrations using high doses of bacteria and looked for macroscopic ( leaf collapse ) and microscopic ( dead cells stained with trypan blue ) indicators of HR-like cell death 24 h post infiltration . Surprisingly , no HaRxLs besides the positive control ( ATR13Emco5 in Nd-0 ) provoked clear signs of macroscopic HR . We then stained infiltrated leaves with trypan blue and examined for microscopic lesions . All micro-HR lesions were much smaller and weaker than those triggered by bacterial effectors like AvrRpm1 or AvrRpt2 ( data not shown ) or by the Hpa effector ATR13Emco5 in Nd-0 ( Table S4 ) . In 78 pEDV-HaRXL/accession combinations , we saw micro-HR-like cell death in only 7 interactions , comprising just 4 candidate effectors ( HaRxL4 , 18 , 70 and 80; in bold in Table S4 ) . Similar results were obtained with both Pf0-1 and Pst-ΔCEL strains , except for HaRxL106 where no HR was detected in Col-0 and Ksk-1 when delivered through Pf0-1 ( Table S4 ) . Nevertheless , the decrease in bacterial growth observed for Pst-LUX delivering each of these candidate effectors in the corresponding accessions was confirmed by reduction in disease symptoms and bacterial growth using Pst-ΔCEL ( data not shown ) . None of the mild recognition patterns matched with profiles expected for ATR4 , ATR5 and the putative ATR ( s ) recognized in Ksk-1 and Br-0 ( Figure S4 ) . Interestingly , two HaRxLs ( HaRxL18 and 70 ) were weakly recognized in Bay-0 , an accession susceptible to isolate Emoy2 . These results suggest that the decreases in Pst growth we see in some HaRxL/accession combinations are not due to strong R/AVR interactions . Also , weak recognition of some HaRxLs might not result in HR [49] , [56] . Many HaRxLs delivered in planta by Pst-LUX confer increased growth of an already virulent pathogen . Enhanced susceptibility to adapted pathogens is often a result of PTI suppression [42] , [43] , [44] , [45] . PTI- responses , like callose deposition , likely limit the growth of Hpa during infection [58] , [59] , [60] . Also , ATR13Emoy2/Emco5 can complement HopM1-mediated suppression of callose deposition when delivered by Pst-ΔCEL [37] . Therefore , we investigated if PTI affects Hpa growth , and whether Hpa is able to actively suppress PTI . As no known PAMP has been identified for Hpa , we tested whether Hpa infection alters responses to known PAMPs . To test if PTI can attenuate Hpa growth during a compatible interaction , we pre-treated young Col-0 plants with flg22 ( 100 nM ) , an inactive flg22 ( from Agrobacterium tumefaciens ) , or chitin ( 200 µg/ml ) 24 h before the plants were sprayed with spores of Hpa isolate Noco2 . Reduced hyphal growth was observed in the areas where the PAMPs were applied , as assessed by trypan blue staining of the pathogen in the leaves ( Figure 4 A ) . We also noticed a decrease in the rate of Hpa sporulation ( Figure 4 B ) . These phenotypes were not observed with inactive flg22 ( Figure 4 A , B ) , or when the treatment was applied on plants mutant for these PAMP receptors ( fls2-1 and cerk1-1 , data not shown ) . These data suggest that the phenomenon is specific for PTI . We also noticed that the “protection” that flg22 and chitin conferred to the plants was higher near the infiltrated site , was dose-dependent , and diminished with the time of pre-infiltration relative to Hpa infection ( 24 hs>48 hs>72 hs ) ( Figure 4 A lower panel and data not shown ) , consistent with the transient nature of the local PTI response [61] . We did not observe extensive local micro-HR in flg22-treated leaves [62] . The “local” effect of flg22 and chitin in restricting Hpa hyphal growth ( Figure 4 A ) might indicate that either we did not induce systemic acquired resistance ( SAR ) [63] , or we applied Hpa before SAR was established . One of the earliest PTI responses is the generation of reactive oxygen species ( ROS burst ) [64] . To determine if Hpa can suppress ROS , we measured flg22-induced ROS in infected leaf tissues . We observed a highly reproducible reduction by ∼50% in ROS accumulation induced by flg22 if leaves were pre-infected with Hpa isolate Noco2 ( Figure 4C ) . Oy-0 plants infected with Emoy2 showed the same pattern ( data not shown ) . Thus , Hpa infection can dampen PTI responses . PTI results in callose deposition in the cell wall [65] and microbial effectors that impair PTI also suppress callose deposition [66] , [67] , [68] , [69] . Pst-ΔCEL is unable to suppress callose deposition due to lack of HopM1 and AvrE [70] . We introduced pEDV-HaRxL constructs into Pst-ΔCEL and evaluated if they could restore callose suppression when infiltrated in Col-0 . Sixty-two pEDV-HaRxLs and 2 control proteins were delivered through this system . Due to variability between leaf reactions in the same plant and among experiments , we established a threshold to define significant reductions in callose deposition ( see Materials and Methods ) . Taking into account the maximum levels of random callose suppression observed after delivery of the negative controls ( NC1 and NC2 ) , we set up the threshold to 40% of callose suppression because negative controls could reduce up to 29% or 34% of the callose dots found when Pst-ΔCEL delivered the additional controls ( EDV5:HA-AvrRPS41–136 , EDV6:HA-YFP or EDV6:HA-AvrRps4AAAA ) . Using this stringent criterion we found that 35 HaRxLs were able to suppress callose deposition by more than 40% , while 27 HaRxLs did not . Those effectors complementing the phenotype of Pst-ΔCEL are indicated with asterisks ( * ) in Figure 3 . We noticed that most of the HaRxLs able to complement Pst-ΔCEL were also able to enhance Pst-LUX growth in four or more host accessions ( Figure 3 ) . To establish the degree of correlation between both phenotypes , the extent of callose suppression was compared with the changes in Pst-LUX growth produced by each effector in the accession Col-0 ( Figure 4 D ) . For this , we classified the effector's conferred phenotype in the host as follows: enhanced susceptibility = 27 HaRxLs ( right side of the graph ) , decreased susceptibilty = 10 HaRxLs ( left side of the graph ) , no change = 25 HaRxLs ( axes intersection ) . When we plotted the percentage of callose suppression of each of the effectors in these groups , we found 77 . 7% of HaRxLs candidate effectors with a positive effect on Pst-LUX growth in Col-0 were able to suppress callose deposition , while only 3 . 2% of those decreasing Pst-LUX growth could reduce callose levels ( Figure 4 D ) . These percentages deviate significantly from those expected for a random distribution ( Z-test p<0 . 05 ) . The HaRxLs located at the top right side of Figure 4 D were those that strongly suppressed callose deposition , and are indicated with plus ( + ) signs in Figure 3 . The HaRxLs with no effect on Pst-LUX growth showed no clear trend in ability to suppress callose deposition . We conclude that most HaRxLs that enhance Pst growth also suppress PTI , increasing host-susceptibility . Based on the data presented in Figure 3 , Tables S2 and S3 , Figure S3 and Figure 4D , we prioritized HaRxL14 , 21 , 44 , 45/45b , 57 , 62 , 106 , HaRxLL60 , 464 , 492 and 495 for further detailed studies . HaRxL14 , 21 , 44 , 57 and 62 were chosen because they increased Pst-LUX growth in more than 6 accessions and strongly suppressed callose deposition in Col-0 ( >60% ) ( Figure 3 and top right side on Figure 4D ) . HaRxLL464 and HaRxL45/45b strongly enhanced Pst-LUX growth in 9 or more accessions , but showed a mild reduction in callose deposits ( Figure 3 and Table S2 ) . HaRxL106 conferred enhanced susceptibility to Pst-LUX in several ecotypes , except Col-0 , where it nevertheless reduced callose deposits ( Figure 3 ) . HaRxLL60 and HaRxLL492 conferred enhanced growth of Pst-LUX in 5–6 accessions , but were unable to complement the Pst-ΔCEL phenotype . To investigate if the phenotypes observed with the Pst-EDV-delivery screenings were also conferred by stably expressing the corresponding HaRxLs directly in the host plant , transgenic Arabidopsis plants were generated initially in the Col-0 background . The following HaRxLs were expressed from the constitutive ( CaMV 35S ) promoter: HaRxL14 , 21 , 44 , 45/45b , 57 , 62 , 106 , HaRxLL60 , 464 , 492 and 495 . Of these , for three candidates ( HaRxL62 and HaRxL45/45b ) either we did not obtain transgenic lines or the ones generated showed segregation of pleiotropic effects , and in consequence are not described here . Some plants ( lines 35S-HaRxLL464 and 35S-HaRxL44 ) showed a 20–30% increase in fresh weight and others ( line 35S-HaRxLL60 ) a 30–40% decrease in fresh weight . In some cases ( 35S-HaRxL106 and 35S-HaRxLL60 ) the shape of the leaves changed , becoming either elongated and darker , or serrated and smaller , respectively ( data not shown ) . The level of expression of the transgene in each line was verified by semi-quantitative RT-PCR ( Figure S5 ) . Using three independent lines for the remaining nine candidate effectors , that did not showed perturbed growth , we assessed whether in planta over-expression altered pathogen development , PTI or ETI . We characterized the responses of these transgenic lines to both bacterial ( Pst-LUX , Pst ΔavrPto/ΔavrPtoB ) and oomycete pathogens ( Hpa isolates Noco2 and Emoy2 ) ( Figure 5 and Figure S6 ) . Transgenic lines expressing different HaRXLs showed increased susceptibility to Pst-LUX when spray-inoculated ( 8 lines , Figure S6 ) , or to Pst ΔavrPto/ΔavrPtoB when infiltrated ( 7 lines , Figure 5 A ) . Also , seven lines showed enhanced susceptibility to Hpa isolate Noco2 ( Figure 5 B ) . These phenotypes were observed in at least two out of the three transgenic lines recovered for each effector . We investigated if the transgenic lines were compromised in ROS burst and callose deposition in response to flg22 ( Figure 6 ) . Eight 35S-HaRxLs were able to reduce flg22-triggered ROS accumulation by 22 to 65% compared to controls ( Figure 6 A ) . Also , callose deposition was diminished by an average of 40% compared to controls ( Figure 6 B ) . The ROS and callose suppression in transgenic lines expressing HaRxL 14 , 21 , 44 , 57 , 106 , HaRXLL 464 was comparable to that observed in plants that express the bacterial effectors HopU1 and HopAO1 ( Figure 6 A , B ) . In summary , six different Hpa HaRxLs , when stably-expressed in planta , displayed a positive correlation between increased susceptibility to Pst and/or Hpa and reduced levels of ROS and callose deposition elicited by flg22 ( Figure 7 ) . To establish if any of the nine HaRxLs could also compromise ETI , we tested transgenic lines for altered resistance to Hpa Emoy2 which is recognized in Col-0 [71] . Two-week-old seedlings were sprayed with Emoy2 conidiospores and trypan blue-stained at 5 dpi . While some restricted hyphal growth was detected , we did not observe asexual or sexual reproduction -in true leaves- in any line ( summarized in Figure 7 and data not shown ) . We then studied the ETI response to AvrRpm1 from P . syringae pv . maculicola [72] . AvrRpm1 was delivered via Pf0-1 in leaves of 4-week-old plants and a macroscopic HR recorded . The onset of HR was delayed but not completely suppressed in four different lines ( data not shown ) . We therefore performed a more sensitive assay by quantifying the levels of ion leakage upon AvrRpm1 detection ( Figure S7 ) . Notably , the same four sets of transgenic lines ( 35S-HaRxL106 , 35S-HaRxLL492 , 35S-HaRxLL464 and 35S-HaRxLL60 ) , slightly but significantly reduced the levels of ion leakage caused by recognition of AvrRpm1 compared to the control lines ( Figure S7 and Figure 7 ) . Our results suggest that the majority ( six out of nine ) of Hpa Emoy2 HaRxLs constitutively expressed in planta gave phenotypes ( enhanced susceptibility to Pst-LUX and suppression of PAMP-triggered callose deposition ) consistent with the results obtained from the Pst-LUX EDV-screening in the Col-0 accession ( Figure 7 ) . Two transgenic lines ( 35S-HaRxL106 and 35S-HaRxLL60 ) showed phenotypes that contrasted with those observed in the EDV screen on Col-0 , and for another line ( 35S-HaRxL70 ) no effect was detected with the EDV system in Col-0 , while enhanced Pst-LUX growth was observed in the over-expressing plants ( Figure 7 ) . When HaRxL106 is delivered by EDV from Pst DC3000 strains , it appears to be recognized by Col-0 and Ksk-1 accessions , but this HR-like cell death is not evident when it is delivered by Pf0-1 ( Table S4 ) . Expression of 35S-HaRxLL60 caused the plants to be smaller , accumulate callose constitutively and become resistant to the pathogens tested ( Figure 5 , Figure 6B , Figure S6 ) . Nevertheless , 35S-HaRxLL60 reduced flg22 ROS production ( Figure 6A ) . The set of six lines with enhanced susceptibility to Pst and/or Hpa also displayed a reduced ROS burst and callose deposition after PAMP treatment ( Figure 7 ) . Overall , our analysis points to suppression of PTI-related responses as a predominant mode of action of Hpa candidate effectors in planta . Most plants are resistant to most pathogens , and this so called “non-host resistance” ( NHR ) could be caused by either ineffectiveness of effectors , resulting in failure to suppress PTI , or recognition of effectors , resulting in resistance via ETI [8] . To test these hypotheses we delivered HaRxLs via Pst-LUX in the non-host Brassica rapa cv Just Right ( turnip ) . Pst-LUX is virulent in turnip and causes disease symptoms at 3 dpi when inoculated at low dose . We tested our collection of HaRxL-carrying Pst-LUX strains in turnip and monitored symptoms and growth . After three rounds of screening we found that 20 effectors can alter Pst-LUX growth in turnip ( 13 increase , 7 decrease ) , while the remaining 44 did not cause any changes ( Figure S8 and Table S2 , column U ) . We compared these data with the number of “effective” HaRxLs in one given Arabidopsis accession ( 39 in Col-0 ) and the average for the 12 accessions we tested ( 35 ) ( green plus magenta bars vs . black bars in Figure S4 ) . In contrast , only thirteen HaRxLs increased Pst-LUX growth in turnip , while in Arabidopsis accessions an average of 27 , and minimum of 18 ( in Br-0 ) increased Pst-LUX growth ( see Figure S4 and S8 ) . Those candidates enhancing Pst-LUX growth in turnip also did so in >3 Arabidopsis accessions , implying that their effect on plant immunity is not species-specific and some plant targets might be conserved . Conversely , while similar numbers of HaRxLs decrease Pst-LUX growth in Arabidopsis ( 8 in average ) and turnip ( 7 ) , we noticed three HaRxLs ( HaRxL17 , HaRxL47 and HaRxL63 ) that reduced Pst-LUX growth and disease symptoms in turnip that did not show this phenotype in the 12 Arabidopsis accessions . To assess if these HaRxLs might be specifically recognized in turnip and contributing to NHR against Hpa , we used higher dose inocula to deliver them using Pst-ΔCEL . We did not observe HR-like cell death , but we confirmed the reductions in growth and disease symptoms ( data not shown ) . It remains possible that these HaRxLs might be triggering weak ETI in turnip that does not involve HR-like cell death but still contributes to NHR ( see Discussion ) .
We found that of 64 expressed HaRxLs that could be secreted by the EDV delivery system , 43 ( ∼67% ) could increase the growth of Pst-LUX in several ( >4 ) host accessions . We were surprised that so many candidate effectors can enhance growth of an already virulent pathogen . The false positive rate is likely to be low , while the false negative rate could be high , because we set a stringent threshold to judge a putative effector “effective” compared to four internal controls . Despite the intrinsic variability of Pst-LUX spraying assays , the phenotypes were reproducible , even when the measurable differences were small ( Table S2 and S3 ) . We found that detection of LUX activity is more sensitive and less laborious than growth curve assays and noticed that 2- fold differences in LUX emission usually corresponded to differences between 0 . 3 and 0 . 6 log in growth . These might be considered small contributions to virulence , but are consistent with previously reported observations for bacterial effectors such as AvrRpm1 AvrRpt2 , AvrPtoB , HopF2 , HopAO1 and HopU1 , where their individual contribution to Pst DC3000 growth is of the same order of magnitude ( around 0 . 4 log ) or only detectable using low virulence Pst mutants [47] , [69] , [76] , [77] , [78] . A strong advantage of the EDV approach is that one Pst-DC3000 strain can be tested on many different Arabidopsis accessions to reveal accession-specific differences in HaRxL efficacy . This would be extremely laborious by generating stable transgenic lines in multiple accessions . Furthermore , some HaRxLs confer severe pleiotropic defects when expressed directly in planta , hampering efforts to test whether such lines are immunocompromised . Since not all HaRxLs are effective in all accessions , it seems likely that each Hpa isolate expresses a repertoire of effectors , each of which may be functional on some but not all host genotypes . The level of infection in Col-0 by compatible Hpa isolates is quite variable , with Waco9 more virulent than Noco2 , which is more virulent than Emco5 . Such observations are usually interpreted in terms of variation in avirulence gene content . However , variation in host targets as well as in Hpa effector complement may also underpin quantitative differences in host susceptibility . Hpa isolate Emoy2 was reported as having the highest likelihood of producing high levels of sporulation in a study involving 96 Arabidopsis accessions , and isolate Emco5 the lowest [79] . Although Hpa virulence appears to depend on multiple virulence genes with weak effects , rather than a few genes with strong effects , some effectors , such as HaRxL62 , 14 , 44 , 57 and 106 , are particularly effective and will repay detailed mechanistic investigation in the future . Significantly , we found that most of the HaRxLs ( 77% ) that increase Pst-LUX growth in Col-0 were also able to suppress Pst-ΔCEL-induced callose deposition . Conversely , those HaRxLs reducing Pst-LUX growth in Col-0 were generally unable to suppress callose deposition . Callose deposition is a late PTI response ( though also associated with ETI ) . We speculate that HaRxLs may enhance Pst-LUX growth via additional PTI suppression , either alone or in conjunction with Pst effectors . The increased susceptibility to Pst-LUX observed using the EDV system was usually consistent with phenotypes of plant lines that constitutively express the corresponding HaRxLs ( Figure 7 ) . Moreover , seven of the transgenic lines also showed increased susceptibility to Hpa isolate Noco2 . We infer that enhanced susceptibility results from suppression of host mechanisms that are active against diverse pathogens . The fact that they further elevate virulence conditioned by Pst-DC3000 effectors may reflect HaRxLs interference with targets that are not identical to those of Pst-DC3000 , resulting in an additive effect . Assays using flg22-induced ROS or callose deposition on stable transgenic lines indicate that the main target of HaRxLs is PTI . All of the transgenic lines tested showed either reduced levels of flg22-induced ROS or callose deposition or both . The PAMP complement in Hpa is unknown , as are their receptors and downstream signal transduction pathways in Arabidopsis . Several molecules have been reported as oomycete PAMPs [17] , [80] but their existence in Hpa is not known [9] , [81] . We show that pre-elicitation of PTI by bacterial and fungal PAMPs impairs Hpa growth and reproduction , indicating that to infect , Hpa must counteract these host responses . Moreover , in host tissues where high numbers of haustoria are established , PTI responses are attenuated . PTI involves multiple processes that can be attenuated by diverse pathogen effectors [76] , [77] , [78] , [82] . Our data support the idea that the function of the majority of the effector proteins is to inhibit plant immunity [4] , [83] , [84] . For Pst , 13 out of 28 active effectors ( 12 belonging to Pst-DC3000 ) have been reported to suppress PTI [84] , [85] , [86] . Thus , ∼50% of this bacterial pathogen's effector repertoire targets PTI in one host . Importantly , this hemibiotroph can infect Solanaceae as well as Brassicaceae , so more effectors might emerge as PTI suppressors when other host species are studied . It has also been observed that 91% of Pst-DC3000 effectors , when delivered at high titers from Pf0-1 , are able to suppress the HR induced by the bacterial effector HopA1 ( from P . syringae syringae ) in tobacco [38] . Since tobacco is a non-host for Pst-DC3000 , this study again points to a high functional redundancy between effectors in suppressing HR . In oomycetes , experimental characterization of several RxLR effector genes suggests that many function to suppress host defenses [37] , [50] , [86] . Also , 3 out of 32 P . infestans RXLR candidate effectors were able to suppress PAMP- triggered programmed cell death ( PCD ) in N . benthamiana , while another 13 induced either non-specific or R-mediated PCD [87] . We also identified HaRxLs that reduced Pst-LUX growth in the interaction with Arabidopsis accessions , and investigated whether they are new avirulence determinants ( ATRs ) . Surprisingly , we observed that strong incompatibility caused by HaRxLs is rare . None was able to trigger macroscopic ETI when delivered in planta at high titer , as ATR13Emco5 did in Nd-0 . Instead , several were identified that can reduce Pst-LUX growth in specific Arabidopsis accessions and four triggered micro HR-like lesions when delivered via Pst-ΔCEL . Since we tested only 64 HaRxLs from just one isolate , on only 12 host accessions , our survey was not exhaustive , and the anticipated ATR4 might not have been in our repertoire . Alternatively , the EDV assay may not be sensitive enough to detect new Hpa ATRs because these ATR-RPP recognitions are weaker than those already described with this system ( ATR13-RPP13 or ATR1-RPP1 ) . Conceivably , some ATRs might not carry an RxLR motif and therefore were not identified as candidate effectors in our bioinformatic analysis , as with the recently cloned ATR5 [88] . It also remains possible that either the sub-cellular localization or post-translational modifications of the EDV-delivered HaRxLs are not similar enough to their native form to be able to elicit ETI , although this has not been the case for ATR1 and ATR13 alleles [37] , [50] . We also tested if the recognition or non-functionality of HaRxLs could be involved in non-host resistance to Hpa in Brassica rapa ( turnip ) . We found that HaRxLs were “less effective” in turnip , but those HaRxLs that enhanced Pst-LUX growth in B . rapa also did so in Arabidopsis , suggesting conservation in their targets . Notably , three HaRxLs conferred reduced Pst-LUX and Pst-ΔCEL growth in B . rapa , but did not reduce growth in any Arabidopsis accession . Therefore , the inability of Hpa to grow in turnip might result not only from reduced “effectiveness” of the effector complement , but also from recognition in the “non-host” of a subset of effectors that are not recognized by most Arabidopsis accessions . As with any screening protocol , this heterologous system has some limitations . For example , HaRxLs that require extensive post-translational modifications will not be correctly produced by a prokaryotic system . Also , the co-delivery of an HaRxLs with c . a 30 effectors from Pst DC3000 might alter the outcome of the assay if positive or negative interactions exist between them . This might explain some of the discrepancies we observed between results obtained with the EDV system and those generated by expressing the candidate effectors directly in the plant . A further potential limitation of the EDV system is that effectors required to elaborate a haustorium inside the host cell might not be revealed as promoting Pst growth by the assays we developed . Despite such limitations , most of the phenotypes observed with the EDV system were confirmed in the transgenic lines . In conclusion , the EDV-based system has enabled the systematic analysis of the biological relevance of effector candidate proteins . The Pst-LUX and Pst-ΔCEL screens allowed the generation of a “ranking of effectors” that permitted the selection of highly interesting candidates as targets for subsequent mechanistic studies . Further detailed investigations of Hpa effectors will help reveal how Hpa alters host cellular processes to promote its growth and reproduction .
Different versions of the genome of Hpa isolate Emoy2 ( http://vmd . vbi . vt . edu; v3 . 0 , v6 . 0 and v8 . 3 . 2 ) were translated in all 6 reading frames . ORFs from ATG to Stop codon were identified using FgenesH ( www . softberry . com ) and GETORF ( http://emboss . sourceforge . net ) . Only sequences that encoding ≥100 aminoacids were considered . Secreted proteins were identified using SignalP v3 . 0 ( score cutt-off >0 . 9 ) , TargetP and PSort ( www . psort . org ) [89] . Proteins were considered as secreted if two out of three programs called the Signal Peptide as significant . HaRxLs were selected as fulfilling the following criteria: i ) Signal Peptide ( SP ) length <30 amino acids , ii ) RxLR-like motif ( RxLR/Q , RxL ) between 4 and 60 amino acids from the SP cleavage site , iii ) predicted protein had >40 amino acids after the RxLR like motif . Redundancy in the ORF dataset was corrected using BlastP . Sequences with 100% identity and E<10−5 were clustered and simplified to the one with highest SP score . A sub-set of RxLR-EE proteins was identified carrying an acidic motif ( EE , EER/G/D ) [28] between 4 and 30 aminoacids from the RxLR like motif . The expression of HaRxLs was verified using the following resources: i ) ESTs generated by Sanger sequencing and 454 sequencing from cDNA extracted from Emoy2 conidiospores [6] , ii ) Illumina sequence tags ( SAGE using 3′ tags from 7 dpi infected tissue ) , iii ) Illumina normalized/concatamerized cDNA ( from 3 and 7 dpi infected tissue ) ( Ishaque et al . , unpublished ) and iv ) RT-PCR with primers designed at 100 bp flanking the ORF sequence ( Baxter et al . , unpublished ) . The presence of the predicted and alternative ATGs and Stops Codons , as well as introns , was verified . Nucleotidic sequence polymorphisms on the HaRxLs accross seven Hpa isolates was assessed using either PCR products or in silico assemblies of Illumina short reads ( Ishaque et al . , unpublished ) . The HaRxLs were roughly classified as: No polymorphic ( 0 SNPs ) , Low ( ≥1 SNPs ≤5 ) , Medium ( ≥6 SNPS ≤15 ) and High Polymorphic ( >16 SNPs ) . To complete the characterization of the Hpa Emoy2 HaRxLs set , its sub-cellular localization ( PSORTII ) and presence of known conserved protein domains using Coil , Gene3D , HMMPfam , HMMSmart , HMMTigr , PFAM and Prosite was recorded . These data are available in Table S1 for the subset of HaRxLs cloned in this work . Selected HaRxLs were amplified from genomic DNA extracted from conidiospores of isolate Emoy2 using proofreading polymerase ( Accuprime Pfx , Invitrogen ) and standard PCR conditions . To generate the HaRxLs collection , the primers were designed to amplify from the signal peptide cleavage site or the RxLR ( inclusive ) until after the stop codon ( 3′ untranslated region , UTR ) . For cloning in pEDV3 or pEDV5 [37] , the primers were designed to have SalI/ClaI and BamHI/BglII restriction sites at the 5′ and 3′ ends respectively . For cloning in pEDV6 , a Gateway destination version of pEDV3 , the sequence CACC was added at the 5′ of the Forward primer . PCR products were gel purified ( Qiagen ) and ligated ( EDV3/5 ) or recombined in pENTRY-SD-D-TOPO/pDONR221 following the manufacturer's instructions and electroporated in Escherichia coli DH5∝ . Gentamycin ( EDV3/5 ) or kanamycin ( pENTR/pDONR ) resistant colonies were selected on plates and colony PCR performed with M13F and M13R primers . Colonies carrying the right size insert were selected for plasmid purification and sequencing . For EDV6 , the correct inserts on pENTRY/pDONR vectors were recombined using Gateway LR clonase or LR clonase II enzyme mix ( Invitrogen ) . The in frame fusion of vector-HA tag-HaRxL sequences were confirmed by sequencing with M13F and M13R primers . Plasmids were mobilized from E . coli DH5∝ to wild-type or mutant Pst strains by standard triparental matings using E . coli HB101 ( pRK2013 ) as a helper strain . Bacterial growth in vitro was controlled at 12 , 24 , 32 and 44 hs post inoculation of 10 ml Kings B media with a dilution corresponding to 0 . 00001 OD of an overnight culture of each of the Pst-LUX clones harbouring a different HaRxL or control proteins ( GFP , AvrRPS4AAAA ) . Three colonies per clone were assayed in different experiments . Growth was measured assessing turbidity at OD600 ( for liquid cultures ) or counting colonies of plated dilutions ( in solid media ) . No significant differences in growth kinetics were observed for the Pst-LUX carrying HaRxLs regarding the clones carrying control proteins or the empty vector pEDV5 . Bacterial strains used in this study include E . coli DH5∝ , Pseudomonas syringae pv tomato DC3000 carrying the luxCDABE operon from Photorhabdus luminescens ( Pst-LUX ) [41] , Pseudomonas syringae pv tomato DC3000 mutant ΔCEL [90] , Pseudomonas syringae pv tomato DC3000 double mutant ΔavrPto/ΔavrPtoB [91] , Pseudomonas fluorescens Pf0-1 carrying a functional TTSS [57] and Agrobacterium tumefasciens GV3101 ( pMP90 RK ) . E . coli , and Agrobacterium were grown in low salt Luria-Bertani broth at 37°C ( E . coli ) or 28°C ( Agro ) using either liquid media or petri dishes . Pseudomonas strains were grown in either LB or King's B medium at 28°C in liquid media or petri dishes . Antibiotics concentrations ( µg/ml ) were as follows: Rifampicin 100 , Kanamycin 50 , Gentamycin 25 , Spectinomycin 50 , Chloramphenicol 50 , Tetracycline 10 , Carbenicillin 50 . Arabidopsis accessions used in this study were obtained from NASC . The fls2-1 mutant was obtained from Cyril Zipfel and the cerk1-1 mutant was a kind gift of JP Rathjen . Transgenic lines constitutively expressing HopU1 and HopAO1 were kindly provided by Jim Alfano . Turnip seeds ( Brassica rapa cv Just Right ) were purchased from Gurney's seeds ( http://gurneys . com ) . Tobacco ( Nicotiana tabacum cv petit Havana ) and tomato ( Solanum lycopersicum cv Moneymaker ) seeds were obtained from John Innes Horticultural services . Arabidopsis plants were grown in Scotts and Levington F1 modular compost in controlled environment rooms under short day cycles ( 10 h/14 h day/night and 150–200 µE/m2s ) at 22°C and 60% relative humidity and slightly watered every day from below . Tobacco , tomato and turnip plants were grown under similar conditions as Arabidopsis for 5 weeks post-germination . Plants expressing constitutively HaRxLs were generated by recombining the corresponding ORFs cloned in pDONR221 in the Gateway destination binary vector pB2GW7 [92] under the control of the CaMV 35S promoter . Constructs were transferred to A . tumefaciens strain GV3101 ( pMP90 RK ) [93] and transformed into Arabidopsis accession Col-0 by the floral dipping method . Primary transformants ( T1 ) were selected on soil containing BASTA ( Bayer CropScience , Wolfenbüttel , Germany ) and self-pollinated . The progeny of the T2 generation was observed and 3∶1 ( BASTA-resistant/BASTA-susceptible ) segregating lines were taken further . Homozygous lines were selected by examining the BASTA resistance of T3 seedlings . Three independent transgenic lines per HaRxL ( T4s ) were analysed and for simplicity we present results for two . Primary streaks of Pst-LUX complemented with the controls or HaRxLs were made from isolated colonies onto selective King's B plates and grown overnight at room temperature . Selected individual colonies were then spread with a sterile loop in solid KB plates and incubated overnight at room temperature to produce even bacterial lawns . Cells were scraped from plates with a sterile loop and suspended in 50 to 100 ml of 10 mM MgCl2 to a final OD600 of 1 . Dilution series were made from these suspensions to: spray ( OD600: 0 . 2 ) or infiltrate ( OD600 = 0 . 001 ) Arabidopsis plants , or to infiltrate tobacco ( OD600 = 0 . 01 ) , tomato ( OD600 = 0 . 001 ) or turnip ( OD600 = 0 . 001 ) plants . For tobacco and turnip , leaf panels of the third- to fifth-oldest leaves of were infiltrated by pricking the leaves with a dissecting needle and infiltrating with a blunt syringe . pEDV-HaRxLs were compared with controls on the same leaf . For tomato , leaflets of the third and fourth most recently expanded leaves were used . Concentrations of other bacterial pathogens used in this work are stated on the corresponding figure legends or other sections of M and M . Hpa isolates Emoy2 and Noco2 were maintained in compatible host accessions and inoculated onto 2-week-old plants at 1 or 5×104 conidiospores/ml . After infection , plants were covered with a transparent lid to maintain high humidity ( 90–100% ) conditions in a growth chamber at 16°C for 7 days in short day ( 10 h/14 h day/night ) cycle . To increase the ratio pathogen/host biomass for gene expression analysis , plants were sprayed with the conidiospores solution ( or water as control ) were kept uncovered in low humidity ( 60% ) . Arabidopsis plants of all 12 different accessions were grown in arrays of five individual cells and shuffled randomly before inoculation . Five four-week-old plants of each accession were sprayed with Pst-LUX bacterial suspensions ( 2×108 cfu/ml , 0 . 03% ( v/v ) Silwet L-77 ) carrying pEDVs encoding for control or HaRxL proteins . Spraying was done using an airbursh system attached to a compressor ( GS , model AS18 ) . About 3 mls of bacterial suspension was used per five plants at a pressure of 10–12 psi . Sprayed plants were kept under a transparent lid to keep high humidity conditions . At three days post-spraying , sets of whole five plants were placed in an ultra low light CCD camera ( Photek , www . photek . com ) . Photons emitted per second were scored per plant and referred to the whole plant fresh weight to account for the foliar area . The average level of Photon Counts per Second per gram of Fresh Weight was obtained for each Pst-LUX EDV-delivered protein and the ratio versus the control was scored on each accession ( Figure 2 ) . To verify the correspondence between increase on photon emission and bacterial population growth , leaf discs were sampled from the five plants sprayed to generate at least 6 technical replicates . The leaves were surface sterilized ( 30 s in 70% ethanol , then 30 s sterile distilled water ) . Traditional growth curve assays were performed as described [94] . Total RNA was extracted from three-week-old HaRxL overexpressing lines ( T4 generation ) using the RNeasy Plant Mini Kit ( Qiagen , Hilden , Germany ) . 1 µg DNAse-treated total RNA was reverse transcribed using the QuantiTect Reverse Transcription Kit ( Qiagen ) . For semi-quantitative RT-PCR 1 µl of cDNA was used per reaction and amplified with an initial denaturation step at 95°C for 3 minutes followed by 23 cycles with the following conditions: 45 s at 94°C , 30 s at 55°C and 30 s at 72°C . In a last cycle a final elongation step at 72°C for 5 min was added . PCR products were separated on 1 . 5% TAE agarose gels . To control the equal amount of cDNA in every reaction the Actin2 gene ( At3g18780 ) was used . The specificity of the primers for amplification of the HaRxLs transcripts was tested on pDONR221 clones harboring the corresponding HaRxLs . In case of control RT-PCRs on Arabidopsis Col-0 wild type plants cDNA , no unspecific amplification using the HaRxLs' primers was observed . The sequences of primers used in this study are available on request . ROS released by the leaf tissue in response to flg22 was measured using a chemiluminescent assay [95] . Briefly , leaf discs ( 0 . 38 cm2 ) from Col-0 , fls2-1 mutant , transgenic lines expressing a given 35S-HaRxL lines , and control lines were sampled . At least 24 leaf discs from four five-week-old plants per accession/line were sampled and floated for 14–16 h in sterile distilled water in a 96 flat-bootom white multiwell plate ( Greiner Bio-One ) kept in the dark . ROS production in response to flg22 was measured by replacing the water by 100 ul of a working solution of Luminol ( 34 µg/ml final concentration , Sigma ) , Horseradish Peroxidase ( 20 µg/ml final concentration , Sigma ) and flg22 ( 100 nM final concentration , Peptron , South Korea ) . The plate was immediately introduced in a Luminometer ( Varioskan , Thermo Scientific ) and photon counts were recorded every 40 seconds for at least 40 minutes . Leaves of five-week-old plants were hand-infiltrated at the bottom of the leaf area with 5×107 cfu/ml ( OD600 = 0 . 05 ) Pst-ΔCEL suspensions . Bacteria complemented with control or HaRxL proteins were infiltrated in different plants of the same set . Twenty leaf samples were taken 12 to 14 h after inoculation at the top of the infiltrated area to avoid visualization of mechanical damage induced callose . Leaf discs were cleared 2 times ( 1 h ) in 96% ethanol and then re-hydrated in ethanol/water series ( 70% , 50% , 30% ) per 30 min each . Leaf disc were floated in water 1 h and then transferred to a solution of 0 . 01% ( w/v ) Aniline Blue ( Gurr , BHD , England ) in K2HPO4 Buffer ( 150 mM pH 9 . 5 ) for 1 h . The samples were then transferred to Glycerol 60% ( v/v ) and mounted for observation with a Leica DM R fluorescence microscope using UV light and I3 filter ( A4-UV ) . Pictures were taken with a Leica DFC 300 FX Digital Camera . Images were analyzed and callose dots quantified using Image J software and an in house written Macro . The same imaging system was used to visualize Hpa infection structures after staining with lactophenol trypan blue . The number of conidiophores per leaf was scored by manually scanning the abaxial and adaxial surfaces of each leaf on at least four true leaves of five-ten plants per accession/transgenic line analyzed . Five-week-old plants were infiltrated with 1×108 cfu/ml ( OD600 = 0 . 1 ) P . fluorescens Pf0-1 carrying pVSP61-AvrRPM1 . To be able to detect HR symptoms the bacteria had to be grown in plates . Leaf discs were collected right after infiltration using a cork borer number 4 . Twenty-four leaf discs were collected per transgenic line and shacked in falcon tubes with 45 ml distilled water for 1 h at room temperature . Four leaf discs were transferred multi-well plates with 2 ml distilled water . The level of ion leakage caused by AvrRPM1 recognition by RPM1 in the Col-0 background was detected as an increment in conductivity . This was measured with a Conductivity meter ( Horiba Twin B-173 ) every 60 min over 14 h on at least 6 technical replicates per transgenic line . To verify the accumulation proteins corresponding to the AvrRPS4-effector fusions in Pseudomonas and its secretion by the TTSS , the strains complemented with Hpa HaRxLs were grown overnight at 28°C in 25 ml liquid LB media to a final OD600 = 0 . 3 , centrifuged , washed twice with 10 mM MgCl2 and re-suspended in 20 ml minimal media ( hrp-inducing minimal medium , MM ) [50 mM potassium phosphate buffer , 7 . 6 mM ( NH4 ) 2SO4 , 1 . 7 mM MgCl2 , 1 . 7 mM NaCl , pH 6 . 0 , with 20 mM glucose added] . The cultures were incubated at 22°C at 200 rpm on a rotator shaker to a final OD600 of 0 . 5 . The pellet and supernatant fractions were separated by centrifugation at 5 , 200× g for 15 min at 4°C . The pellets were re-suspended in 300 ul of bacterial protein extraction buffer [100 mM NaCl , 25 mM Tris-HCl pH 8 , 10% glycerol , 10 mM DTT and 1× protease inhibitors cocktail ( Complete EDTA-free tablets , Roche ) ] , sonicated 3 times ( 10 s ) , and centrifuged at 12 . 000 rpm for 5 minutes . Then 100 µl of supernatant were taken and 25 µl of SDS loading buffer were added . Samples were boiled for 5 minutes at 96°C before loading . The supernatant of the 20 ml MM culture was filtered through a 0 . 2 µm pore filter and concentrated using centricon YM-10 columns ( Amicon Bioseparation ) by sequential centrifugations of 30 min at 5000 g at 4°C . The process was repeated several times . Proteins in the final 2 ml of concentrated supernatant were precipitated using Strataclean beads ( Stratagene ) . Five to ten µl of beads were used per ml of supernatant; incubated 10 min at 4°C inverting gently , centrifuged at 2000 g for 2 min and re-suspended in 25 ul Laemmli buffer containing 0 . 1 M NaOH . Samples were boiled for 5 minutes at 96°C and centrifuged at 12 . 000 rpm for 2 minutes before loading . The pellet fraction ( 15 µl ) and the culture fluid fraction ( 25 µl ) were analyzed by SDS–PAGE , electro blotted onto PVDF membrane ( Bio-Rad ) , and probed with horseradish peroxidase- conjugated high affinity anti–HA antibody ( Roche ) and re-probed with anti-NPTII antibody ( Upstate ) . Bands were visualized using PICO kit ( Thermo Scientific ) and imaged with Kodak scientific imaging film . | Hyaloperonospora arabidopsidis ( Hpa ) is an obligate biotroph whose population coevolves with its host , Arabidopsis thaliana . The Hpa isolate Emoy2 genome has been sequenced , allowing the discovery of dozens of secreted candidate effectors . We set out to assign functions to these candidate effectors , investigating if they suppress host defenses . We analyzed a sub-set of Hpa candidate effectors ( HaRxLs ) that carry the RxLR motif , using a bacterial system for in planta delivery . To our surprise , we found that most of the HaRxLs enhanced plant susceptibility on at least some accessions , while few decreased it . These phenotypes were mostly confirmed on Arabidopsis transgenic lines stably expressing HaRxLs that became more susceptible to compatible Hpa isolates . Furthermore , effectors that conferred enhanced virulence generally suppressed callose deposition , a hallmark of plant defense . This indicates that the “effectorome” of Hpa comprises multiple distinct effectors that can attenuate Arabidopsis immunity . We found that many HaRxLs did not confer enhanced virulence on all host accessions , and also that only ∼50% of the effectors that conferred enhanced Pst growth on Arabidopsis , were able to do so on turnip , a non-host for Hpa . Our data reveal interesting HaRxLs for detailed mechanistic investigation in future experiments . | [
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] | 2011 | Multiple Candidate Effectors from the Oomycete Pathogen Hyaloperonospora arabidopsidis Suppress Host Plant Immunity |
This study aimed to assess analytical parameters of a prototype LAMP kit that was designed for detection of Trypanosoma cruzi DNA in human blood . The prototype is based on the amplification of the highly repetitive satellite sequence of T . cruzi in microtubes containing dried reagents on the inside of the caps . The reaction is carried out at 65°C during 40 minutes . Calcein allows direct detection of amplified products with the naked eye . Inclusivity and selectivity were tested in purified DNA from Trypanosoma cruzi stocks belonging to the six discrete typing units ( DTUs ) , in DNA from other protozoan parasites and in human DNA . Analytical sensitivity was estimated in serial dilutions of DNA samples from Sylvio X10 ( Tc I ) and CL Brener ( Tc VI ) stocks , as well as from EDTA-treated or heparinized blood samples spiked with known amounts of cultured epimastigotes ( CL Brener ) . LAMP sensitivity was compared after DNA extraction using commercial fiberglass columns or after “Boil & Spin” rapid preparation . Moreover , the same DNA and EDTA-blood spiked samples were subjected to standardized qPCR based on the satellite DNA sequence for comparative purposes . A panel of peripheral blood specimens belonging to Chagas disease patients , including acute , congenital , chronic and reactivated cases ( N = 23 ) , as well as seronegative controls ( N = 10 ) were evaluated by LAMP in comparison to qPCR . LAMP was able to amplify DNAs from T . cruzi stocks representative of the six DTUs , whereas it did not amplify DNAs from Leishmania sp , T . brucei sp , T . rangeli KPN+ and KPN- , P . falciparum and non-infected human DNA . Analytical sensitivity was 1x10-2 fg/μL of both CL Brener and Sylvio X10 DNAs , whereas qPCR detected up to 1x 10−1 fg/μL of CL Brener DNA and 1 fg/μl of Sylvio X10 DNA . LAMP detected 1x10-2 parasite equivalents/mL in spiked EDTA blood and 1x10-1 par . eq/mL in spiked heparinized blood using fiberglass columns for DNA extraction , whereas qPCR detected 1x10-2 par . eq . /mL in EDTA blood . Boil & Spin extraction allowed detection of 1x10-2 par . eq /mL in spiked EDTA blood and 1 par . eq/ml in heparinized blood . LAMP was able to detect T . cruzi infection in peripheral blood samples collected from well-characterised seropositive patients , including acute , congenital , chronic and reactivated Chagas disease . To our knowledge , this is the first report of a prototype LAMP kit with appropriate analytical sensitivity for diagnosis of Chagas disease patients , and potentially useful for monitoring treatment response .
Chagas disease , caused by the parasite Trypanosoma cruzi , remains a major concern in 21 endemic countries of Latin America , where infection is acquired mainly through the triatomine insect vector . Due to migration movements , it has spread over other continents , with 6 to 7 million people estimated to be infected . T . cruzi infection can also be transmitted by blood transfusion , the trans-placental route causing Congenital Chagas disease , oral contamination , organ transplantation and laboratory accident [1] . Two disease stages can be distinguished and the strategies for diagnosis are stage-dependent . Firstly , a short acute stage occurs with patent parasitemia that can be detected using conventional parasitological techniques , such as parasite microscopic observation in blood smears or microhematocrite , xenodiagnosis and hemoculture . However , these methods usually lack sensitivity and are operator dependent , and the last two mentioned techniques are cumbersome and their results can be acquired only several weeks after sample collection [2] . In a majority of acute cases , symptoms are not evident and thus the infection mostly goes undiagnosed; it enters in an indeterminate chronic period that may span life-time in around 70% of cases . In the remaining 30% , chronic stage leads to cardiomyopathy and/or digestive megasyndromes , causing death if untreated . As in the chronic phase , parasitemia is intermittent and low , diagnosis is largely made by serological tests . Due to the antigenic variability of the parasite , WHO’s guidelines recommend to perform at least two serological assays based on distinct antigen sets , which must agree for a conclusive diagnosis [1] . The different transmission modes , the disease phases and the high genetic variability of the parasite increase the difficulties of making diagnostic kits with most appropriate markers for the diverse Chagas disease epidemiological settings . Nucleic acid amplification strategies have opened new options to detect T . cruzi infection and evaluate anti-parasitic chemotherapy . Diagnostic assays for Chagas disease need improvement . The development of diagnostic test for the following areas have been identified as a priority: acute phase , including oral and congenital transmission and monitoring of anti-parasitic treatment response [2 , 3 , ] . The use of Loop-mediated isothermal amplification ( LAMP ) of DNA has been proposed as an outstanding approach to bridge some of these gaps [3] . LAMP is a platform developed by Eiken Chemical Company of Japan ( http://www . eiken . co . jp/en ) . This technology detects known genes from different pathogens [4] [5] [6] . It has the following characteristics: ( 1 ) only one enzyme is used and the amplification reaction proceeds under isothermal conditions [7] [8] ) ; ( 2 ) extremely high specificity because of the use of four primers recognizing six distinct regions on the target; ( 3 ) high amplification efficiency , with DNA being amplified 109−1010 times within 15–60 minutes of incubation; and ( 4 ) it produces tremendous amounts of amplification product , making simple visual detection possible [8] [9] . Due to the above mentioned characteristics , LAMP can be performed in basic laboratories without the need for specialized infrastructure and it is appropriate for field applications and point-of-care diagnosis . Several of these kits are quite mature , such as the Tuberculosis and Malaria assays which are already commercialized Loopamp Assays . LAMP Assays—HUMAN Diagnostics Worldwide . Available: https://www . human . de/products/molecular-dx/isothermal-amplification/lamp-assays/ ) A LAMP method for detection of T . cruzi DNA was previously designed based on the 18S ribosomal RNA ( rRNA ) gene and evaluated in DNA samples extracted from internal organs of triatomine vectors [10] . However , its low analytical sensitivity ( 100 fg per reaction tube ) has not allowed its application to diagnosis of Chagas disease in humans . LAMP tests based on amplification of multicopy repetitive sequences , such as the mobile element RIME of T . brucei [11] and the satellite DNA sequence of T . vivax [12] have been used to improve sensitivity . Accordingly , a novel LAMP assay based on the highly repetitive satellite DNA sequence of T . cruzi was developed to create a prototype kit for detection of this parasite in human blood . The present study aimed to assess the analytical performance of this kit prototype on DNA extracted from EDTA-treated as well as from heparinized human blood and explore its performance to detect T . cruzi DNA in blood samples from Chagas disease patients .
Informed written consent was obtained from all healthy donors and Chagas disease patients included in the study before blood collection , after permission of the IRB of the participating institutions , in agreement with argentine legislation in force ( Blood Donation Law N° 22990 , Res . N°1409/15 ) Peripheral blood samples from a total of 23 well-characterised Chagas disease patients and 10 seronegative controls were tested by LAMP and qPCR . Four clinical groups with T . cruzi infection were evaluated , namely Group CI: Samples from five newborns/neonates born to Chagas disease women; Group AI-TXRI: samples from three transplanted seronegative receptors of organs from a seropositive donor that acquired acute T . cruzi infection; Group CCD: samples from ten chronic Chagas disease patients; Group RCD: samples from five chronic Chagas disease patients undergoing clinical reactivation due to immunosuppression after organ transplantation . All cases and controls were admitted at Health Centers of Argentina . T . cruzi stocks belonging to the six discrete typing units ( DTUs ) [13] were used in the present study . Sylvio X10 ( Tc I ) and CL-Brener ( Tc VI ) are available at INGEBI since year 2009 and their identity is periodically assessed by multiplex Real Time PCR [14] . JG1 ( Tc II ) , M6241 ( Tc III ) and M4167 ( Tc IV ) were kindly provided by Dr Constança Britto and Otacilio Moreira ( Instituto Oswaldo Cruz , Rio de Janeiro , Brasil ) and PAH179 ( Tc V ) by Dr Patricio Diosque ( Instituto de Parasitología Experimental—IPE , University of Salta , Salta , Argentina ) . Strains were cultured using LIT medium and DNAs were purified using phenol-chloroform extraction followed by ethanol precipitation . DNAs from Leishmania sp . and T . rangeli were kindly provided by Dr . Concepción Puerta from Pontificia Universidad javeriana—PUJ University , Bogotá , Colombia and DNA from T . brucei sp . and P . falciparum by Dr Yasuyoshi Mori from Eiken Chemical Company , Japan . All DNAs were measured with microvolume UV-Vis spectrophotometer ( Nanodrop 1000 , Thermo Scientific , with nd_1000-v . 3 . 8 . 1 software ) . DNA was extracted from EDTA blood and heparinized blood samples , using two different preparation procedures: a ) Blood based-DNA for LAMP was obtained from 200 μL of EDTA-blood using the High Pure PCR Template Preparation Kit with packed fiberglass filter tubes , cell lysis buffer and proteinase K ( RAS extraction kit , Roche Applied Sciences , Mannheim , Germany ) with one additional step prior to the addition of 100 μL of elution buffer , which consists of spinning the columns after the second wash to eliminate any traces of isopropanol and b ) a Boil & Spin rapid procedure modified according to the type of blood sample: b1 ) Boil & Spin of EDTA-blood samples: 200 μL of blood were mixed with 200 μL of 0 . 5% SDS in double distilled water in a 1 . 5 mL microtube with screwcap and o-ring by vortex ( 10 seconds ) , then heated in a thermoblock at 95°C for 5 min . The tube content was spun down for 5 minutes at the maximum speed ( 13 , 300 rpm ) on a bench top centrifuge . The supernatant was pipetted into a new labeled 1 . 5 mL flat cap microtube , ready to use immediately or stored at −20°C for up to 48 h , prior to use in the LAMP reaction , with only one freeze-thaw cycle before testing . b2 ) Boil & Spin of heparinized blood samples: 300 μL of blood and 15 μL of 10% SDS in double distilled water were thoroughly mixed in a 1 . 5 mL microtube with screwcap and o-ring by vortex ( 30 seconds ) . 100 μL of the solution were withdrawn , transferred to a new tube containing 400 μL of sterile water , then heated in a thermoblock at 90°C for 10 minutes . The tube content was spun down for 3 minutes at maximum speed ( 13 , 300 rpm ) . The supernatant was pipetted into a new labeled 1 . 5 mL flat cap microtube and the previous step was repeated once . The supernatant was used immediately for the LAMP reaction . Blood based-DNA for qPCR was obtained from 200 μL of EDTA-blood using the the RAS extraction kit as detailed for LAMP assays . This product is based on the nucleic acid amplification method , LAMP , developed by Eiken Chemical Co . , Ltd . Japan , using as molecular target the repetitive satellite DNA sequence of T . cruzi . The primer sequences were designed after alignment analysis of selected database sequences to have a relatively well-conserved sequence recognized in all six described DTUs . The nucleotide sequence of the primers used in the study are not provided , since the assay is being developed into a commercial product . T . cruzi Loopamp kits can be obtained from Eiken ( http://www . eiken . co . jp/en/inquiry . html . ) . The reaction tube contains strand displacement Bst ( Bacillus stearothermophilus ) DNA polymerase , deoxynucleotide triphosphates ( dATP , dCTP , dGTP and dTTP ) , calcein and T . cruzi-specific primers . These reagents are in dried form on the inside of the cap of the reaction tube and are stable for one year at 30°C . A negative control ( NC , distilled water ) is provided in the kit . The positive control was 1fg/μL of DNA from CL-Brener stock ( Tc VI ) . The LAMP reaction was performed as follows: 30 μL of sample DNA extract was dispensed into each LAMP tube , and the cap was closed . Each tube was flicked down to collect the solution at the bottom and placed upside down during two minutes to reconstitute the dried reagent . It was inverted five times to mix the content followed by a spin down . Incubation of the reaction was carried out at 65°C for 40 minutes for isothermal amplification , followed by a step at 80°C for five minutes for enzyme inactivation using different devices: 1 ) Rotor Gene 6000 thermocycler ( Corbett Life Science , Cambridgeshire , UK ) ; Perkin-Elmer 9600 ( Thermofisher , USA ) and Genie III Fluorimeter Instrument ( Optigene , Horsham , West Sussex , UK ) . The results of amplification were visualized using different strategies: i ) Real-time fluorescence data was obtained on the Rotor Gene 6000 FAM channel ( excitation at 470 nm and detection at 510 nm , auto-optimisated gain level -2 , 33 after 40 cycles of 60 seconds each at 65°C to achieve an isothermal reaction followed by a hold on 80°C during 5 minutes ) ii ) Genie III Instrument ( excitation at 470 nm , detection at 510 nm , low gain level 4 , specific for calcein; the run profile settings for amplification were 65°C , 40 min . To end reaction , the anneal settings starts at 98°C with a ramp rate of 0 , 06°C/second to reach 80°C during 5 minutes in order to inactivate the enzyme iii ) visualization of fluorescence by the naked eye iv ) in some cases , 1 . 2% agarose gel electrophoresis with TAE ( Tris-acetate with EDTA ) , ethidium bromide and GelPilot loading dye 5x , final dilution 1x , run at 80 V ( Qiagen , Hilden , Germany ) was carried out to corroborate the typical ladder profile of multiple bands of LAMP products [15] . The LAMP method was compared to a standardized qPCR assay that consisted of a duplex qPCR with TaqMan probes targeted to T . cruzi satellite DNA and Internal Amplification control ( IAC ) , as described by Duffy and coauthors [16] on the basis of the T . cruzi primer and probe sequences published by Piron and coauthors [17] , and validated following international guidelines [16 , 18] . The qPCR reactions were carried out in duplicates with 5 μL of eluted DNA in a final volume of 20 μL containing 2X FastStart Universal Probe Master Mix ( Roche Diagnostics GmbHCorp . , Mannheim , Germany ) , 10 μM primers cruzi 1 and cruzi 2 and probe cruzi 3 [19]; 5 μM primer IAC FW , primer IAC Rv and probe IAC-Tq [14] . Cycling conditions were a first step of 10 minutes at 95°C followed by 40 cycles at 95°C for 15 seconds and 58°C for 1 minute . The amplifications were carried out in an ABI7500 ( Applied Biosystems , Foster City , CA , USA ) or a Rotor-Gene 6000 ( Corbett Life Science , Cambridgeshire , United Kingdom ) thermocycler equipments . All reactions included a strong positive control ( SPC ) : 10 fg/μL of CL Brener DNA and a weak positive control ( WPC ) : 1 fg/μL of CL-Brener DNA .
Analytical sensitivity was estimated on ten-fold serial dilutions done in triplicate from three independent DNA samples of Sylvio X10 ( Tc 1 ) and CL Brener ( Tc VI ) stocks ( Fig 3 ) . The prototype LAMP kit detected T . cruzi DNA at concentrations ≥ 1 x 10−2 fg/μL ( 0 . 3 fg per test ) in both CL Brener and Sylvio X10 stocks . DNA samples spanning 1 fg/μL to 1 x 10−3 fg/μL were also assayed by qPCR for comparative purposes ( S2 and S3 Figs ) . The qPCR detected up to 1 x 10−1 fg/μL of CL Brener DNA ( S2 Fig ) and 1 fg/μL of Sylvio X10 DNA , whereas 1 x 10-1fg/μL of the latter was detectable in only one aliquot ( S3 Fig , aliquot A1 ) . The analytical sensitivity of LAMP was tested in EDTA and heparinized-blood samples spiked with known parasite loads ( par . eq . /mL ) . EDTA blood samples spiked with CL Brener DNA showed positive LAMP results when using the RAS extraction kit ( 6 x 10−4 par . eq . per test; Fig 4A ) and the Boil & Spin preparation method ( 3 x 10−4 par . eq . per test; Fig 4B ) . The qPCR was carried out in spiked EDTA blood samples following the standardized procedure , which gave a sensitivity of 1 x 10−2 par . eq . /mL ( S1 Table ) A panel of peripheral blood specimens belonging to Chagas disease patients ( N = 23 ) , as well as seronegative controls ( N = 10 ) were evaluated by LAMP in comparison to qPCR . LAMP allowed detection of T . cruzi DNA in all congenital Chagas disease ( CI ) , seronegative receptors of organs from seropositive donors with acute infection ( AI-TXRID ) and reactivated Chagas disease ( RCD ) patients , in agreement with qPCR positivity ( Table 2 ) . However , in a panel of samples from ten chronic Chagas disease patients ( CCD ) , LAMP detected T . cruzi DNA in four , whereas qPCR was positive in three , suggesting higher sensitivity of LAMP with respect to qPCR . LAMP gave negative results in samples from ten seronegative patients in agreement with qPCR results ( Table 2 , NI ) . Examples of the tested cases are illustrated in Fig 6 ( LAMP results ) and S5 Fig ( qPCR results ) .
Clinical molecular diagnosis of Chagas disease is important for: ( i ) early diagnosis of congenital transmission in newborns when presence of maternal anti-T . cruzi antibodies may deliver false positive results [20] , ( ii ) early detection of infection in transplant receptors of organs from a Chagas positive donor [21] , ( iii ) monitoring of parasite reactivation in chronically infected patients immune-suppressed due to organ transplantation [22] or AIDS [23] , and ( iv ) the evaluation of new treatments in clinical trials , because detection of serological negative conversion in seropositive patients showing a favorable treatment outcome is impractical from a study time perspective [24] [25] . This work aimed to assess the performance of a LAMP kit prototype targeted to satellite T . cruzi DNA , a highly repetitive and conserved sequence in all characterized T . cruzi strains . The prototype kit has the advantage of using reagents in dried form on the inside of the cap of the reaction tubes . Moreover , the use of calcein allows direct visualization of amplification by the naked eye . Calcein is included in the reaction tubes in a quenched state , bound to manganese ions . Once the LAMP reaction starts , pyrophosphate ions that are generated bind to the manganese ions , releasing calcein and generating fluorescent light . Furthermore , the presence of magnesium ions in the buffer system enhance calcein fluorescence [9] . Analytical sensitivity and specificity of the kit prototype was carried out using purified DNA from different parasite stocks representative of the six T . cruzi DTUs , as well as from human blood samples anti-coagulated with EDTA or with heparin and spiked with known quantities of T . cruzi cells . Furthermore , the prototype was evaluated in a blind panel of peripheral blood samples from well characterized Chagas disease patients at different stages and with different clinical manifestations , as well as seronegative donors as non-infected controls ( Table 2 and S5 Fig ) . The LAMP kit was able to amplify DNA from all tested T . cruzi stocks by the naked eye and by Real Time detection of fluorescence in a Rotor Gene Corbett Thermocycler ( Table 1 ) . Analytical sensitivity was calculated for Tc I and Tc VI stocks , among other parasite strains , and was higher than the analytical sensitivity observed with the same stocks using standardized qPCR based on the same target [16] . The kit does not intend to discriminate between DTUs . Variations in Cts among T . cruzi stocks can be explained due to the heterogeneity in copy numbers of satellite repeats [19] , however sensitivity was high for all tested strains . Indeed , satellite DNA has been successful as a molecular target for LAMP assays to detect infections by T . vivax [12] and to implement a direct dried blood based diagnostic tool for Human African trypanosomiasis [11] [26] . LAMP was specific for T . cruzi DNA since negative findings were obtained even with high concentrations ( up to 1 pg/μL ) of DNAs from T . rangeli ( KPN+ and KPN- stocks ) , P . falciparum , several Leishmania species , and T . brucei sub-species . The use of EDTA as anticoagulant proved to be suitable in our study , although it has not been recommended for LAMP , due to the fact that EDTA competes for manganese ions with the pyrophosphate ions generated once the reaction starts [27] . Nevertheless , LAMP analysis carried out using DNA obtained from EDTA-blood samples has also been reported [26] . Since an easy and quick detection is desirable for application of a LAMP prototype in point-of-care diagnosis , EDTA-blood samples are appropriate since they are routinely withdrawn in most health care centers; i . e . for hemogram reports . In fact , we detected up to 1 x 10−2 par . eq . /mL in extracts from spiked EDTA blood samples using fiberglass commercial columns as well as after Boil & Spin , in accordance to the sensitivity detected by qPCR . In contrast , when LAMP experiments were performed in heparinized spiked blood using RAS columns , sensitivity was one order below . Moreover , LAMP results obtained using 30 μL of Boil & Spin DNA preparations from heparinized blood could only be detected after agarose gel electrophoresis , whereas observation of fluorescence with the naked eye was not possible: reddish appearance of the contents of tubes hampered visualization ( Fig 5B ) . Direct visualization was possible only after diluting the DNA extracts at least 1:100 times . However , the analytical sensitivity in those diluted samples was poor in comparison to the detection limit achieved using the other methods; it was only 1 par . eq . /mL ( S4 Fig ) . Consequently , the Boil & Spin procedure used in our study needs to be revised for improvement . It performed well in other Loopamp kits , such as those developed for Pan/ P . falciparum ( 27 , 28 ) and Trypanosoma brucei detection ( Standard operating procedures for the Loopamp Trypanosoma Available: https://www . finddx . org/wp-content/uploads/2016/06/HAT-LAMP-SOP_13JUN16 . pdf ) . The analytical sensitivity of this LAMP assay was superior to that previously obtained using a LAMP procedure based on the 18S rDNA gene [10] . The mentioned study was done using DNA from Tulahuen strain ( Tc VI ) , and analytical sensitivity was 100 fg per test , using a Real-time turbidimeter , detection under UV light and direct visualization by the naked eye after 60 minutes of incubation . To our knowledge , this is the first report of a LAMP kit with similar analytical sensitivity than Real Time PCR . It was validated using the same T . cruzi strains [16 , 28] and using different methods for visualization of amplification . Our data provide evidence of the usefulness of this LAMP kit for molecular diagnosis of Chagas disease . Prospective analysis of clinical specimens will allow establish its performance in different epidemiological and clinical scenarios , such as early diagnosis of congenital infection , POC detection of acute infections due to oral transmission or in seronegative recipients of organs from seropositive donors , early detection of reactivation due to immunosuppression due to organ transplantation or AIDS [3 , 20 , 21 , 22 , 23 , 29] . Furthermore , the persistence of positive LAMP results in patients under etiological treatment could be useful to assess treatment failure [3 , 24 , 25 , 30 , 31 , 32] . | Trypanosoma cruzi , a parasite transmitted to humans from hematophagous insects , causes Chagas Disease , a Neglected Tropical Disease with public health impact , affecting 7 million people in Latin America . Although mainly related to low income populations inhabiting rural environments , migrations have conveyed Chagas Disease to urban areas of endemic and non-endemic countries . It often presents non-specific symptoms , and direct , low cost microscopy-based diagnosis only detects acute infections , missing a high proportion of cases . Serology is the “gold standard” diagnostic technique for chronic stages and needs the concordance of at least two different assays to confirm infection . In this context , we aimed to evaluate the analytical sensitivity and specificity of a prototype kit based on a novel and rapid molecular biology reaction , named Loop mediated isothermal amplification ( LAMP ) , using standardized Real Time PCR as a comparator . To our knowledge , this is the first LAMP prototype kit with an analytical performance appropriate for human diagnosis of Chagas disease and potentially useful for monitoring treatment response . Its simple handling using basic laboratory devices will enable point-of-care diagnosis and screening for congenital infection at birth as well as early detection of acute infections due to oral contamination . | [
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"disease... | 2017 | Analytical sensitivity and specificity of a loop-mediated isothermal amplification (LAMP) kit prototype for detection of Trypanosoma cruzi DNA in human blood samples |
Intracellular calcium cycling is a vital component of cardiac excitation-contraction coupling . The key structures responsible for controlling calcium dynamics are the cell membrane ( comprising the surface sarcolemma and transverse-tubules ) , the intracellular calcium store ( the sarcoplasmic reticulum ) , and the co-localisation of these two structures to form dyads within which calcium-induced-calcium-release occurs . The organisation of these structures tightly controls intracellular calcium dynamics . In this study , we present a computational model of intracellular calcium cycling in three-dimensions ( 3-D ) , which incorporates high resolution reconstructions of these key regulatory structures , attained through imaging of tissue taken from the sheep left ventricle using serial block face scanning electron microscopy . An approach was developed to model the sarcoplasmic reticulum structure at the whole-cell scale , by reducing its full 3-D structure to a 3-D network of one-dimensional strands . The model reproduces intracellular calcium dynamics during control pacing and reveals the high-resolution 3-D spatial structure of calcium gradients and intracellular fluxes in both the cytoplasm and sarcoplasmic reticulum . We also demonstrated the capability of the model to reproduce potentially pro-arrhythmic dynamics under perturbed conditions , pertaining to calcium-transient alternans and spontaneous release events . Comparison with idealised cell models emphasised the importance of structure in determining calcium gradients and controlling the spatial dynamics associated with calcium-transient alternans , wherein the probabilistic nature of dyad activation and recruitment was constrained . The model was further used to highlight the criticality in calcium spark propagation in relation to inter-dyad distances . The model presented provides a powerful tool for future investigation of structure-function relationships underlying physiological and pathophysiological intracellular calcium handling phenomena at the whole-cell . The approach allows for the first time direct integration of high-resolution images of 3-D intracellular structures with models of calcium cycling , presenting the possibility to directly assess the functional impact of structural remodelling at the cellular scale .
The cardiac intracellular calcium ( Ca2+ ) handling system is responsible for the control of cellular and organ contraction associated with the heartbeat [1] . Malfunction of this system can directly affect the ability of the heart to work effectively as a pump–reducing cardiac output and potentially leading to mortality . Moreover , abnormal Ca2+ handling dynamics has been increasingly linked to the development of arrhythmogenic triggers in the myocardium [2–4] , through two-way coupling between the electrical and Ca2+ handling systems . Ca2+ handling in cardiac myocytes is regulated by multiple ion channels , pumps and transporters . During the cellular electrical action potential ( AP ) associated with excitation , an influx of Ca2+ through opening of the voltage-gated L-type Ca2+ channels ( LTCCs—carrying flux JCaL ) triggers a significant release of Ca2+ from the intracellular Ca2+ store ( the sarcoplasmic reticulum , SR ) through opening of the Ryanodine Receptors ( RyRs—carrying flux Jrel ) . This process is referred to as Ca2+-induced- Ca2+-release ( CICR ) [5] . The Ca2+ released from the SR binds with the contractile myofilaments in the bulk intracellular space—the cytoplasm—facilitating cellular contraction . During relaxation , the Ca2+ concentration in the SR is restored from the cytoplasm through the flux Jup , carried by the SERCA Ca2+ pump; Ca2+ is removed from the cell primarily by the sodium- Ca2+-exchanger ( NCX–carrying flux JNaCa ) and also through the membrane Ca2+ pump ( PMCA—carrying flux JpCa ) . This completes the cardiac cellular Ca2+ cycle associated with electrical activation and contraction . The LTCCs are found in clusters along the surface membrane and transverse-tubules ( TTs ) —invaginations of the sarcolemmal membrane responsible for delivering the AP into the interior of the cell . PMCA and NCX are distributed on the surface sarcolemmal membrane and the TTs . On the SR membrane there are clusters of RyRs [6] and continuously distributed SERCA proteins . Critical for CICR is the spatial arrangement of the TT network and the junctional portion of the SR ( jSR ) to form a microdomain , termed the dyad , to co-localise LTCCs and RyRs on the two membranes . There are thousands of dyads within a cell , and the bulk of the SR forms a network like structure ( nSR ) connecting the distributed jSRs . Employing serial block face scanning electron microscopy ( SBF-SEM ) we have previously determined the 3-D organisation of the TTs and SR in a large animal model , the sheep , revealing details of the network organisation and jSR morphology and importantly the relationship with the TT network to form dyads [7] . Multiple groups have demonstrated that the TTs and related structures are remodelled in disease conditions [7–14] and we have additionally shown that the SR structure is also perturbed in heart failure [7] . These observations highlight the potential importance of structure-function relationships at the sub-cellular scale in Ca2+ dynamics associated with cardiac arrhythmia and perturbed contraction; the precise nature and impact of these relationships requires further investigation . Computational modelling is a complementary approach to experimental research , and has been successfully applied to provide insight into numerous cardiac phenomena , such as pacemaker activity ( e . g . , [15] ) and the functional impact of pathophysiological ion channel remodelling ( e . g . , [16] ) . Recently , computational models which explicitly account for spatio-temporal Ca2+ dynamics have been developed and successfully reproduced phenomena including Ca2+ transient alternans and spontaneous Ca2+ release [17–41] . Such models are in general idealised ( with an idealised structure and dyad distribution ) , but some have used imaging data to distribute RyRs throughout the cell or cell-portion [17 , 18 , 22 , 39]; Other models have been created of small regions of the cell ( e . g . the volume surrounding a single TT or even a single dyad ) and have integrated more detailed imaging data [29 , 31 , 32 , 37] . However , a spatio-temporal Ca2+ handling model which accounts for realistic SR structure , TT structure and dyad distribution at the whole-cell scale has not yet been developed and involves significant challenges . Nevertheless , the potential advantages offered by such a model–providing a method to directly study variability in structure-function relationships–remain an attractive prospect . Therefore , the aim of this study was to develop an approach to overcome the challenges of modelling spatio-temporal Ca2+ dynamics using the experimentally reconstructed 3-D structures for the TT and SR at the whole-cell scale . We demonstrate that the approaches developed are sufficient to capture spatio-temporal calcium dynamics with a realistic network SR structure and membrane fluxes distributed according to the sarcolemma/TTs . We additionally report how this model can be used to reproduce Ca2+ transient alternans and spontaneous release events during perturbed rapid pacing as a demonstration of its suitability for future research , and provide preliminary analysis of the importance of structure-function relationships underlying cardiac cellular dynamics . This model ( provided in S1 Code ) therefore provides a powerful tool to understand structure-function relationships in physiological and pathophysiological cardiac electro-mechanics .
Details of image acquisition using SBF-SEM ( FEI Quanta 250 FEG SEM equipped with a Gatan 3View ultramicrotome ) and methods for the 3-D reconstruction of the TT and SR structure have been described fully elsewhere [7] . In brief , tissue was extracted from the sheep left ventricle and immediately fixed and prepared for SBF-SEM [42] . The voxel size of the acquired data was 13 . 5 nm /pixel in the x-y plane and 50 nm in the z-direction . The region within a cardiac myocyte for which the dyad positional data was generated for this study is indicated in Fig 1A . The staining technique employed allowed clear delineation of the TT and SR features ( Fig 1A–1C ) . Segmentation of the SR subsequently allowed the network SR ( nSR ) to be distinguished from the jSR ‘patches’ . 3-D reconstruction of both the TTs and SR morphology ( [43] ) within the defined region enabled identification of jSR along TTs forming putative dyads ( Fig 1D–1F ) . The position of each dyad was marked with a sphere ( Fig 1 ) to build up a 3-D distribution grid through 357 consecutive slices . The resolution at which the images were acquired , 13 . 5nm ×13 . 5nm × 50nm , is impractical for computational modelling at the whole-cell scale . Computational grids created for numerical simulation were down-sampled to a resolution of 350nm × 350nm × 350nm following processing at the full resolution . First , the reconstruction of the SR at full resolution was smoothed and cleaned ( Fig 3A ) . This was the baseline geometry to which the down-sampled geometry was mapped . The primary challenge in modelling SR structure is due to its thin cross section: resolutions necessary to capture detailed SR structure are impractical for whole-cell simulations . To overcome this challenge , the 3-D network structure of the SR , rather than its full 3-D cross-sectional structure , was identified as the key feature to be captured in the model . Under this reduction , the SR can be approximated by a 3-D network of 1-D strands ( Fig 3C ) . The resolution of this “1-D-strand model” was down-sampled to 350nm × 350nm × 350nm , and an algorithm applied to preserve each SR voxel’s nearest-neighbours ( Fig 3C ) . The volume of each node in the 1-D strand model corresponds to the total volume of the full resolution reconstructed SR divided by the number of nodes in the 1-D strand map , and is thus not defined by the volume of a single voxel at the discretised resolution ( vvox . nsr = 0 . 00305 μm3 ) . For visualization of small portions of the cell model , the concentration distribution in the 1-D strand model was mapped back onto the full resolution reconstruction; this was impractical for visualization of the whole cell due to the computational memory demands of rendering such a large , high resolution structure . In this section , a general spatio-temporal Ca2+ handling model is described in the context of an idealised cell structure . Then , approaches to incorporating the reconstructed SR and membrane structures are discussed . Full model equations can be found in S1 Text; in this section , only the fundamental equations are given . In order to perform preliminary analysis to assess the role of intracellular structure affecting spatio-temporal Ca2+ dynamics , alternative geometries were considered for comparison to the fully detailed structural model: ( 1 ) Semi-idealised structure: The cytoplasm geometry was used to describe all of the spatially diffuse spaces and fluxes . Dyads were distributed evenly throughout the volume to match the mean inter-dyad distances in the structurally detailed model , resulting in comparable dyad densities ( Ndyads for cross section = 2182 vs 2208 for the full and semi-idealised models , respectively ) . ( 2 ) Altered dyad densities: The density of the dyad distribution ( and thus inter-dyad distances ) in the fully detailed structural model was altered , by either including additional dyads ( at junctions of the SR and TT ) or by removing dyads . ( 3 ) Altered SR diffusion properties: The diffusion coefficient in the SR was varied ( by a factor of half and a factor of two ) . Furthermore , the connectivity of the SR neighbour map was perturbed by removing some neighbours for a randomly selected set of the SR voxels .
The developed cell model reproduces properties of whole cell electrical and Ca2+ handling dynamics ( Fig 6A ) during control pacing ( basic cycle length , BCL , of 1250ms ) . The AP duration to 90% repolarisation ( APD90 ) at this cycle length is 345ms and the Ca2+ transient has an upstroke time of 20ms , magnitude of 0 . 68μM and duration to 90% of peak of 400ms . These properties fall within expected ranges for large mammal ventricular myocytes ( e . g . for the sheep–the animal model from which the structural datasets were attained [50 , 51] ) . The temporal evolution of force follows the Ca2+ transient ( Fig 6Ae ) which in turn follows the AP upstroke . Model dynamics are stable over long simulation duration times ( Fig 6B ) once steady-state is achieved . The model successfully reproduces rate dependence of the diastolic and systolic Ca2+ concentrations , exhibiting an elevation of both as pacing rate increases ( S2 Fig ) . Spatio-temporal dynamics in the cytoplasmic Ca2+ concentration associated with a single beat are shown in Fig 7 and S1 Video . The model captures the dynamics of a single Ca2+ spark ( Fig 7A ) , illustrating the rapid and non-linear decay of the Ca2+ transient as a function of distance from the centre of the dyad [17 , 28]: the peak of the Ca2+ concentration at a distance of 1 . 2 μm from the centre of the dyad was an order of magnitude smaller than at the dyad centre . A linescan along the longitudinal axis of the cell demonstrates the spatial variation in the rise and decay of the Ca2+ transient ( Fig 7B ) : temporal variation of the initiation of the upstroke of the transient results in significant spatial gradients during the initial phase of excitation; significantly smoother spatial gradients were observed during the decay phase . Snapshots of spatial Ca2+ concentration in 3-D ( Fig 7C ) and with enhanced scaling ( Fig 8A ) reveal the 3-D structure of the Ca2+ gradients associated with normal excitation . Properties of Ca2+ gradients during the different phases of the transient are determined by intracellular structure and the nature of the various fluxes controlling Ca2+ dynamics . The discrete and non-uniformly distributed dyads , heterogeneity in the dyadic cleft volume and small protein numbers ( which enhances stochastic state transitions of the RyRs and LTCCs ) , combined with the rapid transient morphology of Jrel during excitation led to a rapid upstroke of the Ca2+ transient and significant spatial heterogeneity during this initial excitation phase ( Fig 7Ci; Fig 8Ai ) . Jrel quickly terminates at around the time of the transient peak ( S4 Fig ) —the Ca2+ influx throughout the cell is significantly reduced and Ca2+ diffuses away from the dyads through the bulk cytoplasm , reducing the peaks surrounding the dyads and smoothing the gradients . Due to significant Ca2+ buffering , diffusion is slow and gradients are not eradicated ( Fig 7Ciii; Fig 8Aii , iii ) . The decay of the Ca2+ transient is slower and more spatially uniform due to the spatially and temporally continuous nature of the effluxes , JMem and JSR , ( Fig 7Civ; Fig 8Aiv ) . Subcellular heterogeneity was also observed during controlled AP clamp conditions . Comparison to the semi-idealised cell model ( which contains a uniform dyad distribution—See Methods: Semi-idealised and perturbed structure models ) reveals that the complex 3-D structure of Ca2+ gradients is largely determined by the dyad distribution ( compare Fig 8A with 8B ) . Temporal variation in the activation time of individual dyads results in significant gradients during the first few miliseconds of excitation in the semi-idealised model , but then much more uniform Ca2+ concentration was observed during the main excitation phase , compared to the fully detailed structural cell model ( Fig 8 ) . Inclusion of realistic flux distribution in the semi-idealised model enhances spatial heterogeneity but has a much smaller impact than the dyad distribution . Spatial Ca2+ gradients in the nSR were less pronounced than in the cytoplasm ( Fig 9; S1 Video ) . Nevertheless , gradients were observed during the initial excitation ( emptying ) phase , due to the distributed dyads/jSRs ( Fig 9i and 9ii ) . The spatial distribution is almost uniform at the time of maximum depletion of the SR ( Fig 9iii ) . During the refilling phase ( Fig 9iv ) , gradients were observed but are more uniform than those during the initial phase ( compare Fig 9 panel iii with v ) , comparable to the behaviour observed in the bulk cytoplasm . Fluxes acting on the cytoplasm in a single portion of the 3-D cell were analysed ( Fig 10 ) . The spatial distribution of Jup ( Fig 10A ) and JNaCa ( Fig 10B ) can be clearly seen , and correspond to the nSR and surface sarcolemma/TT structures , respectively . Spatial gradients in the magnitude of both of these fluxes are observed , as a direct result of the gradients in intracellular Ca2+ concentration ( note the spatial correspondence between the gradients in Figs 7 , 8 and 10 ) . The effect of voltage inhibition on the activity of JNaCa combined with the initial large peaks of Ca2+ in locations close to the dyads can be clearly seen by spatially distributed peaks in JNaCa in the initial phase of excitation ( Fig 10Bi ) , uniform and small fluxes during the bulk of the excitation phase , wherein the Ca2+ concentration is large ( Fig 10Bii ) , and a more spatially uniform but larger flux during the decay phase of the Ca2+ transient , corresponding to the peak of INaCa ( Fig 10Biii ) . Varying the SR diffusion parameters primarily affected the spatial-distribution of Ca2+ in the SR: Rapid diffusion in the SR facilitated equilibration of the SR Ca2+ content and reduced gradients; slower diffusion enhanced Ca2+ gradients ( Fig 17A ) . However , the impact on whole-cell dynamics was relatively minimal under the variance in diffusion coefficient of 0 . 5–2 times the baseline value ( 0 . 3 μm/ms ) considered in this study . Reduced connectivity in the SR network led to significant SR Ca2+ gradients during normal excitation , with islands of SR loading appearing during the systolic phase ( Fig 17B ) . This localised early SR loading promoted secondary spontaneous release during the AP–note the locations of secondary release correspond to the islands of SR loading .
There are numerous exemplary studies in recent years implementing spatio-temporal Ca2+ handling models in multiple dimensions ( e . g . [17–41] ) . These models have been used to mechanistically evaluate physiological and pathophysiological dynamics in the intracellular Ca2+ handling system , such as graded release [20] , RyR dynamics [23 , 40] , Ca2+ transient alternans [20 , 21 , 25 , 27 , 30] , Ca2+ waves [19 , 20 , 41] and pacemaker activity [38] , and account for varying degrees of detail of intracellular structure ( realistic RyR distribution; reconstruction of single TT; super-resolution of single dyad ) . However , no model has yet accounted for the realistic structure of the nSR , nor integrated fluxes according to realistic membrane structure , dyad distribution and nSR structure at the whole cell scale . The model developed in the present study achieves this goal , providing for the first time a framework to directly integrate imaging data on multiple structures with whole-cell modelling . A major feature of the model is the ability to account for the structure of the network SR at the whole-cell scale . Identifying the network-like structure of the model as the key feature to be captured provides a method to model Ca2+ diffusion throughout the nSR at lower resolutions than required to image this structure , through the reduction to a 3-D network of 1-D strands ( Fig 3 ) . Whereas idealised geometry based models offer ease of data interpretation and are suitable for general analysis of Ca2+ dynamics , the modelling approaches presented in this study provide a method to remove much of the uncertainty inherent to models based on idealised geometries and to directly asses how structure-function relationships are affected by variability in intracellular structure , which may be particularly relevant when considering structural remodelling associated with disease [7] . The present approach provides the first framework which allows multiple structural datasets to be directly integrated with mathematical modelling of Ca2+ dynamics without the assumptions required by idealised models to capture structural variability , which accentuate the inherent uncertainty of these models . This , therefore , potentially significantly increases the confidence of simulation data regarding variability in intracellular structure . On the other hand , models of specific regions of the cell which incorporate realistic structure can offer significant understanding of local control of EC coupling , but cannot capture whole-cell emergent properties such as the interaction of heterogeneity of these structures throughout the cell . The model presented here therefore complements those previously developed , providing a framework to investigate whole-cell dynamics underlain by real structure and heterogeneity . We note that , in support of the validation of the model , results in the present study are consistent with those of previous modelling studies where comparable . For example , Izu et al . 2006 [17] found similar results regarding the criticality of inter-dyad distances in maintaining the propagation of Ca2+ waves . Ca2+ spark hierarchy is similar to that shown in Nivala et al . 2012 [19] . The mechanism underlying Ca2+ transient alternans is consistent with the studies of Restrepo et al . 2008 [20] , Rovetti et al . 2010 [21] and Alvarez-Lacalle et al . 2015 [25] . The suitability of the model for future research was demonstrated by its application under multiple conditions . The model reproduces whole-cell and spatio-temporal Ca2+ characteristics under control pacing ( Figs 7–9 ) , rapid perturbed pacing leading to alternans ( Figs 11–13 ) , and rapid pacing leading to SR overload , spontaneous Ca2+ release and the development of single-cell triggered activity ( Figs 14–16 ) . The primary focus of the study was to develop an approach to overcome the challenges inherent in the construction of such a detailed model and to demonstrate the suitability of the model for future research , which was achieved through application of the model in normal and arrhythmic excitation conditions . The intention of the model is for future studies to incorporate multiple datasets , including those describing multiple disease conditions , to fully assess the role of variability in intracellular structure in determining potentially pro-arrhythmic dynamics; such detailed analysis was therefore beyond the scope of this study . This notwithstanding , the present study also provides for the first time detailed and high resolution ( spatial , temporal and concentration ) reconstruction of Ca2+ gradients and fluxes in 3-D in both the cytoplasm and network SR ( see Results sections: Spatio-temporal Ca2+ dynamics in the bulk cytoplasm; Spatio-temporal Ca2+ dynamics in the network SR; Evaluation of spatial distribution of fluxes in the cytoplasm ) , as well as preliminary analysis of the importance of structure underlying spatio-temporal Ca2+ dynamics and the role of SR diffusion and connectivity ( see Results sections: Intracellular Ca2+ transient alternans; Ca2+ spark hierarchy and spontaneous release events; SR Diffusion Properties ) . Comparison with the semi-idealised model revealed similarities and differences between the two levels of detail included in the model . The similarities of whole-cell characteristics and gross spatio-temporal dynamics between the two models during normal structure highlights the suitability of idealised models for general and mechanistic modelling of spatio-temporal dynamics associated with normal structure . However , multiple results presented here also emphasise the important role of structure and heterogeneity underlying the complex and fine-scale details of 3-D intracellular Ca2+ dynamics , which may be particularly important for disease models of intracellular structural remodelling . First , the complex structure of 3-D Ca2+ gradients and intracellular Ca2+ fluxes arises primarily as result of dyad distribution and intracellular structure; this complex structure does not emerge in the idealised cell model . Moreover , spatially heterogeneous distributions of the membrane and SR Ca2+ fluxes were observed despite homogeneous distribution of the maximal flux rates , as a direct result of these intracellular Ca2+ gradients . Second , the degree of disorder associated with alternans dynamics was significantly reduced in the structurally detailed model compared to the idealised model; dyad distribution significantly affects recruitment patterns and therefore spatially constrains the probabilistic nature of recruitment , with real structure reducing the phase variance associated with multiple small-amplitude cycles . Similarly , reduced density of dyad distribution led to significant failure to recruit and very small transients associated with the small amplitude cycle . These results thus add further insight to those gained in previous studies [20 , 25 , 27] . Third , the critical inter-dyad distances to maintain Ca2+ propagation are within the distribution of dyad nearest neighbour distances; whereas the overall trend of Ca2+ spark hierarchy is preserved under different dyad distributions , the propagation patterns emerging and dependence on SR Ca2+ concentration were significantly affected by dyad distribution , with whole-cell coordinated single waves requiring relatively short inter-dyad distances . Finally , the connectivity of the network SR was vital in maintaining normal Ca2+ dynamics; reduced connectivity led to failure of the SR to equilibrate , significant and heterogeneous SR loading and secondary systolic Ca2+ release . These results highlight the necessity for integrated multi-scale models which can capture realistic structure of both the intracellular and SR spaces as well as heterogeneity in dyad properties and flux distribution . For example , a combination of increased heterogeneity in dyad properties with remodelling of dyad distribution may result in highly unpredictable constraints on dynamics . TT structure , dyad distribution and SR structure have all been shown to be perturbed in both animal models of disease and patients [7–14] . Whereas many previous modelling studies have investigated remodelling in proteins and flux dynamics in the single cell ( e . g . , [16] ) , intracellular structural remodelling has only been investigated by a few computational studies and these have been idealised ( e . g . , [52] ) . Application and analysis of structurally accurate models such as the one presented in this study will therefore allow mechanistic evaluation of the role of structural remodelling in determining arrhythmogenic electrical and perturbed contractile dynamics at the cellular scale as well as structure-function relationships underlying normal cardiac excitation . Whereas the present study includes a novel mathematical model describing intracellular Ca2+ dynamics , the primary focus was on the methods to process and integrate structural datasets into modelling frameworks . To demonstrate the generalisability of these approaches , the structural model was integrated with the independent mathematical model of Nivala et al . 2012 [27] . Note that this model contains a different schematic structure ( with no subspace ) and exhibits a larger Ca2+ transient associated with excitation . The whole-cell characteristics of the integrated model were comparable to that of the Nivala et al . study , whereas the structure of intracellular Ca2+ gradients is comparable to those previously shown in the present study; Ca2+ diffusion is aided in the Nivala et al . version of the model as a result of the larger transient , and thus the quantitative comparison of the gradients reveals some differences–nevertheless , the main structure remains ( S5 Fig ) . This therefore demonstrates the generalisability of the approaches for integration with independent mathematical models and further supports the suitability for future research . For the purpose of the methodological development of the model , only a portion of the myocyte was reconstructed at high resolution and processed to form the computational grids and mapping functions used for simulation . For specific studies of variability in Ca2+ dynamics , especially those in disease where intracellular structure may be highly irregular , a larger or full-cell reconstruction would be necessary . However , it should be noted that this does not affect the fundamental model itself nor the methodological approaches for structural data processing . In the model , the membrane and SR fluxes were spatially distributed according to maps created from the reconstructions . Within this structure , the fluxes were distributed evenly and continuously . Incorporation of immuno-labelling data describing the realistic distribution of the proteins responsible for these fluxes would provide a further degree of accuracy in the cell model as well as a tool for investigation of the functional impact of changes to the distribution of these flux-carrying proteins; arbitrary heterogeneity could have been introduced into the present model but this was avoided due to the inherent uncertainty in such an approach–the purpose of this model being to remove this necessity for future research . In further simulations , we also demonstrate that redistribution of INaCa primarily to the TTs , as has been performed in other studies [24] , results in preferential flux in the TTs without significant perturbation to whole-cell dynamics ( S6 Fig ) . The model includes a restricted buffering subspace which functionally couples neighbouring dyads . The inclusion of this subspace was primarily motivated by considerations for reproducing physiological Ca2+ dynamics . Whereas the presence of pathways between the localised Ca2+ buffers may be physiologically relevant , it is not the intention of this study to make a comment and such functionality requires further investigation experimentally . We , however , note that similar constructs are present in previously developed models from other groups: in both Gaur-Rudy 2011 [28] and Voigt et al . 2014 [26] , neighbouring dyadic spaces are directly coupled . The present model contains this functional coupling but also preserves the restricted local spaces of individual dyads . This construct is not critical to the primary novelty of the developed approach–in processing and modelling with the structural datasets–as demonstrated by integration with the independent mathematical model of Nivala et al . , which does not contain this subspace ( see Discussion: Generalisation of the model ) . The dyads/jSRs were treated as point sources ( single voxels ) in the spatial geometries . Whereas heterogeneity was incorporated through functionality to vary dyadic cleft volume and protein numbers ( NRyR and NLTCC ) , the spatial structure of the dyad , including local RyR distribution , is not accounted for in the present model . A multi-scale integration method could feasibly be implemented to integrate previously developed super-resolution models of single dyads [37 , 53] , though this was beyond the scope of the present study . Similarly , the RyR dynamic model is primarily functional rather than rigorously based on experimental data describing RyR kinetics . Incorporation of a more accurate RyR model , such as the recent induction decay models [40] would also improve the accuracy and suitability for future studies . Furthermore , heterogeneity in the jSR volume and calsequestrin concentration could be included to further analyse the role of these heterogeneities underlying spatial Ca2+ dynamics . Further possible extensions to the model include segmentation of the mitochondria [39] ( the spatial distribution of the mitochondria will affect spatial Ca2+ diffusion because they effectively act as barrier to diffusion ) and subsequent incorporation of localised energetics . Furthermore , the contractile system could also be segmented for simulation of spatially localised troponin buffering and force generation , for application to understand reduced contractile force associated with heart failure , for example . A computational model of 3-D spatio-temporal Ca2+ dynamics has been created which incorporates realistic reconstructions of multiple intracellular structures , namely the network SR structure , cytoplasm volume , and fluxes distributed according to the membrane/TT and SR structures . Understanding the role of intracellular Ca2+ cycling in physiological and pathophysiological cellular dynamics is vital for mechanistic evaluation of perturbed contraction and arrhythmia associated with Ca2+ handling malfunction , and may contribute to improved treatment strategies for prevention , management and termination of life-threatening conditions . The methodological framework and model reported here provide a powerful tool for future investigation of structure-function relationships at the whole cell scale underlying physiological and pathophysiological intracellular Ca2+ handling , beyond the insight gained in this study . Full model code is provided ( S1 Code ) to facilitate realisation of the potential of future applications of these approaches . | The organisation of the membrane and sub-cellular structures of cells in the heart closely controls the coupling between its electrical and mechanical function . Computational models of the cellular calcium handling system , which is responsible for this electro-mechanical coupling , have been developed in recent years to study underlying structure-function relationships . Previous models have been largely idealised in structure; we present a new model which incorporates experimental data describing the high-resolution organisation of the primary structures involved in calcium dynamics . Significantly , the structure of the intracellular calcium store is modelled for the first time . The model is shown to reproduce calcium dynamics in control cells in both normal and abnormal conditions , demonstrating its suitability for future investigation of structure-function relationships . Thus , the model presented provides a powerful tool for the direct integration of experimentally acquired structural data in healthy and diseased cells and assessment of the role of structure in regulating normal and abnormal calcium dynamics . | [
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"... | 2017 | A computational model of spatio-temporal cardiac intracellular calcium handling with realistic structure and spatial flux distribution from sarcoplasmic reticulum and t-tubule reconstructions |
During meiotic prophase , telomeres cluster , forming the bouquet chromosome arrangement , and facilitate homologous chromosome pairing . In fission yeast , bouquet formation requires switching of telomere and centromere positions . Centromeres are located at the spindle pole body ( SPB ) during mitotic interphase , and upon entering meiosis , telomeres cluster at the SPB , followed by centromere detachment from the SPB . Telomere clustering depends on the formation of the microtubule-organizing center at telomeres by the linker of nucleoskeleton and cytoskeleton complex ( LINC ) , while centromere detachment depends on disassembly of kinetochores , which induces meiotic centromere formation . However , how the switching of telomere and centromere positions occurs during bouquet formation is not fully understood . Here , we show that , when impaired telomere interaction with the LINC or microtubule disruption inhibited telomere clustering , kinetochore disassembly-dependent centromere detachment and accompanying meiotic centromere formation were also inhibited . Efficient centromere detachment required telomere clustering-dependent SPB recruitment of a conserved telomere component , Taz1 , and microtubules . Furthermore , when artificial SPB recruitment of Taz1 induced centromere detachment in telomere clustering-defective cells , spindle formation was impaired . Thus , detachment of centromeres from the SPB without telomere clustering causes spindle impairment . These findings establish novel regulatory mechanisms , which prevent concurrent detachment of telomeres and centromeres from the SPB during bouquet formation and secure proper meiotic divisions .
Chromosome positioning changes dynamically during development and differentiation , and contributes to various chromosomal events including gene expression and DNA metabolism [1–5] . Especially during meiosis , chromosomes adopt a characteristic position called the “bouquet” arrangement , in which telomeres cluster at the nuclear periphery . The bouquet arrangement is highly conserved among eukaryotes [6 , 7] , and how it is formed and what functions it has are important questions in the field of meiosis . Studies of various organisms show that the bouquet arrangement facilitates homologous chromosome pairing [7–9] . Bouquet-defective mutants of yeasts and mammals exhibit impaired homologous chromosome pairing and phenotypes associated with the impaired pairing , such as increased non-homologous association , decreased recombination , and defective formation of the synaptonemal complex , a structure that bridges the paired homologous chromosomes [10–23] . In Caenorhabditis elegans , special chromosome regions called “pairing centers” cluster instead of telomeres , and impaired clustering of pairing centers causes similar defects [24–29] . Homologous chromosome pairing is essential for formation of chiasmata , which physically link homologous chromosomes and enable their segregation at meiosis I ( reductional segregation ) . Consistently , bouquet-defective organisms also exhibit impaired chromosome segregation [10 , 14 , 30] . Among different organisms , the fission yeast Schizosaccharomyces pombe shows the most prominent example of the bouquet arrangement . S . pombe mitotic chromosomes are positioned with their centromeres clustered at the spindle pole body ( SPB; a centrosome equivalent in fungi ) and their telomeres located away from it ( this corresponds to the “Rabl” configuration seen in other organisms ) [31] . Under nitrogen-starved conditions , S . pombe cells enter meiosis through cell conjugation . Around this period , telomeres cluster at the SPB and centromeres become detached from it , forming the bouquet arrangement ( Fig 1A ) [32] . When the bouquet arrangement is formed , the SPB oscillates between the cell ends with the clustered telomeres , generating so-called “horsetail” nuclear movements ( Fig 1A , Horsetail stage ) . The SPB-led telomere movements promote pairing of homologous chromosomes by inducing their alignment and contact; impairment of either telomere clustering or horsetail movements compromises homologous chromosome pairing [8 , 33] . Studies of S . pombe have unveiled additional functions of the bouquet arrangement . Telomere clustering additionally contributes to spindle formation , and defective telomere clustering causes impairment of spindle formation [34 , 35] . Furthermore , centromere detachment from the SPB induces the formation of meiosis-specific centromeres . During homologous chromosome segregation , homologous chromosomes attach to opposite SPBs ( bipolar attachment ) with sister chromatids attaching to the same SPB ( monopolar attachment ) ( S1A Fig ) [36 , 37] . At this period , the kinetochores on the sister centromeres face the same side ( kinetochore mono-orientation ) , facilitating monopolar attachment of sister chromatids , while centromere cohesion persists , preventing sister chromatid separation upon their bipolar attachment ( S1B Fig ) [38] . Persistent centromere cohesion also enables sister chromatid segregation ( equational segregation ) at meiosis II . Without centromere detachment from the SPB , meiotic centromere properties are not properly established and sister chromatids frequently undergo equational segregation at meiosis I [39 , 40] . Centromere detachment also induces meiotic centromere formation in the budding yeast Saccharomyces cerevisiae [41] . The linker of nucleoskeleton and cytoskeleton ( LINC ) complex formed by the conserved SUN ( Sad1/Unc-84 ) and KASH ( Klarsicht/ANC-1/Syne homology ) domain nuclear membrane proteins recently emerged as a key player in SPB association of telomeres and centromeres [8 , 33 , 42–44] . In S . pombe , the LINC complex consisting of Sad1 ( SUN ) and Kms1/Kms2 ( KASH ) is localized at the SPB . Upon entering meiosis , meiosis-specific Bqt1 and Bqt2 recruit the LINC complex to telomeres , resulting in localization of Sad1 and Kms1/Kms2 at telomeres in addition to the SPB [10 , 45] . The telomere-recruited LINC complexes form the microtubule-organizing center termed the “telocentrosome” ( Fig 1B ) . Subsequently , microtubule motors gather telomeres at the SPB by moving on SPB- and telocentrosome-nucleated microtubules ( S1C Fig ) [33 , 45] . SPB localization of centromeres also depends on the LINC complex; the Csi1-dependent interaction of the outer kinetochore components with Sad1 causes tethering of mitotic centromeres to the SPB [44] . When the bouquet is formed , the outer kinetochore components delocalize from the centromere , and this kinetochore disassembly causes centromere detachment from the SPB [39 , 46] . In S . pombe , centromere detachment immediately follows telomere clustering [47 , 48] , and both events require mating pheromone-dependent activation of MAP kinase [39 , 49] . Together with the fact that SPB localization of telomeres and centromeres depends on the LINC complex , these facts suggest that regulation of centromere detachment and telomere clustering is related . However , their regulatory relationship remains unclear . Here , we show that , when telomere clustering is inhibited , centromere detachment is also inhibited . Efficient centromere detachment requires SPB recruitment of the telomere component Taz1 by telomere clustering and microtubules that drive telomere clustering . We provide evidence indicating that this regulation secures proper spindle formation when telomere clustering is defective . To our knowledge , this is the first example of a regulatory relationship between telomere and centromere positions , which is critical for proper execution of meiotic divisions .
S . pombe cells normally enter meiosis through conjugation of two haploid cells . The bouquet arrangement is formed around the period of cell conjugation [45 , 47] , and persists until meiotic divisions start ( Fig 1A ) [32] . To explore the relationship between telomere clustering and centromere detachment in bouquet formation , we examined telomere and centromere positioning during karyogamy and the horsetail stage in telomere clustering-defective cells . We first examined telomere positioning by visualizing the telomere-proximal sod2 locus on chromosome I using the lacI/lacO recognition system [50] ( Fig 2A , Mitotic interphase ) . In wild-type cells , during mitotic interphase , sod2 signals were located away from the SPB , which was visualized by an mCherry-tagged , conserved SPB component , Sfi1 [51] . By contrast , they were mostly juxtaposed with the SPB during karyogamy and the meiotic mononuclear stage ( including the horsetail and post-horsetail stages; see Fig 1A ) , confirming the occurrence of telomere clustering ( Fig 2A and 2B , WT ) . Telomere clustering depends on telomere-LINC interaction , and a Sad1 interactor , Bqt1 , and a telomere-Bqt1 connector , Rap1 , are essential for this interaction ( Fig 1B ) [10 , 11 , 52] . Consistently , loss of either Bqt1 ( bqt1Δ ) or Rap1 ( rap1Δ ) caused almost complete elimination of sod2-SPB juxtaposition ( Fig 2A and 2B ) . Furthermore , Rap1 interacts with telomeres via two different telomere-binding proteins , Taz1 and Pot1 ( Fig 1B ) [11 , 52–54] . Loss of either Taz1 ( taz1Δ ) or a Rap1-Pot1 connector , Poz1 ( poz1Δ ) , probably partially compromises telomere-LINC interaction , and loss of both eliminates the interaction completely . Accordingly , introduction of taz1Δ or poz1Δ mutation only reduced or rarely affected sod2-SPB juxtaposition , whereas introduction of both mutations almost completely eliminated their juxtaposition ( Fig 2B , Karyogamy ) . Thus , telomere-clustering defects are correlated with telomere-LINC interaction defects . Telomere clustering also depends on telocentrosome formation , which requires a γ-tubulin complex ( γ-TuC ) -recruiting factor , Mto1 ( Fig 1B ) [45] , and sod2-SPB juxtaposition was reduced in mto1Δ cells ( Fig 2A and 2B ) . We next examined centromere positioning by visualizing the centromere-specific histone H3 variant Cnp1 [55] . In wild-type cells , all centromeres were co-localized with the SPB during mitosis ( Fig 2C , Mitotic interphase ) , but they were mostly located away from it during karyogamy and the horsetail stage ( judged by a single nucleus with an astral microtubule array; see Fig 1A ) ( Fig 2C and 2D , WT ) . By striking contrast , in all telomere clustering-defective cells , centromeres were often co-localized with the SPB ( Fig 2C and 2D; S2 Fig ) . Therefore , centromere detachment from the SPB is commonly inhibited in telomere clustering-defective cells . However , in taz1Δ and mto1Δ cells , although there was a considerable level of telomere-SPB association ( Fig 2B ) , centromere detachment was inhibited at similar levels to those seen in bqt1Δ and rap1Δ cells during karyogamy ( Fig 2D , Karyogamy; no statistically significant differences among the mutants ) , in which telomere-SPB association was almost completely lost ( Fig 2B ) . This indicates that inhibition of centromere detachment is not correlated with loss of telomere-SPB association . Because centromere detachment is induced by outer kinetochore disassembly , we next examined the localization of Nuf2 and Mis12 , which are components of the Ndc80 and Mis12 outer kinetochore complexes , respectively . The outer kinetochore components delocalize from the centromere at the time of centromere detachment , and remain delocalized until kinetochore reformation occurs around the end of the horsetail stage ( Fig 3A ) [46] . In wild-type cells , Nuf2 was observed at the centromere-localized SPB during mitotic interphase , and was mostly absent during karyogamy and the horsetail stage ( Fig 3B and 3C , WT ) . Mis12 showed similar localization patterns , but with higher observation frequencies ( S3A and S3B Fig , WT ) . The higher observation frequencies probably mean that Mis12 complexes delocalize from the centromere later and re-localize at the centromere earlier than Ndc80 complexes . By contrast , in all telomere clustering-defective mutants , Nuf2 and Mis12 signals were frequently observed ( Fig 3B–3D; S3 Fig ) . The higher observation frequencies in the horsetail stage were not due to enrichment of cells in the late , kinetochore-reformation stage ( Fig 3A ) because , when we specified early horsetail-stage cells by nuclear signals of RFP-tagged Mrc1 , a DNA replication regulator that was observed only in the early meiotic stage ( S4A Fig ) , Nuf2 was still observed at higher frequencies ( Fig 3C , Horsetail with Mrc1 ) . In addition , the higher observation frequencies were not caused by centromere clustering-dependent increases in signal intensities because the number of centromere signals significantly increased during the horsetail stage in the bqt1Δ mutant [average number of Cnp1 signals was 3 . 4 ± 1 . 1 ( n = 162 ) for wild type and 3 . 9 ± 1 . 2 ( n = 225 ) for the bqt1Δ mutant during the horsetail stage; p<0 . 001 by the Student’s t-test] . This indicates that delocalization of the outer kinetochore components is inhibited in the mutants . The retained signals were localized either at the SPB or at the SPB-detached centromeres ( Fig 3B and 3D; S3A , S3C and S4B Figs ) [centromere and/or SPB localization of Nuf2 signals was confirmed by co-localization of the Nuf2 signals with either SPB and/or centromere signals in cells in which both the SPB and centromeres were visualized ( n = 73 ) ] . Furthermore , the signals were sometimes co-localized with the centromere-free SPB ( Fig 3E , arrowhead ) . This indicates that , in addition to the dissociation of kinetochore complexes from the centromere , that from the SPB is also inhibited . We also noticed that localization patterns of the retained signals in the mto1Δ mutant were somewhat different from those in other mutants; the signals tended to be localized at SPB-detached centromeres in mto1Δ cells , but at the SPB in other mutant cells ( Fig 3D; S3C and S4B Figs ) . Because telomere clustering depends on microtubules [45] , we next examined if microtubule disruption inhibits centromere detachment using a microtubule-depolymerizing drug , methyl 2-benzimidazole carbamate ( MBC ) . To disrupt microtubules before telomere clustering , we synchronously induced meiosis using haploid cells bearing both mating-type genes , as reported previously [40 , 45] . These haploid cells underwent meiosis fairly synchronously after a single mitotic division ( shown by a transient increase in binuclear cells ) in nitrogen-depleted conditions , forming more than two nuclei ( S5A–S5C Fig ) . We treated the cells with MBC or its solvent DMSO after mitotic division . In the DMSO-treated control experiment , as the percentage of cells with SPB-associated telomeres increased ( Fig 4A , WT+DMSO ) , the percentage of those with SPB-associated centromeres ( Fig 4B and 4C , WT+DMSO ) or Nuf2 signals ( Fig 4D and 4E , WT+DMSO ) conversely decreased . This confirms the occurrence of centromere detachment and kinetochore disassembly in addition to telomere clustering . It should also be noted that loss of Bqt1 or Mto1 caused inhibition of telomere clustering , centromere detachment , and kinetochore disassembly in haploid meiotic cells , as shown by the decrease in cells with SPB-associated telomeres ( Fig 4A , bqt1Δ and mto1Δ ) and the increase in those with SPB-associated centromeres ( Fig 4B and 4C , bqt1Δ and mto1Δ ) or Nuf2 signals ( Fig 4D and 4E , bqt1Δ and mto1Δ ) . Thus , haploid meiotic cells mirror diploid meiotic zygotes . MBC treatment inhibited telomere clustering without inhibiting meiosis progression ( S5A–S5C Fig ) , as shown by a significant decrease in the percentage of cells with SPB-associated telomeres ( Fig 4A , WT+MBC ) . Notably , MBC also inhibited centromere detachment and kinetochore disassembly , as shown by a significant increase in the percentage of cells with SPB-associated centromeres ( Fig 4B and 4C , WT+MBC ) or Nuf2 signals ( Fig 4D and 4E , WT+MBC ) . The retained Nuf2 signals were often located away from the SPB ( Fig 4D and 4E , WT+MBC ) . This localization pattern was similar to that seen in mto1Δ cells but different from that seen in bqt1Δ cells , where the signals were mainly at the SPB ( Fig 4D and 4E ) . These observations suggest that the effects of MBC on kinetochore disassembly are similar to those of mto1Δ mutation but different from those of bqt1Δ mutation . Furthermore , although the effects are similar , those of MBC are perhaps stronger than the mto1Δ mutation because the percentage of Nuf2-retaining cells was larger in MBC-treated cells than in mto1Δ cells ( Fig 4D , WT+MBC and mto1Δ ) . To understand the effects of impaired telomere clustering on kinetochore disassembly in greater detail , we next examined the localization dynamics of kinetochore components in haploid meiotic cells . In all wild-type cells , Nuf2 signals disappeared transiently ( Fig 5A and 5B , WT; S1 and S2 Movies ) . By contrast , in bqt1Δ cells , Nuf2 signals sometimes did not disappear ( Fig 5A , bqt1Δ ) , confirming inhibition of kinetochore disassembly . Furthermore , even when they disappeared , their disappearance was delayed , and their reappearance was conversely advanced ( Fig 5A–5C; S3 and S4 Movies ) ; as a result , the duration of Nuf2 disappearance was shorter ( Fig 5D ) . These observations confirm the inhibition of kinetochore disassembly in telomere clustering-defective cells . Kinetochore disassembly is thought to be required for establishment of sister kinetochore mono-orientation and protection of centromere cohesion; therefore , we next examined sister chromatid segregation in telomere clustering-defective cells . For this analysis , we used cells that do not form chiasmata because chiasmata promote monopolar attachment of sister chromatids and obscure centromere properties ( S1B Fig ) [38] . If the properties of meiosis-specific centromeres are impaired , sister chromatids should undergo equational segregation in chiasma-lacking cells . GFP visualization of centromeres of one of the homologous chromosomes ( Fig 6A ) showed that sister chromatids rarely underwent equational segregation in rec12 recombination-deficient , chiasma-lacking zygotes ( Fig 6B , + ) , as reported previously [38 , 40] . By contrast , introduction of bqt1Δ or mto1Δ mutation significantly increased equational segregation in rec12 zygotes ( Fig 6B ) . This indicates that the centromere properties are compromised . In chiasma-lacking cells , sister centromeres frequently attach to both SPBs despite mono-orientation of sister kinetochores , but protected centromere cohesion prevents sister chromatid separation . In these cells , sgo1Δ mutation , which eliminates cohesion protection , causes frequent equational segregation of sister chromatids ( Fig 6C , + ) [38] . Notably , bqt1Δ mutation further increased equational segregation in the rec12Δ sgo1Δ mutant ( Fig 6C , bqt1Δ ) , similar to moa1Δ mutation ( S6 Fig ) , which is thought to compromise kinetochore mono-orientation [56] . This strongly suggests that bqt1Δ mutation compromises kinetochore mono-orientation . bqt1Δ mutation also increased equational segregation in haploid rec12+ cells ( Fig 6D and 6E ) , indicating that the increase was not attributed to loss of Rec12 functions . Furthermore , MBC treatment also increased equational segregation ( Fig 6F ) . All these results support the idea that impairment of telomere clustering causes inhibition of kinetochore disassembly that is required for establishment of the meiosis-specific centromere properties . Telomere clustering causes recruitment of telomere-LINC connectors to the SPB ( S7A Fig , Wild type ) . We therefore hypothesized that an SPB-recruited telomere-LINC connector ( s ) promotes centromere detachment . Among the telomere-LINC connectors , we suspected that Taz1 contributes to centromere detachment , because in the taz1Δ mutant , although telomere-SPB association was substantially retained ( Fig 2B , taz1Δ ) , the observed inhibition of centromere detachment was not statistically different from that observed in bqt1Δ or rap1Δ cells during karyogamy ( Figs 2D and 3C , S3B Fig ) , where telomere-SPB association was almost completely eliminated . To test this possibility , we constructed a Taz1 fragment that lacks the telomeric DNA-binding domain ( Myb domain ) [11 , 57] and fused it with an mCherry fluorescent protein for its visualization and with the nuclear localization sequence for its nuclear localization ( Taz1Δmyb; Fig 7A ) . In taz1Δ cells , Taz1Δmyb was expected to be localized at the SPB by interacting with SPB-localized Rap1 [11] , but not with telomeres ( S7A Fig , taz1Δ+Taz1Δmyb ) . If SPB recruitment of Taz1 is important , it should restore centromere detachment in taz1Δ cells . In wild-type cells , Taz1Δmyb was localized in the nucleus during karyogamy with a very low or undetectable level of accumulation at telomeres or the SPB ( Fig 7B , WT ) . Its weak SPB accumulation was probably due to the presence of endogenous intact Taz1 . In taz1Δ cells , by contrast , Taz1Δmyb accumulated at the SPB ( Fig 7B , taz1Δ , arrowheads ) without restoring telomere-SPB association ( S7B Fig , +Taz1Δmyb , taz1Δ ) . Importantly , Taz1Δmyb significantly decreased the percentage of cells with SPB-associated centromeres or Nuf2 signals ( Fig 7C and 7D , +Taz1Δmyb , taz1Δ ) . This result supports the idea that defective SPB recruitment of Taz1 causes inhibition of centromere detachment in taz1Δ cells . In bqt1Δ and rap1Δ cells , Taz1Δmyb failed to decrease cells with SPB-associated centromeres or Nuf2 signals ( Fig 7C and 7D , +Taz1Δmyb ) . However , Taz1Δmyb did not accumulate at the SPB because Taz1 cannot directly interact with the LINC complex without Bqt1 or Rap1 [10] ( Fig 7B , Taz1Δmyb , bqt1Δ ) , and it remained unclear if defective SPB recruitment of Taz1 is the cause of the inhibition in these cells . To elucidate this point , we artificially tethered Taz1Δmyb to the SPB by fusing it with almost the entire length of Sad1 , a LINC component that constitutively localizes at the SPB ( Taz1Δmyb-Sad1; Fig 7A; S7A Fig , bqt1Δ+Taz1Δmyb-Sad1 ) . Taz1Δmyb-Sad1 did not affect cell growth ( judged by cell viability and the doubling time ) and centromere-SPB association during mitosis . Importantly , Taz1Δmyb-Sad1 accumulated at the SPB without restoring telomere-SPB association in bqt1Δ and rap1Δ cells , as well as in taz1Δ cells ( Fig 7B and S7B Fig , +Taz1Δmyb-Sad1 ) , and significantly decreased the percentage of cells with SPB-associated centromeres or Nuf2 signals ( Fig 7C and 7D , +Taz1Δmyb-Sad1 ) . These observations indicate that a lack of Taz1 at the SPB causes inhibition of centromere detachment and kinetochore disassembly in bqt1Δ , rap1Δ , and taz1Δ cells . Taking all these results together , we conclude that SPB-recruited Taz1 promotes centromere detachment from the SPB . It should be noted , however , that in bqt1Δ , rap1Δ , or taz1Δ cells expressing Taz1Δmyb-Sad1 , the percentage of cells with SPB-associated centromeres or Nuf2 signals was slightly higher than that in wild-type cells ( Fig 7C and 7D ) , suggesting that Taz1Δmyb-Sad1 does not completely restore centromere detachment . In mto1Δ or MBC-treated wild-type cells , although Taz1 is often recruited to the SPB by frequent telomere-SPB association ( Figs 2B and 4A ) [45] , centromere detachment was substantially inhibited ( Figs 2D and 4B ) . In addition , in mto1Δ zygotes , although Taz1Δmyb-Sad1 was localized at the SPB ( Fig 7B , +Taz1Δmyb-Sad1 , mto1Δ , arrowheads ) , it decreased the percentage of cells with SPB-associated centromeres or Nuf2 signals only slightly , and the decrease was not statistically significant ( Fig 7C and 7D , +Taz1Δmyb-Sad1 , mto1Δ ) . Likewise , in mto1Δ or MBC-treated haploid meiotic cells , Taz1Δmyb-Sad1 did not significantly affect centromere and Nuf2 localization ( Fig 8A ) as well as meiosis progression ( S5D and S5E Fig , WT+Taz1Δmyb-Sad1 and mto1Δ+Taz1Δmyb-Sad1 ) . These results suggest that microtubules contribute to the regulation of centromere detachment independently of Taz1 . To test this possibility , we treated haploid bqt1Δ cells , which are completely defective in SPB recruitment of Taz1 , with MBC . MBC treatment increased the percentage of cells containing SPB-associated centromeres ( Fig 8B , Cnp1 , Δbqt1 ) without inhibiting meiosis progression ( S5D Fig , bqt1Δ ) . Even with Taz1Δmyb-Sad1 expression , MBC treatment increased the percentage of cells with Nuf2 signals , as well as those with SPB-associated centromeres ( Fig 8B , bqt1Δ+Taz1Δmyb-Sad1 ) . From these results , we conclude that microtubules promote centromere detachment independently of SPB-recruited Taz1 . Microtubules drive SPB movements , which cause horsetail nuclear movements . However , centromere detachment was not dependent on SPB movements because centromere detachment was not inhibited in dhc1Δ cells , which are defective in SPB movements [58] ( S2 Fig ) . Our results show the presence of regulatory mechanisms that inhibit centromere detachment from the SPB in the absence of telomere-SPB association . However , the advantages of the mechanisms in meiosis are unclear . It was recently reported that defective telomere clustering causes impairment of spindle formation with detachment of the SPB from the nuclear membrane , and that centromere-SPB association restores spindle formation in the absence of telomere clustering [34 , 35] . These facts suggest that meiotic spindle formation requires SPB association of either telomeres or centromeres , and that concurrent detachment of telomeres and centromeres from the SPB is harmful for spindle formation . The inhibitory mechanisms of centromere detachment may secure spindle formation by preventing concurrent detachment of centromeres and telomeres . To test this possibility , we observed spindle formation in taz1Δ and bqt1Δ mutants , which are partially and fully defective in telomere clustering , respectively ( Fig 2B ) , and examined the effects of Taz1Δmyb-Sad1 on spindle formation . If spindle formation requires SPB association of either telomeres or centromeres , Taz1Δmyb-Sad1-dependent elimination of centromere-SPB association should cause impairment of spindle formation in telomere clustering-defective cells , and the extents of the resultant impairments should be correlated with the extents of telomere-clustering defects . In wild-type cells , meiosis I and II occurred sequentially with the formation of one and two spindles , respectively ( Fig 9A , WT; S5 Movie ) , and Taz1Δmyb-Sad1 only marginally compromised spindle formation ( Fig 9B , WT ) . In taz1Δ cells , spindle impairment was not detected , while , in bqt1Δ cells , impaired spindle formation together with detachment of the SPB from the nuclear periphery were observed in about half of cases , as reported previously [34] ( Fig 9A and 9B , +None; S6 Movie ) ( we cannot exclude the possibility that the detached SPBs remained connected with the nucleus by Cut11-lacking nuclear membrane ) . Notably , Taz1Δmyb-Sad1 induced or increased spindle impairment together with SPB detachment from the nuclear periphery in taz1Δ and bqt1Δ cells ( Fig 9A and 9B , +Taz1Δmyb-Sad1; S7 Movie ) , supporting the importance of centromere-SPB association for spindle formation . In addition , the extents of spindle impairment were correlated with the extents of defective telomere clustering; telomere clustering was more severely impaired in bqt1Δ cells than in taz1Δ cells ( Fig 9B , +Taz1Δmyb-Sad1 ) . These results support the idea that meiotic spindle formation requires SPB association of either telomeres or centromeres , and that the inhibitory mechanisms of centromere detachment secure spindle formation by preventing the concurrent detachment of centromeres and telomeres . The SPB was frequently missing at one ( monopolar ) or both ( nonpolar ) spindle poles ( Fig 9A and 9B; S6 and S7 Movies ) . This may mean that telomere/centromere-SPB association is critical for anchoring the SPB to the spindle pole .
In this study , we showed that kinetochore disassembly-dependent centromere detachment from the SPB is inhibited when telomere clustering is compromised during bouquet formation in S . pombe . Two lines of evidence show that SPB recruitment of Taz1 promotes centromere detachment ( Fig 10A ) . First , in taz1Δ cells , despite occasional SPB association of telomeres , centromere detachment was greatly inhibited . Second , Taz1Δmyb-Sad1 alleviated the inhibition without restoring telomere clustering . We also found that in addition to Taz1 , microtubules contribute to centromere detachment ( Fig 10A ) . This conclusion was drawn from the fact that centromere detachment was inhibited by introduction of mto1Δ mutation or MBC treatment . Two facts indicate that the contribution of microtubules is Taz1-independent . First , in cells with a disorganized microtubule network , despite occasional recruitment of Taz1 to the SPB , centromere detachment was inhibited to a level comparable to that in cells in which SPB recruitment of Taz1 was completely defective . Second , Taz1Δmyb-Sad1 failed to restore centromere detachment in mto1Δ or MBC-treated cells . Thus , telomere clustering-dependent SPB recruitment of Taz1 and microtubules independently promote centromere detachment in bouquet formation . Taz1- and microtubule-dependent regulatory mechanisms probably inhibit centromere detachment when telomere clustering is impaired . One likely scenario for Taz1-dependent centromere regulation is that Taz1 recruits a factor ( s ) to the SPB that promotes centromere detachment by inducing dissociation of outer kinetochore complexes from the centromere and SPB . The promoting factor is perhaps activated or induced by mating pheromone-dependent MAP kinase because , in the absence of mating pheromone signaling , centromeres remain attached to the SPB despite clustering of telomeres [39] . Aurora kinase is a potential candidate for the modifying factor because this kinase induces centromere detachment from the SPB in S . cerevisiae [60 , 61] . Another candidate is Polo-like kinase because it regulates meiotic kinetochore mono-orientation and centromere cohesion [62] . However , it is currently unclear if these kinases contribute to centromere detachment . Although Taz1 is important , we cannot exclude the possibility that a different telomere-associated factor ( s ) additionally contributes to centromere detachment . Taz1Δmyb-Sad1 did not appear to completely restore centromere detachment in the telomere clustering-defective mutants ( Fig 7C and 7D ) . Furthermore , the retention level of SPB-centromere association or Nuf2/Mis12 centromere localization in the taz1Δ mutant was slightly lower than that in the bqt1Δ or rap1Δ mutant ( Figs 2D and 3C , S3B Fig , Horsetail ) . These facts may mean the contribution of a different telomere-associated factor to centromere detachment . How microtubules contribute to centromere detachment remains elusive . Given the requirement of telomere- and/or SPB-nucleated microtubules for telomere clustering , we speculate that efficient centromere detachment requires the telomere/SPB-nucleated microtubules . In mto1Δ cells , γ-TuC telomere/SPB localization and telomere/SPB-microtubule interaction are not completely eliminated [45] , and the remaining telomere/SPB-nucleated microtubules may account for why inhibition of kinetochore disassembly appeared to be weaker in mto1Δ cells than in MBC-treated cells . It is possible that the telomere/SPB-nucleated microtubules recruit a cytoplasmic factor ( s ) to telomeres and/or the SPB , which activates a centromere detachment-promoting factor in the nucleus , perhaps via the LINC . Alternatively , microtubule-lacking , LINC-accumulated telomeres may generate inhibitory signals for centromere detachment , as spindle-unattached centromeres do for metaphase-anaphase transition in the spindle assembly checkpoint pathway . In either case , the regulatory mechanism is probably different from the Taz1-dependent mechanism because the localization patterns of retained kinetochore components differ between cells lacking telomere-LINC connectors and those defective in microtubule formation ( Figs 3D and 4D , S3C Fig ) . Kinetochore disassembly is thought to be required for establishment of kinetochore mono-orientation and protection of centromere cohesion [39 , 40] . Our finding that equational segregation of sister chromatids at meiosis I increased in chiasma-lacking , telomere clustering-defective cells , where kinetochore disassembly is inhibited , supported this idea . In telomere clustering-defective cells , kinetochore mono-orientation is probably compromised because equational segregation is increased in sgo1Δ rec12Δ zygotes . It was recently reported that the centromeric histone H3 variant Cnp1 and the heterochromatin protein 1 orthologue Swi6 frequently fail to localize at centromeres in telomere clustering-defective cells [48] . This may mean that kinetochore disassembly also contributes to the proper localization of central centromeric components , and that impaired centromere localization of the central components may cause kinetochore mono-orientation defects . However , if inhibition of kinetochore disassembly induces Cnp1 delocalization , it probably occurs after the horsetail stage because the number of Cnp1 signals was increased during the horsetail stage in the bqt1Δ mutant . We showed that Taz1Δmyb-Sad1 induces or increases impairment of spindle formation in telomere clustering-defective taz1Δ and bqt1Δ cells . Taz1Δmyb-Sad1 induced centromere detachment in these cells; therefore , retention of centromere-SPB association is probably important for spindle formation in the absence of telomere-SPB association . This finding agrees with previous findings that defective telomere clustering causes spindle impairment and that SPB association of centromeres can substitute for SPB association of telomeres and promote spindle formation [34 , 35] . Considering these facts together with the strong correlation of the extents of spindle defects with those of defective telomere/centromere-SPB association , we conclude that concurrent detachment of both telomeres and centromeres from the SPB causes spindle impairment , and propose that the Taz1- and microtubule-dependent inhibitory mechanisms of centromere detachment secure proper spindle formation by preventing such harmful detachment ( Fig 10B ) . Microtubule disruption is caused by various environmental factors such as low temperature and osmotic stress; therefore , telomere clustering is probably often inhibited in nature . Despite the inhibition of meiotic centromere formation , cells may have evolved the inhibitory mechanisms of centromere detachment to secure spindle formation . How telomere/centromere-SPB association contributes to spindle formation is unclear . It was reported that loss of telomere-SPB association leads to decrease in Sad1 at the SPB , and that forced SPB interaction of centromeres restores SPB localization of Sad1 [35] . Given these observations , it was suggested that telomere/centromere-SPB association is required for proper Sad1 localization at the SPB . The decrease in Sad1 at the SPB may account for the dissociation of the SPB from the spindle pole as well as from the nuclear periphery observed in our analyses . Because abolishment of SPB movements restores spindle formation in telomere clustering-defective cells [63] , in the absence of telomere/centromere-SPB association , SPB movements may disrupt Sad1 interaction with the nuclear membrane , reducing Sad1 localization at the SPB . Alternatively , defective SPB recruitment of telomere- or centromere-associated factors that regulate Sad1 localization may cause the reduction . Several facts raise the possibility that similar regulatory mechanisms are present in other organisms . First , meiotic telomere clustering depends on the LINC complexes in other organisms [8 , 42 , 43] . Second , in mouse oocytes , kinetochore disassembly probably also occurs , because some of the outer kinetochore complexes are not localized at centromeres before meiotic divisions [64] . Third , a telomere-binding protein , Ndj1 , which contributes to telomere clustering , regulates meiotic spindle formation by interacting with the SUN-domain protein Mps3 in S . cerevisiae [18 , 19 , 65 , 66] , suggesting the presence of a regulatory relationship between telomeres and spindle formation . Although these facts suggest conservation of the mechanisms , at least in S . cerevisiae , microtubule-dependent regulation of centromere detachment is apparently missing because microtubule disruption causes centromere detachment from the SPB and induces meiotic centromere formation unlike the situation in S . pombe [41] . Although conservation of the regulatory mechanisms is currently unclear , there is no doubt that our findings contribute to understanding the mechanisms of meiosis , because telomere and centromere positions play crucial roles in proper meiotic chromosome segregation . Furthermore , chromosome positioning changes dynamically during development and differentiation and contributes to various chromosomal events in many different organisms [1–5] , and our findings may also be relevant for understanding the chromosome positioning-dependent mechanisms that regulate development and differentiation of other organisms .
The fission yeast strains used in this study are shown in S1 Table . The media and basic genetic manipulation methods used in this study were described by Moreno et al . [67] . The deletion alleles of bqt1+ , rap1+ , taz1+ , and mto1+ were described previously [10 , 11 , 13 , 68] . Visualization of the sod2+ and cen2 loci , Cnp1 , Nuf2 , and microtubules was described previously [40 , 45 , 50 , 69 , 70] . The fusion gene of mis12+ and GFP was obtained from the Yeast Genetic Resource Center . Sfi1 was visualized as follows . A DNA fragment encoding mCherry and the PTET terminator was amplified by PCR using the oligonucleotide primers 5´-ACGCGTCGACGAAGATCTTCGGATCCCCGGGTTAATTAAC-3´ and 5´-GGGGTACCATATTACCCTGTTATCCCTAGCG-3´ , and an mCherry-bearing plasmid as a template , and inserted between the SalI and KpnI sites of an integration vector , pYC36 , which transforms lys1-131 cells into lys1+ cells when it is integrated [71] . The resultant plasmid was digested with SmaI and SacII and ligated with a DNA fragment coding the sfi1+ gene and its promoter , which was amplified by PCR using the synthetic oligonucleotide primers , 5´-TCCCCGCGGGGCATTGTATTTGTCAATACCCA-3´ and 5’-AAAGGCCTACGGGTATTAGGAGGTATAGGC-3’ , and the fission yeast genomic DNA as a template , and digested with SacII and StuI . The resultant plasmid pJM1 was introduced into lys1-131 cells , and integrants were selected by the lys+ phenotype . Alternatively , C-terminal mCherry-tagged Sfi1 was generated by the two-step PCR-based method [72] . DNA fragments encoding the Sfi1 C-terminus-coding region or the sfi1+ terminator were amplified by PCR using two sets of synthetic oligonucleotide primers ( 5´-TTTCAATATTAGTGATTGGAAGCG-3´ and 5´-TTAATTAACCCGGGGATACGGGTATTAGGAGGTATAGGC-3´; 5´-TTTTCGCCTCGACATCATCTCATTTACGATTGACGGAGAGAGT-3´ and 5´-ATACGTATTTCATTTTTGTAAATTTTTC-3´ ) and genomic DNA as a template . The mCherry module was then amplified by PCR using the two PCR products as primers and pHM22 [45] as a template . The resulting PCR product was introduced into cells , and integrants were selected by resistance to the antibiotic nourseothricin ( Werner Bioagents , Jena , Germany ) and confirmed by PCR and microscopic observation . For Mrc1 visualization , a DNA fragment encoding the mrc1 gene and its promoter was PCR-amplified from fission yeast genomic DNA using oligonucleotide primers , 5´-TCCCCGCGGTCGAAAGGGTACACAAGCGGA-3´ and 5´-AAGGCCTGTCAAAGTCCGAGTAATTATTCAA-3´ . The amplified fragment was digested with SacII and StuI and inserted between the SacII and SmaI sites of an mCherry-coding integration plasmid , pHM4 [45] , yielding pAH8 , which encodes the Mrc1-mCherry fusion . Mrc1 was visualized by introducing pAH8 into cells . Cells grown on YES solid medium at 30°C were transferred to ME solid medium and induced to enter meiosis by incubation at 25°C for 14–18 h . Nuclear DNA in meiotic zygotes was stained with DNA-specific Hoechst 33342 dye as described previously [58] . Images of the cells at seven focal planes were taken through a 60×/1 . 42 NA Plan Apo oil immersion objective lens using an Olympus IX71 inverted microscope ( Olympus Corp . , Tokyo , Japan ) equipped with a cooled charge-coupled device camera ( CoolSNAP-HQ2; Nippon Roper Co . Ltd . , Tokyo , Japan ) . Obtained images were processed by deconvolution and analyzed using MetaMorph ( version 7 ) software ( Molecular Devices Japan , Inc . , Tokyo , Japan ) . Haploid cells bearing both mating-type genes were grown in liquid YES medium . They were then induced to enter meiosis by incubation at 30°C in liquid EMM medium lacking a nitrogen source ( EMM-N ) , and treated with MBC , as described previously [45] . For monitoring meiosis progression , 50 μl of the culture was harvested every hour , and nuclear morphology was examined by staining DNA with Hoechst 33342 . Segregation of sister chromatids in zygotic rec12 cells was examined as follows . Cells containing the GFP-labeled cen2 locus and those lacking the GFP-labeled locus of the opposite mating type were grown on YES solid medium at 30°C and mated on ME solid medium at 25°C . For analyzing sister chromatid segregation in haploid meiotic cells , cells containing both mating-type genes were induced to undergo meiosis , as previously described [38 , 40] . The meiotic cells were stained with Hoechst 33342 , and GFP signals were examined in those containing two chromosomal DNA masses . When analyzing mto1Δ zygotes , microtubules were simultaneously visualized by mCherry-tagged atb2+ , and only those forming a meiosis-I spindle were examined because two meiosis I-like chromosome masses were frequently formed due to defective karyogamy . To express an N-terminal portion of Taz1 ( Taz1Δmyb ) , a plasmid bearing a gene encoding Taz1Δmyb was constructed using the integration plasmid pMY23 , which encodes an mCherry and taz1+ fusion and the aur1r gene as a selectable marker [45] . The region that encodes a myb domain of Taz1 in pMY23 was first deleted using the KOD-Plus-Mutagenesis Kit ( Toyobo Co . , Ltd . , Osaka , Japan ) , generating pJM4 . Briefly , a DNA fragment that encodes Taz1Δmyb and mCherry was amplified by PCR using two synthetic oligonucleotide primers , 5’-TTCTCTTCTCAGATTATCACCCTCT-3’ and 5’-AGGATCCCCGGGTTAATTAACAGCA-3’ , and pMY23 as a template . After PCR , the template plasmid was removed by DpnI digestion , and the PCR product was circularized by self-ligation , generating pJM4 . The sequence of pJM4 was confirmed by DNA sequencing . Then , a DNA fragment encoding the NLS together with a FLAG tag and a Swi6 chromo-domain was amplified by PCR using two synthetic oligonucleotide primers , 5’-GAAGATCTGGATCCTAGTCGCTTTGTTAAAT-3’ and 5’-CCTTAATTAAACCCGGGCCTTTCTTCTTTTTG-3’ , and the plasmid Swi6CD-TOPO ( a gift from Dr . Jun-ichi Nakayama , Nagoya City University , Nagoya , Aichi , Japan ) as a template . The PCR product was digested with BglII and PacI and placed at the fusion site of Taz1Δmyb and mCherry by inserting it between the BamHI and PacI sites of pJM4 . The DNA region encoding a FLAG tag and Swi6 chromo-domain was then removed from the resultant plasmid using the KOD-Plus-Mutagenesis Kit with two synthetic oligonucleotide primers , 5’-CCGCGGGTCGACAGGATCCAAACGGCCT-3’ and 5’-CCTTCTCTTCTCAGATTATCACCCC-3’ , as described for pJM4 . The resultant plasmid pAW9-1 encodes Taz1Δmyb tagged with NLS and mCherry at its C-terminus . Its sequence was confirmed by DNA sequencing . pAW9-1 was transformed into cells , and integrants were selected by resistance to the antibiotic aureobasidin A ( Takara Bio . Inc . , Otsu , Japan ) . To tether Taz1Δmyb to the SPB , the plasmid pAH16-6 , which encodes a fusion of Taz1Δmyb and Sad1 , was constructed . pAH16-6 was constructed from a plasmid , pMY35 , that encodes Sad1 with the HA epitope tag . pMY35 was constructed as follows . A DNA fragment encoding the HA epitope tag together with the adh1 terminator Tadh1 was amplified by PCR using synthetic oligonucleotide primers , 5’-ACGCGTCGACGAAGATCTTCGGATCCCCGGGTTAATTAAC-3’ and 5’-GGGGTACCATATTACCCTGTTATCCCTAGCG-3’ , and pFA6a-3HA-kanMX6 [73] as a template . The PCR fragment was digested with KpnI and SalI and inserted between the corresponding sites of the integration plasmid pYC36 , yielding pTO5 . Then , a DNA fragment encoding Sad1 together with its own promoter was amplified by PCR using synthetic oligonucleotide primers , 5’-tccccgcggatgtatccctaacaaacgcaaaaa-3’ and 5’-ccccgctcgagagatgaatcttgacccgtattct-3’ , and the fission yeast genomic DNA as a template , and inserted between the SacII and SalI sites of pTO5 after digestion with SacII and XhoI . A portion of the resultant plasmid that encodes Sad1 fused with the HA epitope tag was amplified by PCR using synthetic oligonucleotide primers , 5’-CCCAGTCACGACGTTGTAAAAC-3’ and 5’- CCCCGCTCGAGATATTACCCTGTTATCCCTAGCG-3’ , digested with SacI and XhoI , and inserted between the SacI and SalI sites of the integration plasmid pTO2 bearing the aur1r gene as a selectable marker [45] to yield pMY35 . To construct pAH16-6 , a fusion gene of Taz1Δmyb and Sad1 was first constructed by inserting a Taz1Δmyb-encoding DNA fragment into pMY35 . A portion of pAW9-1 that encodes Taz1Δmyb and the taz1+ promoter was amplified by PCR using synthetic oligonucleotide primers , 5’-ACGAGCTCATCGACAAGGCATGCGAAGC-3’ and 5’-CCGCTCGAGGATTCTCTTCTCAGATTATCACCC-3’ , and inserted between the SacI and SalI sites of pMY35 after digestion with SacI and XhoI , yielding the plasmid pAH15-1 . Then , pAH16-6 was constructed by replacing a part of pAH15-1 that encodes the HA epitope tag and Tadh1 with an mCherry- and TTET-coding region of the plasmid pHM4 [45] . The part of pHM4 was amplified by PCR using synthetic oligonucleotide primers , 5’- CCTTAATTAATAGCAAGGGCGAGGAGGATA-3’ and 5’- TCCCCGCGGGGATCTGCCGGTAGAGGT-3’ , and inserted between the PacI and SacII sites of pAH15-1 after digestion with the corresponding enzymes , yielding pAH16-6 . The sequence of pAH16-6 was confirmed by DNA sequencing . pAH16-6 was transformed into cells , and integrants were selected as described for pAW9-1 . For analysis of spindle dynamics in diploid zygotes , cells were grown on solid YES medium and induced to enter meiosis by incubation for 14–18 h at 25°C on solid ME medium . Then , the cells were suspended in liquid EMM-N medium . For analysis of Nuf2 dynamics , Nuf2-expressing haploid cells bearing both mating-type genes were grown in liquid YES medium and induced to enter meiosis by incubation at 30°C in liquid EMM-N medium , as described previously [45] . For analysis of spindle or Nuf2 dynamics , a drop of the cell suspension was placed on the bottom of 35 mm glass-bottom dishes ( Matsunami Glass Ind . , Ltd . ) coated with 5 mg/ml lectin ( Sigma-Aldrich Japan , Inc . ) . The cells were observed through a 60×/1 . 42 NA Plan Apo oil immersion objective lens ( Olympus Corp . ) using a DeltaVision microscope system operated by SoftWoRx software or an IX71 inverted microscope operated by MetaMorph software . Time-lapse images of the cells were collected at eight focal planes spaced at 0 . 4 μm intervals every 5 or 10 min for spindle dynamics and at nine focal planes spaced at 0 . 5 μm intervals every 10 min for Nuf2 dynamics using a cooled CCD camera . During collection of time-lapse images , the cells were kept at 25°C . All obtained images were processed by deconvolution , and analyzed using MetaMorph or Priism/IVE software ( available at http://www . msg . ucsf . edu/IVE/index . html ) . | Meiosis is a type of cell division , that generates haploid gametes and is essential for sexual reproduction . During meiosis , telomeres cluster on a small region of the nuclear periphery , forming a conserved chromosome arrangement referred to as the “bouquet” . Because the bouquet arrangement facilitates homologous chromosome pairing , which is essential for proper meiotic chromosome segregation , it is of great importance to understand how the bouquet arrangement is formed . In fission yeast , the bouquet arrangement requires switching of telomere and centromere positions . During mitosis , centromeres are located at the fungal centrosome called the spindle pole body ( SPB ) . Upon entering meiosis , telomeres cluster at the SPB , and centromeres become detached from the SPB , forming the bouquet arrangement . In this study , we show that centromere detachment is linked with telomere clustering . When telomere clustering was inhibited , centromere detachment was also inhibited . This regulatory relationship depended on a conserved telomere component , Taz1 , and microtubules . Furthermore , we show that the regulatory relationship is crucial for proper meiotic divisions when telomere clustering is defective . Our findings reveal a hitherto unknown regulatory relationship between meiotic telomere and centromere positions in bouquet formation , which secures proper meiotic divisions . | [
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"techniqu... | 2016 | A Taz1- and Microtubule-Dependent Regulatory Relationship between Telomere and Centromere Positions in Bouquet Formation Secures Proper Meiotic Divisions |
Oscillations are ubiquitous phenomena in the animal and human brain . Among them , the alpha rhythm in human EEG is one of the most prominent examples . However , its precise mechanisms of generation are still poorly understood . It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography ( EEG ) – functional magnetic resonance imaging ( fMRI ) studies . This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent ( BOLD ) signal . Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus . In this study we explore the potential generative mechanisms that lead to those observations . We use a bursting capable Stefanescu-Jirsa 3D ( SJ3D ) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics . We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections . The model suggests that an inverse correlation between cortical multi-unit activity , i . e . the firing of neuronal populations , and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex . Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation . This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals .
The mechanisms underlying human alpha rhythm generation are still insufficiently understood . One reason is that direct access to human brain activity is limited . Thus , mainly animal studies guide neuroscientists in their quest to reveal these mechanisms . Seminal studies employed dog animal models to explore the cellular substrates of the alpha rhythm [1] . A debated issue is whether alpha oscillations are an emergent property of thalamo-cortical connections or yielded by cortical networks independent of thalamic control . Lopes da Silva et al . were the first to show that a ) there was considerable but not absolute coherence between thalamic and cortical alpha oscillations [1] and b ) that by performing a ‘virtual deafferentiation’ as achieved by partial coherence analysis , there was still considerable coherence within the cortex independent of a thalamic pace maker [2] . Another finding possibly related to generation mechanisms of alpha activity was that sleep spindles oscillating at frequencies partially overlapping with the alpha band are founded on the capability of thalamic nuclei to switch between tonic and phasic activity , the latter sometimes also referred to as bursting mode . This behaviour is caused by an interplay between neuronal populations in the thalamic relay nucleus and the inhibitory reticular nucleus [3] . More recently , it has been shown in-vitro that a bursting mode mechanism might also be relevant for the generation and propagation of alpha oscillations ( e . g . [4 , 5] ) . Complementary to this conceptual finding , another study in monkeys has recently demonstrated that the alpha ( or mu ) amplitude in somato–sensory cortex is not only inversely correlated to sensory discrimination performance but also to firing rate of the involved neuronal ensemble [6] . Furthermore , this study also demonstrated a robust coupling between alpha-phase and firing rate , supporting the idea of high-frequency bursting activity being related to rhythmic alpha activity . Taken together , these findings point to a critical role for the bursting behaviour of neurons in the generation of human alpha activity . In how far this might be achieved locally or as a feature of a thalamo-cortical network is not clear , however . In humans , functional magnetic resonance imaging ( fMRI ) –one of the prevalent non-invasive brain imaging methods–has been combined with electro–encephalography ( EEG , for a review see [7] to further explore the alpha rhythm . Numerous studies revealed a negative correlation between the strength of the classical posterior alpha rhythm derived by EEG and the blood oxygen level dependent ( BOLD ) signal—predominantly in posterior , i . e . mainly visual , cortical regions [8–11] , but see also [1 , 12] . A mechanistic biophysical understanding of this negative relation between rhythm strength and BOLD signal amplitude , however , has been lacking . In the present study we present a generative thalamo-cortical model that attempts to bridge the gap between invasive findings in animals and non-invasive multimodal imaging in humans . Specifically , we provide a model that integrates the inverse relationship between the alpha-rhythm power and the fMRI-BOLD signal in humans and the negative relationship between alpha rhythm and firing rate found invasively in animals . Our thalamo-cortical model consists of coupled Stefanescu-Jirsa 3D ( SJ3D ) nodes , i . e . connected mean field models derived from populations of spike-burst neurons with distributed parameters [1 , 13] . Derived from Hindmarsh-Rose single neurons [2 , 14] , see also Fig 1 ) , the neural activity generated by these SJ3D nodes accounts for a wide repertoire of dynamical regimes observable in empirical electrophysiological data . In particular , the SJ3D model is the only mean field model accounting for bursting activity , hence the prime candidate for modelling of the hypothesized spike-burst patterns of thalamic nuclei . Our working hypothesis is that a set of appropriately coupled large-scale spike-burst nodes can account for the above-mentioned features of human alpha rhythm activity or corresponding activity in animals . We define the nodes of our neuronal network model by choosing the anatomical structures that are assumed to be most relevant for the generation of the posterior alpha rhythm: a node representing the reticular nucleus , a node representing a relay nucleus of the thalamus e . g . such as pulvinar or lateral geniculate body and a node representing the visual cortex . We will further refer to them as reticular , thalamic and cortical node . Each node is represented by a SJ3D model that provides a reduced description of the dynamics of a meso-scale network of coupled inhibitory and excitatory neurons . The individual nodes are coupled through a biologically plausible connectivity skeleton accounting for physiological time delays , the quality of interaction , i . e . inhibition versus excitation , and the direction of information flow ( for a schematic description cf . Fig 2 , for exact values see Table 1 ) . Based on the model output we approximate multi-unit activity , local field potentials , alpha-band power as well as the hemodynamic BOLD response of the relevant nodes in our network . We then vary the parameter of global connectivity , i . e . the connectivity-scaling factor ( CSF ) in the model systematically . Subsequently , we analyse its effect on the model output and on the following features of the alpha rhythm: a ) spectral power and spectral coherence; b ) relationship between amplitude and phase of alpha oscillations and multi-unit activity; c ) relationship between alpha amplitude and the predicted BOLD-fMRI response generated by a convolution model . Finally we compare the model predictions to empirical findings and discuss these results in the context of existing literature .
Using the specified parameters and a moderate connectivity strength with a connectivity strength factor ( CSF ) of 0 . 6 , the cortical node yielded band-limited oscillations around 10 Hz for the simulated local field potential ( see section ‘Methods‘ for detailed description of how we derived an approximated LFP from the model output ) . The amplitude of these oscillations fluctuates as visible in the time-frequency analysis of the local field potential ( LFP , Fig 3 , raw time series in Fig 4 , middle panel ) . Fig 4 shows a zoom-in for a representative time window of 10-s duration , depicting the alpha-power time course ( top panel , grey ) , the LFP time course ( middle panel , grey ) as well as the time course of the multi-unit activity ( MUA , bottom panel , black , see section ‘Methods‘ for derivation of MUA ) . Additionally , an estimation of the total firing rate at the cortical node is overlaid on the alpha power ( top panel , black ) . The alpha-power fluctuations are accompanied by rhythmic bursting in the higher frequency range as reflected by the MUA . Furthermore , the MUA is systematically and inversely correlated with the amplitude of alpha-oscillations in the model ( see Table 1 ) . The resulting hemodynamic response of the model for the cortical node is depicted in Fig 5 . Overlaid on this estimation is the predicted alpha power based on convolution of the time-frequency activity in the alpha-band originating from the cortical node . It results in an inverse relationship between the convolved alpha-power and the modelled hemodynamic response ( correlation coefficients and statistics for all nodes can be found in Table 1 ) . Furthermore , the analysis regarding the relationship of alpha phase and MUA shows a clear dependency as visualized for the cortical node ( Fig 6A ) . MUA is affected as a function of phase of the ongoing alpha activity . In contrast , if we relate the cortical alpha power to a disconnected yet alpha-like oscillating node , we observe no coupling of alpha phase and MUA ( Fig 6B ) .
There are numerous studies modelling the human alpha rhythm with varying degree of biophysical realism . For example , [15] developed an algebraic model with no direct link to its biophysical underpinnings , yet suitable for model inversion . Another class of models has more direct biophysical underpinnings . However , earlier studies often do not include the thalamus [4 , 5 , 16 , 17] . Some do include the thalamus , e . g . [18] or [7 , 19]–the latter modelling detailed nonlinear features of alpha activity such as bistability and scale invariance as reported in [20] . A few studies approximate the fMRI BOLD based on coupled neural mass models [16 , 21–24] . Some studies also try to make a theoretical conclusion about a possible relation between oscillations visible in EEG and metabolism and hemodynamics [25] . There has been relatively little theoretical work on the relationship between oscillations and haemodynamic responses . One theoretical heuristic provided in [26] builds upon earlier work examining the ( necessary ) relationship between fast oscillatory dynamics and mean activity levels in biologically realistic ensembles of coupled neurons [25] . In brief , BOLD signals reflect thermodynamic work and are proportional to neuronal firing rates . Empirically , a loss of alpha activity or desynchronization is usually associated with an increase in beta or gamma activity and reports an activated brain state and increased metabolic activity . In this work , we use a mean field model of neuronal dynamics ( that captures some key dynamical behaviours ) to verify this heuristic using neuronal simulations . What we add here with the present study is the potentially important role of bursting activity that may explain the coupling between multi unit activity and the phase of alpha firing . This coupling is another aspect of electrophysiological responses that can be measured empirically . Another relevant paper [27] proposes a mesoscopic dynamic equation system similar to the one employed here to explore the relations between EEG rhythms and BOLD signal . While all those previous theoretical and numerical studies contributed considerably to our understanding of brain network activity , none of them focused on a possible link between a neuronal model capable of bursting behaviour and the empirical observations of an inverse relation between alpha and the BOLD signal . The present simulation reproduces these empirical findings and thus provides a first hint at the possible key physiological mechanisms . We have assumed that the neurovascular drive behind the BOLD response is a weighted mixture of depolarizations in both inhibitory and excitatory sub-populations ( e . g . see in [28] ) . This is an important assumption that may be contentious . It is generally thought that neurovascular coupling is largely driven by presynaptic glutamate release acting via glial cells and various endothelium relaxing factors . The biophysical mechanisms behind this coupling are reviewed in [27] . For the present purposes , we assume that large deviations in our state variables are sufficient to report or reflect this neuronal drive . However , in future work it may be possible to use empirical EEG and fMRI data to test various hypotheses about this coupling using dynamic causal modelling ( e . g . , [29] ) . In other words , use Bayesian model comparison to evaluate the precise variables that best predict BOLD responses while , at the same time , predicting local field and possibly MUA responses . The relationship between alpha and specific state variables of the model is interesting in itself . However , the scope of this study was to reproduce the empirically observed relationship between alpha and BOLD . Multiple scenarios are conceivable which might lead to the empirically observed negative alpha vs . BOLD relationship , the here presented case is one scenario among others . Furthermore , it appears that due to low-pass filtering by the HRF kernel the correlation of alpha vs . BOLD gets enhanced in comparison to the observed alpha vs . firing rate relation in our simulation . It might be worthwhile to note , that we did not reproduce the finding of a positive relationship between thalamic BOLD activity and cortical alpha activity as found in several empirical studies [8–11 , 30 , 31] . However , notably , there are other resting state studies which did not report such findings regarding the thalamus [32] . A similarly unclear situation exists with regard to positron-emission-tomography ( PET ) and alpha fluctuations . Some groups reported negative correlations [33 , 34] , another reported a positive relation [35] . Also we would like to point out that this relation seems to be modulated by the condition eyes closed versus eyes open . For example , while a positive correlation between cortical alpha and thalamic BOLD was reported in the eyes closed condition ( Moosmann et al . 2003 ) , we found a negative relationship in an eyes open task-related condition [36] . Regarding the present modelling study , it is also important to note that , while we fail to generate a positive relationship in thalamic areas , in simulated data we do reveal a less negative relationship between cortical alpha and thalamus BOLD when compared to the relationship of cortical alpha vs . cortical BOLD . Alpha vs . MUA correlation in reticular nucleus is weakly positive , yet fails to reach significance . This is in contrast to the result for the cortical node . This might be a guide to future studies where to look for the relation between ξ and α state variables . Another important addition for future studies might be the differentiation between nuclei of the lateral thalamus such as pulvinar and lateral geniculate since empirical evidence indicates different functional roles for alpha rhythm generation . The present thalamo-cortical model yields pronounced oscillations with underlying neuronal features such as coherence between regions or average firing rate behaviour conforming to empirical data from animals and humans . Spontaneous fluctuations of neuronal activity as visible in EEG are based on the fact that spontaneous neuronal activity is highly structured in space and time [37–39] . The synchronization of activity within neuronal populations gives rise to large ( summed up ) potential fluctuations that are visible in the EEG [40] . It is also known that these spontaneous synchronous LFP fluctuations are linked to the MUA of the respective neuronal population [39] . Our models shows good concordance with existing studies in that it expresses an oscillation of which the amplitude is negatively correlated with a ) the firing rate and b ) the approximated hemodynamic signal . This at first sight counter-intuitive behaviour is rendered reasonable when considering a firing-rate behaviour dependent on alpha rhythm phase . Similar to what has observed empirically , e . g . in [6] , there is a stronger link between alpha rhythm phase and MUA than between alpha rhythm amplitude and MUA . Still , on average and , in our model , quite consistent across a wide range of connectivity scenarios , firing rate goes down with higher alpha amplitude . An additional interesting finding is that in the range of our tested scenarios , we can identify one ( CSF = 0 . 6 ) which shows–analogue to empirical observations–high coherence between regions on the one hand and local inverse correlations between alpha amplitude and firing rate on the other . We here explored the role of the large-scale network included in the model . More specifically , we tested if considering just a single node yields similar relations between alpha oscillations and neuronal firing as well as the hemodynamic response . We systematically explored the emerging activity in a network consisting of thalamic and cortical nodes . A detailed description of dynamics in a thalamo-cortical network model has also been provided by [41 , 42]; yet with a focus on fast oscillations in the 20 to 60 Hz range . While for a disconnected model ( CSF = 0 . 0 ) we already can reproduce some features of the alpha rhythm such as the inverse relationship between firing rate and alpha power , this does not imply that it is a realistic condition . Increasing connectivity between nodes has effects on other features of our network model such as cross-nodal coherence . Several models generate rhythmic behaviour through the use of transmission delays between coupled populations of spiking neurons . In isolation these populations do not show oscillations . Using the SJ3D model is different in that respect , since here , the single nodes are able to oscillate on their own . This scenario appears to be supported by empirical findings of reports of alpha activity in dogs where a distinct , but not perfect coherence between thalamic and cortical structures has been found [2] . However , even after ‘virtually’ eliminating the pace-making and driving effect of the thalamus by computing the partial coherence for the cortical electrode sites , the authors could show that intracortical coherence in the alpha band was still high . This was taken as indication that the thalamus is not the only structure expressing rhythmic activity ( which then is transmitted ) , but that the cortical areas might oscillate on their own . This holds also true in our case , since ‘deafferenting’ a node by decoupling it from the others might still yield an oscillating node . However , temporal dynamics of these oscillations are then governed by local interactions and not more distant interactions . In summary , just a single node can reproduce the observed negative correlation between cortical alpha and firing rate/ BOLD . Interestingly , this negative relationship between alpha and BOLD in the cortical node is maintained even when including the thalamic and reticular nodes–which is not trivial . More importantly , however , here we were looking for a model , which can reproduce multiple phenomena , with one important feature being the observed coherence between thalamic and cortical alpha oscillations . This can only be achieved by introducing connectivity between the thalamic and cortical node . In that sense , only our connected network demonstrates the reproduction of these features combined , which , in our view , makes this model more interesting without adding needless complexity . Future investigations will investigate whether the network shows additional features , which cannot be accomplished by modelling just a single node . Specifically , we demonstrate how a cellular ensemble in bursting mode oscillates in the alpha frequency regime and how that translates into alpha phase-dependency of the underlying MUA . Furthermore , we also demonstrated how the bursting mode , i . e . the alpha oscillations , translate into lower average firing rate of the involved neuronal ensemble . It is important to note that bursting per-se does not simply explain the observed average inverse behaviour of alpha amplitude and MUA . Bursting behaviour is characterized by the mutual presence of two time scales , a fast spiking and slower bursting time scale . These two time scales allow interactions between and across scales . For instance , it is significantly easier to achieve synchronization across neurons on the slower bursting time scale than on the faster scales [43] . Now why would bursting imply that alpha activity–i . e . the slower time scale–is inversely related to MUA activity–the faster time scale ? In principle , a scenario is conceivable where bursting implies a higher average MUA than in the tonic mode . However , in most cases empirically observed , the bursting mode is associated with the local firing rate activity being generally lower than during non-bursting , which also holds true in case of our model . The activity within nodes goes into a bursting-like mode , when these nodes are less excited by incoming input . This fits well with the reports from monkey data as well as with numerous EEG-fMRI studies demonstrating less metabolic demand with higher alpha amplitudes . The ‘gating by inhibition’ theory [44] ascribes the alpha rhythm an inhibitory role mediated through the temporary suppression of gamma oscillations that in turn have been shown to be positively related to neuronal population firing and visual processing [45] . While this theory has prevailed for some time , the biophysical link between the decreased firing and increased alpha oscillations was missing . While we did not focus on gamma band activity specifically , the relationship between high-frequency neuronal activity and alpha power is clearly visible in our model . The firing rate dependency contingent on alpha phase in our model fits very well with this idea of cyclic inhibition . In our model , it is basically a local phenomenon , but due to the observed alpha coherence across nodes it might also play a role in the connected circuit . The alpha rhythm is the most prominent oscillatory electric large-scale signature of the human brain . In previous empirical studies , we and others found converging evidence for spatially and functionally distinct alpha brain states affecting cognition , behaviour , learning as well as evoked brain responses [36 , 46–53] . The fact that there are spatially and functionally distinct patterns of alpha activity support the idea that the human brain holds various alpha rhythm sources . In addition to the most prominent ‘classical’ alpha rhythms that can be found predominantly over posterior brain regions , also other sensory systems are equipped with resting state alpha like oscillations , such as the tao rhythm in the auditory and the mu rhythm in the sensory-motor system . Similar to multimodal analyses of occipital alpha , spontaneous modulations of the power of the mu rhythm have been shown to exhibit a similar inverse relation with the BOLD signal in the underlying cortical regions [54] as observed for the classical alpha rhythm . This is of importance since it might point to a universal mechanism underlying the inverse relation between the cortical BOLD signal and alpha oscillations . Our model supports this notion since it is blind to which modality might be involved . It generalizes to any network , which is connected in a similar way , i . e . via a thalamic relay nucleus to the cerebral cortex and a modulating , inhibitory nucleus such as the reticular nucleus of the thalamus . The model presented here is a computationally efficient yet powerful simulation of a thalamocortical circuit able to generate alpha-like rhythms with features close to what is empirically observed in human and animal brain oscillations in the alpha frequency range . We believe that this model shows remarkable promise and might be extended to capture additional features of spontaneous human brain activity . It will be interesting to embed this specific network in a more global network on a whole brain level ( see for example [22] for a full brain model based on SJ3D nodes or [55] for a full brain spiking neuron model ) in subsequent studies . As an extension to [22] we would add inhibitory connections and node-specific intrinsic connectivity configurations such as modelled here for the reticular nucleus . That being said , the model described here already generates useful insights on how the alpha rhythm relates to neuronal firing and the BOLD signal , it offers new hypotheses for future work , and points to an important role of bursting behaviour for large-scale EEG dynamics .
The SJ3D model provides three distinct ‘modes’ , which reflect the major degree of variation of neuronal activity within a neuronal mass ( see Eq 2 and [13] ) . For each mode and node , the relevant state-variables are ξ and α , representing the summed membrane potentials of the respective excitatory and inhibitory sub-populations . We down-sampled data from its original 20kHz to 2kHz for all subsequent analyses . We examined the output of the system in a two-fold manner: One type of output is based on the high-frequency ( 500–900 Hz ) part of the resulting membrane potential in analogy to multi-unit activity as measured in real data , e . g . as analysed in [61] , [62] . This output we refer to as an approximation of multi-unit activity ( MUA ) . Another type of output was generated by extracting the low-frequency ( 5-60Hz ) part of the signal in analogy to the analysis of local field potentials ( LFP ) . The resulting signal is referred to as an approximated LFP . Data was filtered with a 2nd order Butterworth filter . For both approximated LFP and MUA , the excitatory activity of all three modes ( state variables ξ , η , τ ) was summed to reflect the total activity of both measures within each node . Inhibitory modes ( α , β , γ , see Eq 2 ) were ignored in this approximated activity , as it is the excitatory pyramidal neurons that provide the dominant contribution to the EEG and the alpha rhythm ( e . g . [63] ) . To estimate the oscillatory activity of the cortical node in the alpha-band , we performed time-frequency decomposition by wavelet decomposition of the resulting LFP using a Morlet mother wavelet ( 3 cycles ) and used its modulus to extract the time evolution of the average power within the frequency band of 8–12 Hz . The mesoscopic model does not provide direct access to single cell activity and spiking . Hence we approximated the local firing rate within each node by calculating the power envelope across the entire chosen frequency band of 500–900 Hz for the approximated MUA output . To obtain a measure of how the cortical alpha power relates to MUA in the individual nodes of our model , we smoothed the MUA power time course ( sliding average , with 75 samples , ( i . e . 375 ms ) lead and lag , resp . ) . Subsequently we performed a linear correlation analysis of the resulting alpha-band power of the cortical node and the resulting smoothed MUA power . We predicted the hemodynamic response of each node by convolving the direct output of the excitatory as well as the inhibitory membrane potentials for each mode ( i . e . state variables ξ and α ) with the canonical hemodynamic response function ( HRF ) characterized by a peak response at 5 s as used by the statistical parametric mapping software SPM ( Functional Imaging Laboratory , London , UK ) . This function closely resembles the results generated with the biophysically based Balloon-Windkessel model [64] . Then , the resulting signal time courses were summed up within each mode with the respective weighting , i . e . inhibitory component 1/3 vs . excitatory component 2/3 , and across all modes to obtain a final estimate of metabolic demand for each node [13] . Following the typical routines of empirical EEG-fMRI analyses—we convolved the time-course of the simulated cortical alpha power with the canonical HRF . This provided the ‘alpha BOLD regressor’ . In a next step we performed a linear correlation analysis between the ‘alpha BOLD regressor’ and the predicted fMRI response linked to the net neural activity at the cortical node . After having performed these calculations for each node , we now determined the relation of the simulated cortical fMRI signal and the simulated and HRF convolved alpha power by analysing their linear correlation . The resulting correlation strength between the two simulated signals was then qualitatively compared to empirical observations in real data taken from our previous EEG-fMRI studies [11 , 36] . We calculated the magnitude-squared coherence between the LFP from the cortical and thalamic node . The coherence indicates how well a band-specific signal corresponds to another signal in the same frequency band . It is a function of the spectral densities of these two signals and theirs cross-spectral density . For analysis , we used the cortical and the thalamic node . A value of 0 indicates absence of coherent frequency-band specific activity . A value of 1 implies that frequency-band specific amplitude fluctuations are perfectly in phase . Approximated cortical alpha was sorted into 20 phase bins using the phase information from the wavelet analysis . For each bin MUA activity was estimated separately and averaged across repetitions ( n = 10 ) . This number was chosen to have a first approximation of variation of our observed effects . Phase dependency can be inferred from the resulting distribution of average MUA . A uniform distribution indicates no phase-dependency , while a sinusoidal distribution of MUA across bins indicates phase dependency . We performed this analysis for the cortical node and as a control condition across two disconnected nodes , i . e . phase of alpha oscillations of one node against MUA of another disconnected node . We examined the effect of global connectivity strength on the described analysed features of the alpha rhythm . Specifically we determined the effects on spectral power , spectral coherence , the alpha-MUA relationship and the alpha-fMRI coupling . To this end , we modulated the parameter CSF , ranging from 0 . 0 ( entirely disconnected model ) to 12 . 8 ( extremely strong coupled model ) . While the model showed saturation effects at CSF = 6 . 4 , at the next level , at CSF = 12 . 8 the model started to collapse completely ( due to hyper-excitation ) , so we did not use CSFs higher than 6 . 4 . For each range of CSF the model was repeated 10 times ( with randomized initial conditions ) to obtain an approximation of the robustness of the observed effects . We focused on cortical alpha oscillations and their relationship to features of the underlying neuronal ensemble or its relationship with features of other nodes . | In this article , we show how a large-scale neuronal model involving a thalamo-cortical circuit can reproduce findings from human and animal brain oscillatory activity and how this can help understanding the mechanisms , which generate this activity . Amongst the brain rhythms , the alpha rhythm ( 8-12Hz ) is the most prominent , having its most pronounced expression when the brain is at rest . The mechanisms underlying its generation are not fully understood . Using computational modelling techniques , we demonstrate how the alpha rhythm may emerge from local and network interactions and explain empirical results , such as why the energy consumption drops when electric brain activity—as indicated by the amplitude of oscillations—goes up . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model |
Gene fusion and fission events are key mechanisms in the evolution of gene architecture , whose effects are visible in protein architecture when they occur in coding sequences . Until now , the detection of fusion and fission events has been performed at the level of protein sequences with a post facto removal of supernumerary links due to paralogy , and often did not include looking for events defined only in single genomes . We propose a method for the detection of these events , defined on groups of paralogs to compensate for the gene redundancy of eukaryotic genomes , and apply it to the proteomes of 12 fungal species . We collected an inventory of 1 , 680 elementary fusion and fission events . In half the cases , both composite and element genes are found in the same species . Per-species counts of events correlate with the species genome size , suggesting a random mechanism of occurrence . Some biological functions of the genes involved in fusion and fission events are slightly over- or under-represented . As already noted in previous studies , the genes involved in an event tend to belong to the same functional category . We inferred the position of each event in the evolution tree of the 12 fungal species . The event localization counts for all the segments of the tree provide a metric that depicts the “recombinational” phylogeny among fungi . A possible interpretation of this metric as distance in adaptation space is proposed .
As the number of complete genome sequences increases , comparative genomics unveils the mechanisms of gene and genome evolution . Duplication , sequence divergence , and recombination are the major mechanisms at work in gene evolution [1] . Recombinational events such as translocation , inversion or segmental duplication can create accidental fusion of DNA sequences associated with different genes , or conversely the fission of a gene into several parts . Potentially , these events can create new genes from already existing parts , or reciprocally shuffle genes into sub-parts across a genome . These rare events participate in the evolutionary history of the species , and must be taken into account in genome rearrangement models . Methods to inventory gene fusion and fission events on a large biological scale can provide insights about the multimodular architecture of proteins [2] , [3] , [4] , as well as a metric between genomes independently of the mutation rate [2] , [5] , this work . Computational detection of fusion and fission events uses sequences from several species , usually proteome sequences . This implies that the detection is only performed in the coding regions , a reasonable approximation as non-coding regions evolve faster . After a recombinational event , gene fusion can occur and is situated either in coding or non-coding sequences . In non-coding sequences , gene fusion can give rise to the misregulation of the expression of a gene now under the control of the cis-regulatory sequence of another gene . For instance , the cells in the majority of human prostate cancers bear a gene fusion where the regulatory sequence of the TMPRSS2 gene controls the coding sequence of a transcription factor , either ERG or ETV1 , resulting in over-expression of this factor and hence anarchic growth [6] . In coding sequences , gene fusion results in the assembly of a new gene , thereby allowing the emergence of new functions by the accretion of peptide modules into multidomain proteins . As an example , the Tre2 ( USP6 ) oncogene emerged from the fusion of the USP32 and TBC1D3 genes in the hominoid lineage of primates , and it has been proposed that this has contributed to hominoid speciation [7] . Gene fission splits a gene into several parts and can be produced by either recombinational events or single base events , such as frameshift and nonsense mutations . The outcome can be the misregulation of the expression of a gene when a cis-regulatory sequence is concerned . Due to the fast evolution of non-coding sequences , the detection of fission events involving such sequences will be out of reach when comparing the genomes of distant species . Loss of continuity in the coding sequences , produced by any of the above events , can give rise to a less complex protein by domain depletion , as , for instance , in the monkey king family of genes in Drosophila species [8] . Gene fission events can also produce pseudogenes [9] . In completely sequenced prokaryotic genomes , fusions occur more frequently than fissions , and there is no striking bias in the functions of the genes that have undergone these events [5] . The same conclusions hold true in the three kingdoms of the tree of life , by considering the structural domains of the proteins [10] . In mammalian genomes , the close evolutionary distances make it possible to detect fusion and fission events in coding and non-coding sequences; the events between coding sequences involve genes whose protein products have a significant propensity to interact [11] . Fusion events between proteomes have been used to predict protein-protein interactions [12] , [13] with some degree of success , in particular metabolic enzymes for which stable protein-protein interactions in one species could be advantageously substituted by the products of fusion events in other species . Altogether , such large-scale comparisons of proteomes revealed that about 4% of the proteins are the products of fused genes and 9% are encoded by genes which are fused in other genomes [2] . These methods work at the level of individual genes , which is an appropriate approach in prokaryotes as the number of duplicated genes is low . We present here a large scale computation method for detecting gene fusion and fission events in eukaryote genomes , even when they have a noticeable amount of internal gene redundancy . Contrary to the methods published so far , we directly worked at the level of groups of paralogs . We applied the method to the proteomes of a coherent phylogenetic group of species over a large evolutionary range . We chose to focus our study on 12 species covering the phylum of fungi in which a number of complete or near complete genomes are currently available , especially in the group of hemiascomycetes ( yeasts ) . Nonetheless we also chose other ascomycete species as well as basidiomycete and zygomycete species ( Table 1 ) . As the evolutionary distances between genomes are large , even inside the group of hemiascomycetes [14] , the divergence of non-coding sequences is too high [15] to search for fusion events in them . Since our study is restricted to coding sequences , we employed complete proteomes to track fusion and fission events . At first we detected 1103 fusion/fission events , some of them having complex structures which were subsequently decomposed ( see Material and Methods ) , finally giving an inventory of 1680 elementary fusion and fission events in the coding sequences . The number of events in which a species is involved is correlated with the genome size of the species . As some of these genomes are thoroughly annotated , we searched for and could observe slight biases in the biological functions of the genes involved in fusion and fission events compared to those of the other genes . In this phylum , the genes involved in an event tend to belong to the same functional category , a feature already found in prokaryotes [2] , [3] . We chose to focus on genome evolution rather than individual domain structure of fusion proteins . Thus we computed the localization of each event in the evolution tree of the 12 fungal species , on the parsimonious assumption that a fusion or fission event happens once during evolutionary history [10] . The weighted counts of events localized in each segment of the phylogenetic tree provided a metric between species , independently of the mutation rate of the genes . From this perspective , it is apparent that some species have undergone massive genome shuffling . The events relative to the hemiascomycetes will be available in the Genolevures database [16] ( http://cbi . labri . fr/Genolevures/ ) and will be incorporated there in the definitions of protein families .
The detection of fusion and fission events was performed on the proteomes of species belonging to the group of fungi , with some emphasis in hemiascomycetes as several complete genomes are available . Only complete , or near complete , genomes can provide sets of protein sequence data exhaustive enough to allow precise counts of events . Thus we restricted our study to genomes which were highly covered by sequences ( Table 1 ) . When the sequence of a single protein is split into several entries in the proteome file , we deduced that these were sequences of exons and merged these entries to avoid false positive artifacts . A small number of sequences were omitted as they were too short ( 10 amino-acids or less ) to be treated . In some proteomes , a part of the detected events may nonetheless be spurious , due to the quality of sequences and the accuracy of the gene models used to predict introns and coding sequences . We assigned Pfam profiles [23] to P-groups according to the proteins that they contain . We extracted the Pfam identifiers from public databases for a large sample of E-groups and O-groups , in fact all the proteins from S . cerevisiae , C . glabrata , K . lactis , E . gossypii , D . hansenii , Y . lipolytica and S . pombe , and we aligned the C-groups to the library of Pfam . Then we converted the Pfam identifiers into GO identifiers [24] through the pfam2go conversion file . Finally , we transformed the GO identifiers into high level identifiers in the GO ontology by using the go2slim script together with the yeast GO slim ontology , thus grouping the identifiers into main categories . For each event , we mapped the species which contained E-groups or C-groups ( Figure 2 ) onto the phylogenetic tree underlying the 12 species [25] . Under the parsimonious assumption that any event occurred once during evolution , the event should be localised on the tree in one of the edges between the species containing E-groups and the species containing C-groups . Thus , we extrapolated the status of the internal , i . e . ancestral , nodes of the tree as either E-group containing node or C-group containing node: ( i ) all internal nodes belonging to a shortest path between two E-group containing species , are extrapolated as E-group containing nodes , i . e . the nodes 1 , 2 , 4 , 5 , 6 , 7 , 9 in Figure 2 example; ( ii ) likewise , the status C-group containing node applies to internal nodes between C-group containing nodes , i . e . none in the example; ( iii ) the event is inferred to be localised on the shortest path between E-group containing nodes/species and C-group containing nodes/species , i . e . either on the edge [node7-node8] or on the edge [node8-A . nidulans] in the example; ( iv ) if a given species without status is connected to this last path and if it contains P-groups equivalent to some of the E-groups which defined the event , then the species is assimilated to an E-group containing species and the path can be shortened , i . e . N . crassa in the example shortens the path , leaving the [node8-A . nidulans] edge as the remaining path; ( v ) each of the n remaining edges receives a score of 1/n , i . e . the [node8-A . nidulans] edge receives a score of 1 in the example . In 4 of the 15 cases of multiple events , the mapping onto the tree brought about internal nodes in which the probable ancestral content in C-groups or E-groups could not be inferred , leading us to suppose that a particular fusion or fission occurred more than once over time . Here , we ordered the involved species in decreasing order of the number of uncertain internal nodes that were resolved when the species was removed . We then used each of these species in this order as the starting point of a shortest path , see preceding paragraph , and removed species until no uncertain internal nodes remained and all of the species were treated . At the end , we identified the minimal number of events necessary to take into account all the species which defined the multiple event and attributed scores to the relevant segments .
We identified gene fusion/fission events in a coherent phylogenetic group of fungi , where completely sequenced and annotated genomes are avalaible , especially in the hemiascomycete yeasts . Despite this coherency , yeast and fungi encompass large evolutionary distances [26] . We selected 12 species among the fungi phylum tree as representatives , and used our method of event detection on the corresponding proteomes . This method only identified events which occurred inside protein coding genes , but , given the evolutionary distances between species , trying to detect events in intergenic regions would have certainly have been worthless . We expected gene redundancy since we worked with eukaryotic genomes . If duplicated genes were involved in a fusion / fission event , this event could accordingly be counted several times . To counter this redundancy , we built a set of paralogous groups ( P-groups ) for each proteome . The clustering of several protein sequences inside a P-group was based on sequence similarity and the length of the alignment , to ensure that the proteins shared the same architecture . The set of P-groups is thus a partition of the protein set in a given species ( see Dataset S1 ) . Our method is designed to detect events at the level of groups of paralogs ( P-groups ) and in several proteomes simultaneously ( see Material and Methods ) . The method also finds events which contain E-groups and C-groups belonging to the same species . We detected 1103 events , 176 of them being complex events were subsequently split , giving altogether 1680 elementary events ( Table 2 and Dataset S2 ) . These events only involve 12% of the P-groups over all the species , either as E-groups or C-groups . The Euascomycota and Zygomycota species happen to be the species the most involved in events; these species are those with the larger proteomes and hence the larger genomes . Indeed , we found a correlation between the genome size of a species and the number of events where it appears ( Figure 3 ) , a relation also found in a large genome survey [27] . Robust linear models were estimated to predict numbers of events from genome or proteome size , using 5000 bootstrap replications of the Huber regression . Distributions are symmetric overall but not entirely unimodal . Estimated coefficients suggest 15 events per megabase in the genome , or 0 . 06 events per protein in the proteome . Performing the analysis on a combination of the genome and proteome sizes only slightly improves the model , and is harder to visualize ( Figure S1 ) . Jackknife after bootstrap was used to evaluate the sensitivity of the distributions to deletion of individual observations . Species A . nidulans and R . oryzae would slightly tend to increase the coefficients , while N . crassa would tend to decrease it ( letters i , l and h , respectively in Figure 4 . Generally speaking , N . crassa is the most unusual data point and has fewer fusion/fission events than are predicted by the linear model . These correlations hold true for the events containing E-groups and C-groups of the same species , about 50% of the events; from a phylogeny angle , these events likely happened recently , that is , after the separation from the last common ancestor with the closest species . The length distributions of the different classes of P-groups and those of the alignments between P-groups used to define events , showed that the C-groups tend to be longer , and that the alignments covered up most of the E-group sequences ( Figure 5 ) . The average number of proteins per P-groups is higher in the C-group and E-group subsets compared to the O-groups ( 2 . 78 , 1 . 96 and 1 . 14 respectively ) , suggesting a higher frequency of duplication for the genes involved in fusion/fission events . We estimated the value of the fusion over fission ratio to be 1 . 28 from the number of events classified either as fusion events or as fission events ( see Material and Methods ) , although undecideable events ( 995 events , Table 2 ) could not be included in this calculation . This ratio is slightly in favor for fusion events which is in accordance with earlier studies [5] , [10] . We then assessed the robustness of the events by removing all the P-groups of one species at a time and then by checking how many events remained ( Table 2 , column Exc . ) . The number of events exclusive to one species ranged between 31 to 800 , suggesting that the set of events is not saturated and that it will increase upon the addition of new species . These numbers , along with the manual curation of the events , indicated that A . nidulans and R . oryzae genomes were likely to have undergone a large-scale reshuffling . Our method allowed us to retrieve well-known fusion examples , such as the event involving S . cerevisiae TRP1 , TRP3 genes [28] and their homologs in other species ( event GFE-1104 , see Dataset S2 ) , in which the corresponding polypeptides are separated entities in Hemiascomycota and fused in a single protein in Euascomycota , Archeascomycota , Basidiomycota and Zygomycota . Another well known example is the one which includes S . cerevisiae URA2 gene [29] . This very ancient event is thought to have happened before the branching of the fungus phylum , but is still visible as every species kept E-groups and C-groups ( event GFE-0970 ) . Other events can bring information to permit an annotation of ORFs based on the annotation of the fusion product . For instance , in event GFE-0238 , the two E-groups contain respectively the uncharacterized ORF YNR068C and YNR069C ( BSC5 ) ORF of unknown function whereas the S . cerevisiae C-group contains YML111W ( BUL2 ) , the gene of a “component of the Rsp5p E3-ubiquitin complex , involved in intracellular amino acid permease sorting” , according to Saccharomyces Genome Database annotations . We tested whether the biological functions of the proteins involved in the events did significantly differ from the functions of the proteins not included in the events . The C-groups were likely to contain several functional domains as they correspond to non overlapping E-groups . We thus chose to predict functional domains using Pfam profiles [23] followed by a conversion into GO terms which were clustered according to the GO-slim “yeast” ontology [24] . We removed the results which mapped to the roots of the ontology , as they were not informative enough; the presented results should therefore be considered as a sample . We followed the same process for E-groups except that , in order to save computation time , we gathered the predicted Pfam identifiers available in public databases for the proteins included in these E-groups . Only eight of the species had this feature ( see Material and methods ) , so again the results should be considered as a sample . In the relative frequency differences between E-groups vs . O-groups , only 5 GO slim categories presented a slight over- or under-representation of more than 1% ( Figure 6 ) : the nucleus is the under-represented cellular localization of the E-group proteins and the membrane is over represented , proteins classified in “helicase” molecular function are relatively more frequent in E-groups than in O-groups whereas those belonging to “transferase” and “protein binding” molecular functions are less frequent . Some studies reported that most pairs of proteins involved in fusions and with known function , were metabolic enzymes [12] , [30] . Another paper [27] indicates receptors and transcription factors to be among the most over-represented functions . As each study was done on a different group of of species , mostly bacteria , and as the set of events is not saturated , it is possible that the discrepancy between these results and ours merely reflects these facts . Moreover , as species may have different ecological constraints and thus different adaptative pressures , it is questionable whether a universal functional bias could be found . The pairs of associated GO-terms , derived from C-groups , were plotted in a square matrix ( Figure 7 ) . Pairs were preferentially located on the diagonal of the matrix , indicating that the domains associated in a C-group tend to belong to the same functional category . This point corroborates a similar situation in prokaryotes as found by [2] , [3] . Instead of focusing on the individual domain structure of fusion proteins , we chose to consider each event from an evolutionary perspective of genome rearrangement . We thus needed to distinguish two types of event . ( i ) The 365 events where at least one pair of E-groups correspond to adjacent genes on a chromosome , are likely to derive from nonsense or frameshift mutations which transform one coding sequence into two coding sequences or more . We did not take these events into consideration as they a priori do not involve genome rearrangement ( Table 2 , column Loc . ) . ( ii ) The 1315 other events , which contained nonadjacent E-group members , have likely occurred through a recombination event and were therefore the basis of our computation . We then , computed the position of each of these latter events in the evolution tree of the 12 fungal species , derived from the study of [25] , with the parsimonious assumption that a fusion or fission event might happen once during evolutionary history [10] . This tree is based on the comparison of the protein sequences translated from families of orthologous genes , and thus was called , in the framework of our study , the “mutation tree” ( Figure 8A ) . Keeping the same topology , we computed the weighted counts of events positioned in each segment of the tree ( see Material and Methods ) , and we changed the length of each tree segment accordingly to make a “recombination tree” ( Figure 8B ) . The use of the event localization weighted counts as a metric dramatically changed the aspect of the tree , making it obvious that some species ( N . crassa , A . nidulans and R . oryzae ) underwent massive genome shuffling .
Until now , the detection of fusion and fission events has been performed at the level of protein sequences with a post facto removal of supernumerary links due to paralogy . Also , earlier reports often did not look for events only defined in a single genome . We designed a large scale computation method to detect gene fusion and fission events in eukaryotes genomes taking into account their internal gene redundancy and thus operated at the level of groups of paralogs in the proteomes , named P-groups . The method basically consisted in building a graph of similarity relations between the protein sequences of several species and then pruning this graph according to rules specific to the definition of gene fusion/fission events . The method works simultaneously between every species as well as within species . The output consisted in connected components of the graph , each one defining a fusion/fission event . An event connects “composite” P-groups ( C-groups ) with “element” P-groups ( E-groups ) . Some of these events could need further splitting into several simpler topologies ( elementary events ) . We distinguished the only four possible topologies , depending on the ratio of E-groups to C-groups in an event . We applied our method to the kingdom of fungi which covers a large evolutionary range [14] , and in which a number of complete or near complete genome sequences are currently available . We chose to focus on a coherent phylogenetic group like fungi , where evolutionary events could be more easily identified , rather than between very distant species , where lifestyle and evolutionary history could make too many events to be immediately instructive . We eventually obtained a set of 1680 elementary fusion and fission events in the coding sequences of 12 fungal species . The number of detected events for a species is related to its genome and proteome size , as it appears to be the case in any species of the tree of life , with few exceptions typically associated with parasitic or infectious lifestyle [5] , [27] . The numbers of gene fusion/fission events confirm that these events are relatively rare [2] , [5] , albeit these numbers are provisional and underestimated as they are not saturated . Thus , the roster of detected events will very likely increase upon the addition of new species into the study . The fusion/fission ratio of 1 . 28 was less large than in comparable studies [5] , [10] , but was still in favor of the fusions . From a phylogeny point of view , we can expect such a tendency , as its beneficial effect would be to permit either the gathering of several biochemical functions into a single polypeptide molecule , thereby reducing the regulation burden of the cell , or the creation of new functions in a scenario which congregates gene duplication , gene fusion and sequence mutation . In the evolution from prokaryotes through lesser eukaryotes and up to higher eukaryotes , a witness of this fusion rate propensity is the observation that proteins have more different domains per protein , along with a larger repertoire of domain combinations [31] , [32] . As some of the genomes we used are thoroughly annotated , we could search for biases in the biological functions of the genes involved in fusion and fission events . Only a few were found . Similar findings were reported in other studies [3] , [27] , [30] , [33] although these functions do not appear to be the same in each report . This variation is not surprising since the different works were done on different sets of species , covering one or several kingdoms . In addition , the sets had unequal sizes and , as stated above , the number of events depends on the number of species . Nevertheless , the genes involved in an event in the fungal phylum tend to belong to the same functional category , a feature already found in other contexts [2] , [3] , [27] . Each event which does not involve adjacent genes on a chromosome , can be interpretated as a landmark of a recombinational event giving rise to gene fusion or fission . We positioned each of such events in the evolution tree of the 12 fungal species on the parsimonious assumption that each happens once during evolutionary history [10] . We only found 7 cases where two independent fissions were necessary for the event to be compatible with the phylogenetic tree ( data not shown ) . For each segment of the tree , the weighted counts of positions provided a metric between the 12 fungal species . This metric is independent of the gene mutation rate , and hence of the “mutation” phylogenetic tree . Rather , the metric depends on another aspect of genome evolution , recombination and gene shuffling . Under this perspective , some species underwent massive genome shuffling , compared to species with more stable chromosome architecture . Other metrics have been proposed to account for a recombitional distance between species , such as a metric based on synteny breakpoints [34] . However this last metric can only be applied on relatively narrow evolutionary distances where synteny exists , such as the vertebrates phylum . In contrast , the fungi phylum encompasses larger distances , for instance even in the Hemiascomycota sub-phylum , synteny blocks shared by Saccharomyces cerevisiae and Yarrowia lipolytica are too few and far between [14] . The metric we propose deals with traces of recombination events which can persist even if a genome has been totally shuffled . Several mechanisms of genome recombination could be put forward to explain the appearance of gene fusion and fission . Translocation or inversion can potentially fuse or split genes at their boundaries [35] , [36] . Segmental duplication can potentially fuse or split gene at their boundaries , as well as put next to each other exon containing sequences of different origin [37] . Horizontal gene tranfer in bacteria can account for 3% of the fused or split genes [10] . Horizontal gene tranfer is a minor mechanism in fungi [38] , but cannot be ruled out as a contributor for fusion/fission events . Partial copies of genes could be inserted in ectopic sites through retrotransposons , potentially creating chimerical genes at the insertion points [39] . Other plausible mechanisms would be transcription mediated gene fusion [40] or retroposition of trans-spliced genes [41] . Whatever the recombination mechanism , it is genetically easier to make a gene fusion than a gene fission [10] , because in gene fusion one partner could bring its promoter and the other its terminator , whereas in gene fission , one of the offspring has to come under the control of a new promoter in order to be expressed . This promoter inheritance and its possible evolutionary divergence will be accessible by testing the genes involved in the events where both C-groups and E-groups exist in the same species , as soon as large scale experimental expression data from the different species will be available . These events , which can be detected by our method , can be considered as evolutionary recent , and thus we may expect a correlation in the patterns of expression of genes from C-groups and those from the E-groups corresponding to the 5′ parts of the C-groups . During evolutionary time , genomes underwent recombinational events , some of which gave rise to gene fusion or fission , hence new genes and new proteins . Gene fusion and fission can abruptly change the length and composition of a gene , as opposed to point mutations which can alter gene content at a more continuous pace . Evolutionary pressure caused some of the genes produced by fusion or fission to be maintained and propagated until present time . Such genes could thus be considered as participating to the overall fitness and adaptation of a species . If we speculate that a species could be considered as a point in an “adaptation space , ” and ecological niches as regions of this space , we could propose the metric we defined as an indirect , or approximate , measure of distance between species in this space . The fact that there is no striking bias in the biological functions of the genes involved in gene fusion or fission , suggests that the recombinational events are basically random . This hypothesis has already been put forward , considering versatility and domain abundance in proteins [32] . Under this consideration , we could also propose that the metric we defined , does not need to be normalized for biological functions , as there is little bias . The events relative to the hemiascomycetes will be available in the Genolevures database [16] ( http://cbi . labri . fr/Genolevures/ ) . | One consequence of genome remodelling in evolution is the modification of genes , either by fusion with other genes , or by fission into several parts . By tracking the mathematical relations between groups of similar genes , rather than between individual genes , we can paint a global picture of remodelling across many species simultaneously . The strengths of our method are that it allows us to include highly redundant eukaryote genomes , and that it avoids alignment artifacts by representing each group of similar genes by a mathematical model . Applying our method to a set of fungal genomes , we confirmed first that the number of fusion/fission events is correlated with genome size , second that the fusion to fission ratio favors fusions , third that the set of events is not saturated , and fourth that while genes assembled in a fusion tend to have the same biochemical function , there appears to be little bias for the functions that are involved . Indeed , fusion and fission events are landmarks of random remodelling , independent of mutation rate: they define a metric of “recombination distance . ” This distance lets us build a genome evolution history of species and may well be a better measure than mutation distance of the process of adaptation . | [
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] | 2008 | Fusion and Fission of Genes Define a Metric between Fungal Genomes |
The ability to store nutrients in lipid droplets ( LDs ) is an ancient function that provides the primary source of metabolic energy during periods of nutrient insufficiency and between meals . The Fat storage-Inducing Transmembrane ( FIT ) proteins are conserved ER–resident proteins that facilitate fat storage by partitioning energy-rich triglycerides into LDs . FIT2 , the ancient ortholog of the FIT gene family first identified in mammals has two homologs in Saccharomyces cerevisiae ( SCS3 and YFT2 ) and other fungi of the Saccharomycotina lineage . Despite the coevolution of these genes for more than 170 million years and their divergence from higher eukaryotes , SCS3 , YFT2 , and the human FIT2 gene retain some common functions: expression of the yeast genes in a human embryonic kidney cell line promotes LD formation , and expression of human FIT2 in yeast rescues the inositol auxotrophy and chemical and genetic phenotypes of strains lacking SCS3 . To better understand the function of SCS3 and YFT2 , we investigated the chemical sensitivities of strains deleted for either or both genes and identified synthetic genetic interactions against the viable yeast gene-deletion collection . We show that SCS3 and YFT2 have shared and unique functions that connect major biosynthetic processes critical for cell growth . These include lipid metabolism , vesicular trafficking , transcription of phospholipid biosynthetic genes , and protein synthesis . The genetic data indicate that optimal strain fitness requires a balance between phospholipid synthesis and protein synthesis and that deletion of SCS3 and YFT2 impacts a regulatory mechanism that coordinates these processes . Part of this mechanism involves a role for SCS3 in communicating changes in the ER ( e . g . due to low inositol ) to Opi1-regulated transcription of phospholipid biosynthetic genes . We conclude that SCS3 and YFT2 are required for normal ER membrane biosynthesis in response to perturbations in lipid metabolism and ER stress .
Eukaryotic cells store neutral lipids ( triglycerides , TGs and steryl esters , SEs ) in cytoplasmic lipid droplets ( LDs ) surrounded by a monolayer of phospholipids and associated proteins [1] . These sub-cellular structures are mobile , dynamic organelles that grow and shrink depending on metabolic conditions [2]–[6] . Lipid droplets serve as the principal reservoirs for storing cellular energy and provide the building blocks for membrane lipids [1] , [3] , [4] . Stored lipid is accessed in a regulated fashion to provide energy through β-oxidation of fatty acids and substrates for the synthesis of other important cellular molecules , such as membrane phospholipids and eicosanoids . In addition , the products of TG hydrolysis , namely diacylglycerol ( DAG ) and free fatty acids , are important regulators of cellular signaling either directly or after subsequent metabolism ( e . g . to phosphatidic acid , PA; or ceramide ) [7] , [8] . LDs also play a central role in cholesterol homeostasis . The storage and release of sterols from LDs can alter the physical properties of membranes , affect the levels of circulating free cholesterol and contribute to the synthesis of steroid hormones and bile acids [1] , [9] . Despite broad recognition of the importance of LDs in cellular metabolism and in diseases associated with excessive lipid storage ( e . g . obesity , type II diabetes , atherosclerosis and fatty liver disease ) , their biogenesis , regulatory mechanisms and the nature of their interactions with other organelles are still largely unknown . Fat storage-Inducing Transmembrane proteins 1 & 2 ( FITM1/FIT1 and FITM2/FIT2 ) were identified as unique , evolutionarily conserved ER-resident proteins that affect the partitioning of TG into LDs [10] . The FIT proteins exhibit different tissue distributions with FIT1 highly expressed skeletal muscle , lower levels in heart , and with FIT2 broadly distributed and most abundant in adipose tissue . Studies in both cultured cells and mice have shown that overexpression of the FIT genes promotes the accumulation of LDs [10] , [11] . Importantly , these changes occur without inducing TG biosynthesis or inhibiting lipolysis . Conversely , knockdown of FIT gene expression decreases LD production in an adipocyte differentiation cell culture model and in zebrafish [10] . Thus , the data suggest that FIT protein function is critical for LD formation and can drive this process without affecting TG biosynthesis or turnover [10] . Budding yeast contains two FIT2 gene homologs , SCS3 and YFT2 , that are readily identified as the only homologous genes in S . cerevisiae in BLAST searches using the full-length human protein as a query ( with E values of 3 . 7E-5 and 5 . 5E-7 , respectively ) [10] . The majority of this sequence conservation occurs with the predicted transmembrane domains . In an experimentally-constrained global topology study of the yeast proteome , Scs3 and Yft2 ( like most polytopic yeast membrane proteins ) were predicted to have cytosolic N and C termini and therefore an even-number of transmembrane ( TM ) helices [12] . Consistent with this , a topological analysis of the murine FIT proteins demonstrated the cytoplasmic localization of both termini and a six-transmembrane domain organization [13] . The mechanism by which the mammalian FIT proteins mediate their effects on LD production has not been determined . However , recent work has found that the FIT proteins bind neutral lipids ( TG and DAG ) and that the extent and/or affinity of this interaction correlates with LD size [14] . Notably , a gain-of-function mutation ( FLL ( 157-9 ) AAA ) in the conserved TM4 domain of mouse FIT2 which increases TG binding and LD size has also been shown to alter the conformation of a cytoplasmic loop connecting TM domains 2 and 3 [13] . Thus , changes in the conformation of FIT proteins , potentially induced by TG binding , may influence the size of LDs [14] . The metabolic pathways leading to the formation of neutral lipids and the hydrolytic reactions catalyzing their mobilization are conserved between yeast and mammalian cells [15]–[17] . The major classes of glycerophospholipids in yeast membranes and their biosynthetic pathways are also conserved [15] , [18] . Yeast mutants with defects in TG synthesis , storage and catabolism have been identified and provide valuable models for understanding disease phenotypes such as obesity , lipodystrophy and lipotoxicity [19]–[21] . These studies support the use of yeast ( and other model organisms ) to better understand the biogenesis and function of LDs [22]–[24] . To this end , two visual screens of the viable yeast gene-deletion collection have been conducted to identify mutants defective in LD number and morphology . Both screens discovered numerous genes involved in LD biology and implicated functions that were not previously associated with this process [22] , [23] . However , a comparison of the genes reported in these studies reveals a limited overlapping set . This suggests that additional effectors of LD biogenesis and function remain to be identified . Consistent with this view , re-screening of the deletion collection on defined minimal media uncovered several gene-deletions that form supersized ( >1 µm diameter ) LDs [25] . We have initiated studies in yeast to identify chemical and synthetic genetic phenotypes associated with deletions of the mammalian FIT gene homologs , SCS3 and YFT2 . Despite the distant evolutionary relationship between these genes in fungi , the data show that SCS3 and YFT2 have shared as well as unique functions . Common functions are also demonstrated between the proteins in yeast and human systems . However , in contrast to FIT gene knockdown experiments in higher eukaryotes , deletion of SCS3 and/or YFT2 does not noticeably impact the number or size of LDs . A genetic network centered on SCS3 and YFT2 is presented that identifies a multitude of aggravating and alleviating interactions that connect major biosynthetic processes critical for cell growth . We find that the functions of SCS3 and YFT2 relate lipid metabolism and signaling with transcription of phospholipid biosynthetic genes and protein synthesis . More specifically , the data show that optimal strain fitness requires a balance between phospholipid synthesis and protein synthesis and suggest that the function of Scs3 and Yft2 impacts a regulatory response that serves to coordinate these processes .
Prior to the discovery of the FIT genes in mammals [10] , YFT2 was an uncharacterized open reading frame ( YDR319C ) whose relationship ( structural or functional ) to SCS3 was not appreciated . Consequently , none of the studies conducted to date on SCS3 have taken into account its potential functional redundancy with YFT2 . Phylogenetic evidence from 42 sequenced fungal genomes [26] indicates that YFT2 arose by a segmental duplication of SCS3 ( Figure S1 ) . Based on the distribution of YFT2 in these fungi , this segmental duplication preceded both the whole-genome duplication that characterizes many Saccharomyces species and their divergence from Candida species that decode CTG as serine instead of leucine ( Figure S1 ) . These observations indicate that SCS3 and YFT2 have been maintained in the Saccharomycotina lineage for >170 million years [26] and imply that each gene confers a selective advantage to those organisms . This notion is supported by genetic interaction data for duplicate gene pairs that have been retained following the genome-wide duplication [27] . The selective advantage to yeast of retaining SCS3 and YFT2 could reflect differences in their patterns of expression and/or functional differences . Indeed , differences in expression following certain chemical or genetic perturbations are evident using SPELL to search a compendium of ∼2400 microarray experiments [28] . To search for functional differences , we screened the scs3Δ and yft2Δ single deletion strains and the scs3Δ yft2Δ double deletion strain for growth defects under a wide variety of conditions [29] . The screen which included 25 different chemicals and drugs , various carbon sources , growth temperatures and osmolarity conditions revealed surprisingly few phenotypes . Cerulenin is a specific inhibitor of fatty acid synthase [30] . Gene deletions that are hypersensitive to cerulenin include INO2 and INO4 , which together encode the transcriptional activator that binds inositol-responsive ( UASINO ) elements and drives the expression of phospholipid biosynthetic enzymes and other lipid metabolic proteins [31] , [32] . Strains lacking either SCS3 or YFT2 or both genes were hypersensitive to cerulenin as indicated by the reduced cell density at saturation ( Figure 1a ) . In addition , the scs3Δ strain and the double deletion strain exhibited longer apparent doubling times during exponential growth ( Figure 1a ) . These properties show that deletion of SCS3 and/or YFT2 exacerbates the limited supply of fatty acids available via de novo synthesis . In addition to this shared phenotype , we found other phenotypes that were specific for strains lacking either SCS3 or YFT2 , consistent with these genes having unique functions . For example , strains deleted for YFT2 were resistant to fenpropimorph , which acts on 8 , 7-sterol isomerase ( Erg2 ) and sterol-14 reductase ( Erg24 ) to inhibit ergosterol biosynthesis ( Figure 1b ) . This resistance was not seen in the scs3Δ strain and was not enhanced in the scs3Δ yft2Δ double mutant indicating that SCS3 function does not contribute to this phenotype ( Figure 1b ) . In the absence of exogenous ergosterol , resistance to fenpropimorph has been reported to occur by a gain of function mutation in the plasma membrane H+-pantothenate symporter , FEN2 , or by loss of function mutations in FEN1 , an ER-localized fatty acid elongase involved in sphingolipid biosynthesis [33] . Accordingly , deletion of YFT2 may alter the activity of the membrane-localized Fen1 or Fen2 enzymes or negatively impact sphingolipid biosynthesis . Phenotypes associated specifically with deletion of SCS3 included cadmium hypersensitivity , paromomycin resistance and inositol auxotrophy ( Figure 1c–1e ) . Interestingly , the resistance to paromomycin does not reflect a general resistance of scs3Δ strains to aminoglycoside antibiotics ( e . g . G418 , data not shown ) but relates SCS3 function to increased translational fidelity based on the properties of a single base substitution within yeast 18S rRNA that also confers this phenotype [34] . Deletion of SCS3 increased the toxicity of cadmium ions . Although the mechanism of this toxicity is not thoroughly understood , cadmium is known to cause lipid peroxidation and like many metals , affects membrane fluidity [35] . Moreover , the uptake and detoxification of cadmium is strongly influenced by plasma membrane and vacuolar transporters [35] . These observations suggest that the cadmium hypersensitivity of scs3Δ strains results from an accumulation of metal ion , potentially due to altered membrane composition and/or transporter function ( see below ) . Inositol auxotrophy has been associated with deletion of SCS3 since the gene was first cloned [36] . Recently , the Ino- phenotype of the scs3Δ strain was attributed to decreased levels of inositol-3-phosphate synthase ( Ino1 ) , the rate-limiting enzyme in the synthesis of phosphatidylinositol ( PI ) [37] . This phenotype is not shared by strains lacking YFT2 ( Figure 1e ) . However , the function of Scs3 that allows growth in the absence of inositol is conserved in human FIT2: A genomic copy of the human FIT2 gene expressed from the yeast TDH3 promoter complemented the inositol auxotrophy of scs3Δ ( Figure 1e ) as well as other scs3-specific phenotypes ( e . g . paromomycin resistance , data not shown , and certain synthetic genetic phenotypes , Figure S2a , discussed below ) . Importantly , expression of human FIT2 in yeast did not activate the UPR , a measure of ER stress , Figure S2b ) . Thus , the complementation mutant phenotypes by human FIT2 indicates that SCS3 is related to the ancestral FIT gene that is broadly distributed in eukaryotes and supports the phylogenetic conclusion that YFT2 was derived by an ancient segmental duplication of SCS3 . Given the complementation of mutant phenotypes by human FIT2 in yeast , we used the fluorescent LD-specific dye BODIPY 493/503 to determine whether either SCS3 or YFT2 could drive the production of LDs in human embryonic kidney ( HEK293 ) cells . Remarkably , transient expression of either SCS3 or YFT2 under the control of the CMV promoter induced the appearance of LDs in HEK293 cells similar to overexpression of the human and mouse FIT genes ( Figure 2a ) [10] . Notably , the size of the LDs induced by the yeast proteins was smaller than for human FIT2 ( Figure 2a ) . Recently , Gross et al [14] showed that droplet size in this assay correlates with the affinity and/or extent of FIT protein binding to TG in vitro . Thus , the yeast proteins may have a reduced capacity for TG binding compared to human FIT2 . Overall , the results are consistent with the view that an ancient function involved in stimulating LD formation has been conserved in SCS3 , YFT2 and human FIT2 . Based on these findings and the ability of FIT2 knock-downs to dramatically diminish the appearance of LDs in NIH 3T3 L1 adipocytes and zebrafish [10] , we anticipated that a yeast strain lacking either SCS3 or YFT2 , or both genes , would exhibit a LD phenotype . Contrary to this expectation , fluorescence microscopy of log phase wild-type , scs3Δ , yft2Δ and scs3Δ yft2Δ cells stained with BODIPY 493/503 showed no differences in the number of LDs ( Figure 2b and data not shown ) . Similarly , no differences were found between the same strains at stationary phase although the number of droplets increased relative to log phase ( ∼13 versus 7–8 LDs/cell , in either fixed or unfixed cell preparations , data not shown ) . An examination of LD formation in the scs3Δ yft2Δ strain under several other growth conditions including low inositol and in oleate-containing media also did not reveal any phenotype ( Figure S3a and S3b ) . Taken at face value , these results suggest that SCS3 and YFT2 do not play a role in LD biogenesis in yeast . While this may be the case , the properties of FIT proteins in other organisms [10] , [11] , [38] , the complementation by human FIT2 in yeast ( Figure 1e and Figure S2a ) , the ability of SCS3 and YFT2 to induce LDs in human cells ( Figure 2a ) and the extensive genetic interactions linking SCS3 and YFT2 with lipid metabolism and transport in yeast ( see below ) , suggest that more complex interpretations should be considered: Yeast may possess alternative mechanisms for storing neutral lipids in droplets . Thus , the absence of one mechanism due to deletion of SCS3 and YFT2 may be compensated by the presence of another . Differences in the size of LDs between S . cerevisiae and organisms where FIT gene-dependent changes have been observed could also be important: The average diameter of LDs in yeast ranges between 0 . 35–0 . 45 µm compared to 1 . 1 µm in C . parapsilosis and up to 100 µm in higher eukaryotes [38]–[40] . To further investigate the unique and shared functions of SCS3 and YFT2 and to uncover functional relationships with other genes and processes , we conducted synthetic genetic array ( SGA ) analysis using query strains deleted for either or both genes ( see Materials and Methods ) . Part of our rationale for including the scs3Δ yft2Δ double mutant strain as a query was to identify genetic interactions associated with the conserved redundant function that drives LD formation in human cells ( Figure 2a ) . Colony sizes of the double and triple mutant strains generated with the three query strains were scored by computer-based analysis of digital images and used to determine strain fitness by comparison to a reference set of images from control screens . Genetic interactions were quantitatively scored as the difference between the observed and expected fitness , determined using a multiplicative model [41] for each double or triple mutant strain ( see Materials and Methods ) . As a measure of the quality of the data , we determined that the colony sizes of 93% of the strains ( ∼4000 out of 4292 strains in each screen ) had coefficients of variation less than 22 . 5% with an average of 12±7% for the 14504 his3Δ::kanMX control strains that were used for colony size normalization ( see Materials and Methods ) . In addition , we examined the genetic interaction score ( ε values ) for all strains in each screen and found them to be normally distributed and centered on zero indicating no bias in the multiplicative model ( Figure 3a ) . Guided by a recent large scale study [42] , we set a stringent threshold for differences in colony size and p value ( ≥40 pixels and ≤0 . 01 , respectively ) and examined the genetic interaction overlap in pairwise comparisons of the three screens ( Figure 3b ) . Of the 354 interactions with SCS3 that met the preceding criteria , ∼50% were identified with the double mutant query . A similar percentage of interactions with the double mutant query were also identified among the 151 interactions with YFT2 . Overall , 221 interactions were shared in screens involving two or more of the query strains indicating a high probability that the majority of these are true positive interactions . Consistent with the view that SCS3 and YFT2 retain some redundant function ( see above ) , we found that the total number of interactions increased significantly using the double mutant query ( Figure 3b ) . This reflects the loss of buffering capacity for the redundant function when both genes are deleted . Similar observations have been made in screens of the partially redundant G1 cyclins , CLN1 and CLN2 [43] and other duplicate gene pairs [27] . Pairwise comparisons of genetic interaction scores ( ε values ) among the 221 “true positives” revealed a sharp cutoff for aggravating and alleviating interactions at −0 . 1 and 0 . 2 , respectively , in the single mutant query screens ( Figure 3c ) . The same sharp cutoffs were also seen in comparisons of the single versus double mutant screens although the boundary for aggravating effects involving the double mutant query was slightly higher at −0 . 05 ( Figure S4 ) . Given the large number of interactions in the three screens ( Figure 3b ) , we set a high stringency cutoff of −0 . 12 for aggravating interactions , as reported by the Costanzo et al . , [42] and 0 . 2 for alleviating interactions ( Figure 3c ) . This produced a set of 636 interactions with known ( verified ) or uncharacterized genes ( Table S1 ) . During this analysis , we found that our screens identified numerous genes annotated as “dubious ORFs” in SGD that overlapped the coding or promoter regions of known genes that were also on the array . These dubious ORFs provided an additional way of testing the reproducibility of the SGA data: We compiled a list of all the dubious ORFs that met our three criteria ( pixel size , p value and ε score ) in at least one of the three screens and then compared their ε values for all three queries with the corresponding values for the overlapping verified genes . The scores were highly correlated ( Pearson's r = 0 . 75 , Figure S5 ) . Synthetic genetic interactions reveal functional relationships between genes [44] , [45] . Based on the gene-specific and shared phenotypes described above ( Figure 1 ) , we expected our SGA analysis to identify sets of genetic interactions that reflect the unique and common functions of SCS3 and YFT2 . Indeed , we identified many interactions with one or the other gene as well as interactions that required deletion of both genes ( Figure 4a–4c ) . These genetic data provide strong support for the existence of unique and shared functions for SCS3 and YFT2 in yeast . In addition , we identified a fourth class of interactions where opposing phenotypes were found for single gene deletions versus the double deletion strain: Deletions of numerous genes showed a strong aggravating phenotype in combination with scs3Δ and/or yft2Δ single mutants but a strong alleviating phenotype with the scs3Δ yft2Δ double mutant ( Figure 4d ) . In other cases , aggravating phenotypes obtained with each single mutant were genetically suppressed ( i . e . ε∼0 in the double mutant ) . Since the fitness of the scs3Δ , yft2Δ and scs3Δ yft2Δ strains is very similar to wild-type ( ε = 0 . 96 , 0 . 96 and 0 . 89 , respectively , versus 1 . 0 for the wild-type strain , see Materials and Methods ) , the interpretation of suppressing or alleviating interactions with these strains primarily involves rescuing the poor fitness of the respective deletion strains on the array . Noteworthy examples are provided by SAC1 and CHS3 . Sac1 is a phosphatidylinositol phosphate ( PtdInsP ) phosphatase that is localized to the ER and Golgi and functions in protein trafficking , secretion and cell wall maintenance . Deletion of SAC1 dramatically and selectively increases the level of PtdIns ( 4 ) P ( 8–12 fold ) and causes missorting of Chs3 to the vacuole [46] . Chs3 is responsible for the majority of chitin synthesis in the cell wall and the disruption of its normal cycling between the Golgi and the plasma membrane in the SAC1 mutant compromises cell wall maintenance [46] . Deletion of SAC1 substantially reduces strain fitness ( to 0 . 48 relative to wild-type ) and this is further aggravated by deletion of either SCS3 or YFT2 . However , the fitness of the triple mutant strain is significantly improved ( to 0 . 78 relative to wild-type ) indicating the strong alleviating effect of deleting both SCS3 and YFT2 . On the other hand , deletion of CHS3 does not have a significant impact on strain fitness by itself but exhibits reduced fitness in combination with either SCS3 or YFT2 . These effects are suppressed in the triple mutant strain . The converse of this pattern of genetic interactions was also seen ( albeit less often ) where each query gene-deletion had alleviating interactions with a particular array gene deletion that was reversed when both SCS3 and YFT2 were deleted . These types of genetic interactions indicate that the functions of SCS3 and YFT2 ( or the processes that they impact ) sometimes antagonize one another . To identify the biological processes that are enriched among the genes identified in our screens , we compared the frequencies of 17 broad GO bioprocess terms among array genes [42] and the 636 genes that interacted with SCS3 and/or YFT2 . Five GO bioprocess terms were significantly over-represented in our data ( Figure 5 ) : Chromatin and transcription , ribosomes and translation , lipid , sterol and fatty acid biosynthesis , ER-Golgi traffic and signaling-stress response . These functional associations illustrate the fundamental importance and broad impact of SCS3 and YFT2 in the cell: They linked together processes ( e . g . chromatin-transcription and secretion ) that individually are among the most highly connected in the global genetic landscape [42] . This is further reflected by a yeast GO-slim component analysis which shows that the cellular distribution of genes that are synthetic with SCS3 and/or YFT2 is very similar to the genome as a whole ( Spearman Rank Order Correlation rs = 0 . 87 for the top 18 terms , even after excluding the cytoplasm and nucleus , which are the most abundant terms , Table S2 ) . Consistent with the localization of the mammalian FIT proteins and Scs3 to the ER , this compartment was the most significantly overrepresented among genes interacting with SCS3 and/or YFT2 ( p = 0 . 002 ) followed by the Golgi and the mitochondrial envelope ( Table S2 ) . Interestingly , a yeast GO-slim component analysis of genes that were synthetic with either SCS3 or YFT2 revealed an over-representation of the former with the ER and the latter with the plasma membrane and the mitochondrial envelope suggesting a cellular bias in their functional relationships . Given the effects of the mammalian FIT genes on LD formation and the enrichment of genetic interactions linking SCS3 and YFT2 to lipid metabolism ( Figure 5 , [10] , [14] ) , we were interested to know whether the scs3Δ yft2Δ strain had altered levels of phospho- and/or neutral lipids . To assess this , we performed metabolic labeling of the wild-type and double deletion strains using either 14C-acetate or 32P-orthophosphate and analyzed the cell extracts by thin-layer chromatography . In parallel , we also prepared unlabeled samples for quantitative mass spectrometry . These analyses did not reveal any significant differences in the levels of total cellular neutral or polar lipid species ( Figure S6 and data not shown ) . To understand how the lipid metabolic functions identified in our screens affected strain fitness , we mapped the genetic interaction data onto known lipid metabolic pathways using gene descriptions annotated in SGD . Despite the fact that some of the genes in these pathways are essential or were otherwise absent from the deletion array , this analysis identified genetic interactions affecting multiple biochemical steps in the synthesis of phospholipids , inositol phosphates and sphingolipids ( Figure 6 ) . Additional interactions identified genes that function in sterol and fatty acid metabolism ( Table S1 ) . Mutations that negatively impact the synthesis of inositol or its conversion into PI showed aggravating interactions . This was also true for mutations that are defective in the synthesis of PA from glycerol-3-phosphate ( Gro-3-P ) , DHAP and LysoPA ( Figure 6 ) . These effects are consistent with PA and the CDP-DAG pathway providing the main route for the synthesis of PI and the other major phospholipids ( Figure 6 ) [47] . We infer that deletion of SCS3 and/or YFT2 impairs the synthesis of PI in a manner that is further exacerbated by deletion of different components in this pathway . In contrast to the synthesis of PI , deletions of the methyltransferases ( CHO2 and OPI3 ) that convert PE to PC in the terminal steps of the CDP-DAG pathway had the opposite phenotype ( Figure 6 ) . Deletions of both SCS3 and YFT2 , which together have little effect on fitness compared to the wild-type strain , suppressed the more severe fitness defect of the cho2Δ and opi3Δ strains . These alleviating phenotypes suggest that deletion of SCS3 and YFT2 may compensate for the reduced synthesis of PC in the methyltransferase mutant strains [48] , [49] . Similarly , alleviating interactions were also found with deletions of all four components of the ERMES complex , an ER-mitochondrial tethering complex that is important for the efficient exchange of PS and PE between these compartments [50] . These interactions and the fact that alleviating phenotypes tend to be associated with genes in a common pathway or process [44] , [45] predict that deletion of SCS3 and YFT2 can bypass defects in the CDP-DAG pathway related to PC synthesis . A possible mechanism by which deletion of SCS3 and YFT2 could increase the fitness of the cho2Δ and opi3Δ strains involves increasing the flux of acyl chains and DAG through the Kennedy pathway for PC synthesis ( Figure 6 ) . In this way , DAG could be diverted from conversion into TGs , consistent with the reduction in LD formation upon knockdown [10] or loss of function of the mammalian FIT genes [14] . In support of this hypothesis , we found that deletion of the choline/ethanolamine transporter , Hnm1 , resulted in aggravating genetic interactions in scs3Δ and scs3Δ yft2Δ strains indicating the importance of a functional Kennedy pathway in these strains ( Table S1 ) . In addition to being a major cellular phospholipid , PI is also a key substrate in the synthesis of inositol phosphates and complex sphingolipids [46] , [51]–[53] . Notably , deletion of genes in downstream steps in these pathways showed strong alleviating phenotypes with SCS3 and YFT2 ( Figure 6 ) . The turnover of complex sphingolipids , which comprise ∼12 mol percent of the yeast lipidome [51] , is critical for survival in mammals and is important in yeast for signaling the response to different types of cellular stress [53] . This turnover reaction is catalyzed by Isc1 , a phospholipase C-type enzyme that removes the polar head groups from complex sphingolipids to regenerate ceramides . Isc1 function confers resistance to a variety of cellular stresses including heat shock where ceramide levels rise dramatically to affect a transient arrest of the cell cycle and induce synthesis of the cryoprotectant trehalose [53] . Similarly , Isc1 is critical for growth on non-fermentable carbon sources and is known to change its localization from the ER to the outer leaflet of the mitochondrial membrane during the shift from fermentation to respiratory metabolism . Subsequent interactions with mitochondrial lipids such as cardiolipin increase Isc1 enzyme activity and thus the level of phytoceramide [53] . Accumulated evidence suggests that the ceramide generated by this turnover functions as a signaling molecule affecting numerous processes [53] . Accordingly , the ability of SCS3 and YFT2 gene deletions to rescue the defective growth of the isc1Δ strain may involve enhanced ceramide signaling or enhanced function of a ceramide-regulated process . We note that the underlying mechanism of this enhanced functionality is not likely to involve elevated de novo synthesis of ceramide or complex sphingolipids since deletions of multiple components in this pathway showed aggravating phenotypes ( negative genetic interactions ) in combination with deletions of SCS3 and/or YFT2 ( Figure 6 ) . A role for SCS3 and YFT2 in cellular signaling is also suggested in relation to soluble inositol phosphates ( IPs ) since deletion of either one or both genes rescues the poor growth of a strain lacking Arg82 , the inositol polyphosphate multikinase responsible for synthesizing IP4 and IP5 ( Figure 6 , [52] ) . Deletion of ARG82 has pleiotropic effects on cellular function including gene transcription , nuclear mRNA export and telomere elongation . Current data suggest that these processes are regulated by IP4 , IP5 and/or their more phosphorylated forms ( IP6 and pyrophosphate derivatives ) which function as controlling ligands for different biochemical activities ( e . g . the ATP-dependent RNA helicase Dbp5 , the INO80 , SWI/SNF and RSC chromatin remodeling complexes and the Pho80-Pho85 cyclin-dependent kinase , [54] ) . Since Arg82 is the only enzyme known to synthesize IP4 and IP5 in yeast , it is not clear how deletion of SCS3 and YFT2 might bypass their absence in the arg82Δ strain . Other effects on inositol phosphorylation are suggested by genetic interactions with genes encoding several PI kinases and phosphatases or their regulators ( e . g . SAC1 , IRS4 , YMR1 and FAB1 , Figure 6 and Table S1 ) . Notably , deletion of the Sac1 phosphatase , with its high selectivity towards PI ( 4 ) P ( noted above ) , suppresses defects associated with a temperature-sensitive mutant of the opposing PI 4-kinase , Stt4 , localized at the plasma membrane [46] . The specificity of this suppression , which is not seen for other PI-4- kinases , together with other evidence [46] , supports the existence of discrete pools of PI ( 4 ) P with specific cellular functions and suggest a close physical association between the ER and the plasma membrane [46] . Based on this knowledge , suppression of the growth defect of the sac1Δ strain by deletion of both SCS3 and YFT2 could involve or impact functions at the plasma membrane . The strong genetic associations that link SCS3 and YFT2 to the synthesis of phospholipids , sphingolipids and inositol phosphates are reflected in the cross-talk between these pathways involving lipid-derived second messengers such as phosphatidic acid ( PA ) and diacylglycerol ( DAG ) [47] , [55] , [56] . These associations are reinforced by genetic interactions involving PAH1 and DGK1 which encode PA phosphatase ( lipin ) and DAG kinase , respectively . Deletions of PAH1 and DGK1 , which catalyze the interconversion of PA and DAG [57] , [58] , resulted in aggravating genetic interactions with SCS3 and YFT2 ( Figure 6 ) and suggest that balancing the levels of these lipid precursors/signaling molecules may be important for optimal growth . Proof of a signaling role for DAG in yeast via the canonical mechanism of PKC activation has proven elusive [55] but DAG generated by Pah1 has recently been implicated in LD biogenesis: Deletion of PAH1 reduces the number of LDs and this effect is suppressed if the cells are also deleted for DGK1 [59] . Given the ability of the mammalian FIT genes to bind DAG and TG and their associated LD phenotypes [10] , [14] , the genetic interaction between SCS3/YFT2 and PAH1 is entirely consistent with a functional association relating DAG metabolism and LDs . In addition , deletion of PAH1 ( or overexpression of Dgk1 ) elevates the concentration of PA which up-regulates the transcription of phospholipid biosynthetic genes and drives a dramatic expansion of the nuclear/ER membrane ( reviewed in [60] ) . This is achieved by controlling the nuclear concentration of the transcriptional repressor Opi1 which is otherwise sequestered on the ER membrane in a complex with PA and the tail-anchored protein Scs2 [60] . Genetic interactions with this machinery are presented in the next section . Overall the interactions described above provide further evidence that SCS3 and YFT2 function in yeast affects lipid signaling and homeostasis . Overexpression of Nte1 , a phosphatidylcholine B-type phospholipase , suppresses the inositol auxotrophy of scs3Δ ( and several other Ino- strains ) and restores normal levels of Ino1 protein [37] . Similarly , the Ino- phenotype of a large number of gene-deletion strains , including SCS3 , can be suppressed by deleting the Opi1 repressor [61] which leads to constitutive expression of UASINO-regulated genes , increased inositol synthesis and altered lipid composition [31] , [62] , [63] . These data suggest that the inositol auxotrophy of scs3Δ strains ( Figure 1 ) could be due to misregulated INO1 gene transcription . Consistent with this , we found that gene-deletions yielding synthetic phenotypes with SCS3 and YFT2 were enriched for known transcription components ( Figure 5 ) and many of these interactions involve regulators of UASINO genes [32] . For example , deletions of known transcriptional repressors were found to have alleviating phenotypes when combined with deletions of both SCS3 and YFT2 ( Figure 7 ) . Among these were four subunits of the Rpd3 ( L ) histone deacetylase ( HDAC ) complex ( SIN3 , SAP30 , PHO23 and DEP1 ) which is recruited to the INO1 promoter via its interaction with the DNA binding factor Ume6 [32] . The fitness of these deletion strains is improved by deleting both SCS3 and YFT2 and this presumably coincides with reduced transcription of INO1 and other target genes in the triple mutants relative to the Rpd3 ( L ) single mutants ( Figure 7 ) . Conversely , deletions of activators or anti-repressors of UASINO-regulated genes were found to have aggravating phenotypes in the scs3Δ yft2Δ strain ( Figure 7 ) . Notably , deletion of SCS2 which tethers Opi1 to the ER , and subunits of the SAGA chromatin modifying complex , resulted in synthetic fitness defects , consistent with SCS3 and YFT2 gene deletions reducing the already diminished transcription of UASINO-regulated genes in these strains . Interestingly , the SAGA subunits are linked genetically and biochemically to other components of the transcription machinery that also interact with SCS3 and YFT2 [64] , [65] . Deletions of the genes encoding these transcription components reveal a coherent pattern of genetic interactions that is consistent with current models of their function in transcription ( Figure 7a–7b ) : Alleviating interactions were found for the Bre1-Lge1 E3 ubiquitin ligase complex along with several subunits of the Paf1 complex which together are important for monoubiquitination of histone H2B during transcription initiation and elongation ( Figure 7 , [64] ) . In contrast , the failure to deubiquitinate H2B has negative consequences for cellular fitness in the scs3Δ yft2Δ strain based on the aggravating phenotypes of SAGA subunit deletions in its deubiquitination module ( Sgf11 and Ubp8 ) and in Sgf73 which tethers this module to the rest of the SAGA complex [65] . This is consistent with the important role of H2B deubiqutination for the recruitment of kinases , such as Ctk1 , to the elongating polymerase [64] , [65] . Subsequent phosphorylation of serine 2 in the carboxy terminal domain of RNA polymerase II provides a binding site for the Set2 methyltransferase leading to histone H3 K36 trimethylation . Recent work has shown that this modification is important for activated transcription of UASINO-regulated genes [66] and for recruitment of the Rpd3 ( S ) HDAC complex which inhibits cryptic initiation within the transcription unit [67] . Accordingly , deletions of SET2 and specific subunits of the Rpd3 ( S ) complex ( RCO1 and EAF3 ) exhibit aggravating interactions with SCS3 and YFT2 ( Figure 7a ) . Other aggravating interactions were identified with subunits of the NuA4 lysine acetyl transferase complex which acetylates histone H2A and its variant H2A . Z along with histone H4 ( Figure 7 ) . Like SAGA , NuA4 is recruited to the promoters and coding regions of genes and cooperates with SAGA in nucleosome disassembly and transcription elongation [68] . Although NuA4 has not been physically associated with UASINO genes , NuA4 mutants are inositol excretors implying a paradoxical increase in INO1 transcription [69] . The mechanism underlying this phenotype is unknown . Induction of INO1 transcription upon inositol starvation involves the repositioning of nucleosomes in the promoter by the INO80 chromatin remodeling complex [70] . In contrast to other activators of UASINO gene transcription ( e . g . SAGA ) , deletions of multiple INO80 subunits led to alleviating rather than aggravating phenotypes in the scs3Δ yft2Δ strain ( Figure 7 ) . This apparent inconsistency is explained by the functional redundancy of INO80 with another chromatin remodeler , SWI/SNF , which masks the effect of INO80 subunit deletions on INO1 gene activation [71] . Additionally , the INO80 complex serves other functions in the cell [70] , [72] , [73] . These include the reassembly of nucleosomes in the transcribed regions of genes to repress transcription during adaptation to cellular stress [72] . Thus , our observations are consistent with the loss of SCS3 and YFT2 rescuing the poor fitness of INO80 complex mutants that are deficient in a repressive function in transcription . This interpretation is supported by strong negative genetic interactions between the INO80 complex and the Rpd3 ( L ) HDAC complex indicating that they function in redundant parallel pathways ( Figure 7 , [42] , [44] ) . INO1 transcription is also positively regulated by activators of the unfolded protein response ( UPR ) pathway , namely the Ire1 sensor kinase/endoribonuclease and the Hac1 transcription factor [74] . Maximal levels of INO1 gene transcription requires Ire1 and Hac1 and starvation for inositol ( reviewed in [32] ) . Conversely , deletion of IRE1 or HAC1 confers inositol auxotrophy [75] , [76] . Consistent with these observations , we found that IRE1 and HAC1 exhibited aggravating genetic interactions with SCS3 and YFT2 , similar to other positive regulators in this system ( Figure 7 , see above ) . Although activation of the UPR by heat stress does not induce INO1 transcription [32] , the role of IRE1 and HAC1 in this process has led to the view that inositol starvation and the resulting changes in lipid metabolism and stress response pathways causes stress on the ER ( e . g . see refs . [37] , [60] ) . Given the known localization of Scs3 and mammalian FIT proteins in the ER membrane [10] , [77] and the negative genetic interactions with IRE1 and HAC1 , we examined whether deletion of SCS3 and YFT2 creates an ER stress that constitutively activates the UPR . For cells grown in synthetic complete medium , northern analysis of HAC1 mRNA splicing revealed no evidence for UPR induction in the scs3Δ yft2Δ strain and a normal UPR response to acute treatment with DTT ( Figure 7c ) . In summary , the data presented in this section indicate that deletion of SCS3 and YFT2 perturbs the regulation of phospholipid biosynthetic genes and sensitizes the cell to additional changes in transcriptional activities that are also involved in this process . Among the genetic interactions affecting protein synthesis , we found that reduced growth due to pseudo-haploinsufficiency of multiple RPs was suppressed by deletion of both SCS3 and YFT2 ( Figure 8 ) . This phenotype was not restricted to RPs but was also seen for positive regulators of ribosome biogenesis such as the transcription factor HMO1 and proteins involved in rRNA processing ( e . g . Bud21 ) and ribosome assembly ( e . g . Rei1 ) ( Table S1 ) . These results imply that the characteristic reduced growth of RP gene-deletion strains is not simply due to a reduction in the number of ribosomes but includes a fitness defect ( i . e . a biological imbalance with other processes ) that can be corrected upon deletion of both SCS3 and YFT2 . To gain some insight into the nature of these processes , we examined all of the alleviating interactions identified in screens of RP gene-deletion strains reported by Costanzo et al [42] , [78] . With the exception of three RP genes , the most frequently recovered alleviating gene-deletion ( in 10 of 37 screens ) was the Cho2 methyltransferase involved in the conversion of PE to PC . Importantly , CHO2 also exhibits alleviating interactions with SCS3 and YFT2 ( Figure 6 ) . These interactions include five triplet genetic motifs involving mutually positive interactions ( i . e . five different RP genes interacting with both CHO2 and SCS3/YFT2 , Figure 8 ) . Together with numerous additional interactions between RP genes and either CHO2 or SCS3/YFT2 , the data strongly suggest that these genes are associated via a common pathway [79] . We propose that the functional significance of these associations is to balance the energy-intensive synthesis of ribosomes with the synthesis of phospholipids in the ER to achieve optimal strain fitness for both of these growth-related processes . In support of this view , it has been known for some time that defects in the secretory pathway that block the delivery of vesicles to the plasma membrane ( and thus inhibit plasma membrane expansion ) activate a Rho1-Pkc1-dependent signaling pathway to repress transcription of rRNA , tRNA and RP genes [80]–[82] . Deletion of SCS3 and YFT2 resulted in strong aggravating phenotypes with deletions such as SEY1 and ICE2 ( proteins linked to ER morphology ) and components of the secretory pathway ( Table S1 ) [83] . This suggested a requirement for SCS3 and YFT2 in normal ER function . To examine this relationship further , we followed the kinetics of vacuolar carboxypeptidase Y processing and maturation as an indicator of protein trafficking between the ER , the Golgi and the vacuole . No gross defects were evident in the scs3Δ yft2Δ strain ( Figure S7a ) . In a more stringent test of ER function , we employed a conditional allele of SEC13 ( sec13-1 ) that causes secretory stress and activates the UPR as a result of defects in COPII-mediated vesicle formation [84] . Deletion of YFT2 , SCS3 or both genes in the sec13-1 strain caused an increasingly strong synthetic sick growth phenotype at the permissive temperature of 22°C ( Figure 9a ) and at higher temperatures ( data not shown ) . The sec13-1 strain exhibits altered lipid metabolism at 25°C and upon shifting to its non-permissive temperature , accumulates TGs at the expense of phospholipid synthesis with a concomitant increase in the number of LDs [85] . However , deletion of SCS3 and YFT2 did not affect the number , apparent size or distribution of LDs in this strain despite the synthetic growth defect ( in either log phase , Figure S3c or stationary phase , data not shown ) . Thus , the strong aggravating phenotype of the sec13-1 scs3Δ yft2Δ strain may be explained by hypersensitivity to secretory stress alone or in combination with altered phospholipid composition . Indeed , deletion of SCS3 has been reported to cause elevated UPRE-reporter gene expression [86] , [87] and to confer hypersensitivity to tunicamycin in UPR-defective strains , consistent with elevated levels of ER stress [88] . These observations prompted a closer evaluation of SCS3 and YFT2 function during induction and attenuation of the UPR . Strains deleted for SCS3 and YFT2 are indistinguishable from wild-type in their acute response to DTT-induced ER stress in nutrient-replete medium ( i . e . normal HAC1 mRNA splicing as described above , Figure 7c ) and do not exhibit significant sensitivity to tunicamycin ( Figure S7b ) . In contrast , scs3Δ strains are hypersensitive to growth on medium containing DTT ( Figure 9b ) suggesting an inability to tolerate the accumulation of unfolded proteins and implying a role in resistance to chronic ER stress . To further explore this possibility , we examined growth in inositol-depleted medium , a UPR-inducing condition that is independent of unfolded protein accumulation [87] . In wild-type strains , low concentrations of inositol ( e . g . 10 µM , sufficient to rescue the inositol auxotrophy of scs3Δ strains ) induce transcription of phospholipid biosynthetic genes ( e . g . INO1 ) and activate the UPR ( increase HAC1 pre-mRNA splicing ) [31] ( Figure 10 , compare 10 µM versus 100 µM inositol ) . However , the SCS3 and YFT2 deletion strains were significantly compromised for both of these responses ( Figure 10 ) . Importantly , deletion of the Opi1 transcriptional repressor ( which confers inositol prototropy to scs3Δ strains and alters the expression of many UASINO genes [31] , [61] ) , restored wild-type levels of INO1 transcription to the SCS3 and YFT2 deletion strains ( Figure 10 ) . That SCS3 was identified along with two known regulators of UASINO gene transcription ( SCS2 , [89] and SCS1/INO2 , [90] ) in a screen for suppressors of a choline-sensitive mutant [36] , coupled with its inability to derepress INO1 transcription ( Figure 10 ) , suggests that the primary defect underlying the inositol auxotrophy of scs3Δ strains is the failure to appropriately regulate transcription of INO1 and other genes involved in lipid metabolism . Phospholipid biosynthesis is induced as part of the normal response to ER stress [91] , yet a failure to inactivate Ire1 and attenuate HAC1 mRNA splicing and the UPR has recently been reported to reduce cell survival under conditions of chronic ER stress [92] , [93] . We found that the hypersensitivity of SCS3 deletion strains to growth on medium containing DTT was independent of the transcriptional repressor Opi1 ( Figure 9B ) , suggesting that the constitutive lipid biogenesis and membrane expansion that follows deletion of OPI1 [91] is not sufficient to rescue the ER stress-induced growth defect in this strain . Indeed , attenuation of the UPR , as detected in a time course of HAC1 mRNA splicing after washout of DTT was compromised in SCS3 deletion strains ( Figure S8 , and data not shown ) . Together , the preceding data suggest a requirement for SCS3 and YFT2 in the normal response to and recovery from ER stress under specific conditions: Chronic exposure to unfolded protein accumulation induced by DTT and growth in low inositol . The defective transcriptional response of scs3Δ and yft2Δ strains to low inositol ( Figure 10 ) suggests that Scs3 and Yft2 may be required for phospholipid biosynthesis in response to ER stress , perhaps contributing to the retention of Opi1 at the ER . The genetic interactions of SCS3 and YFT2 reveal functional relationships with a large network of genes and with biological processes that are among the most highly connected in the overall genetic landscape of the cell [42] . This high connectivity may reflect an important function of the corresponding proteins related to their potential binding of neutral lipids , their localization within the ER membrane and the role of the ER in secretion , stress response and transcriptional control . The finding that SCS3 and YFT2 deletion strains are compromised in the regulation of INO1 transcription and fail to induce splicing of HAC1 mRNA in response to growth in low inositol ( Figure 10 ) demonstrates that these strains have an intrinsic defect in ER membrane function . Together the results suggest that SCS3 and YFT2 are required for normal ER membrane biosynthesis in response to perturbations in lipid metabolism and ER stress . The mechanisms linking Scs3 and Yft2 protein function to downstream cellular responses are unknown . However , the recent demonstration that the mammalian FIT proteins bind DAG and TG suggests that these interactions may affect local properties of the ER membrane such as its curvature or stability ( see ref . [59] ) or the local distribution of PA , altering the dynamics of Opi1-membrane interactions and the function of ER transmembrane proteins such as Ire1 .
Synthetic complete media contained 400 µM myo-inositol unless otherwise stated . Low or no inositol media was prepared from inositol-free YNB ( Difco ) and amino acid dropout mixes . YPO media contained 0 . 3% yeast extract , 0 . 5% peptone , 0 . 1% glucose , 0 . 5% KH2PO4 and 0 . 1% oleic acid ( YPO ) and 0 . 2% Tween 80 [94] . For experiments other than SGA analysis , single , double and triple gene-deletions of SCS3 , YFT2 and OPI1 were generated de novo in BY4742 using standard one step gene replacement and dominant drug selectable markers [95] . Paromomycin , DTT , cerulenin , oleate and fenpropimorph were purchased from Sigma-Aldrich . Query strains were derived from Y7092 [96] by deleting SCS3 ( scs3Δ::natMX ) , YFT2 ( yft2Δ::natMX ) or both genes ( scs3Δ::natMX yft2Δ::URA3 ) and screened by standard SGA methods against an array of 4292 strains representing a healthy subset ( i . e . minimal growth defects ) of the viable gene-deletion collection [43] , [96] . The gene-deletion array contained duplicate copies of each strain in a 1536 colony per plate format and was screened in triplicate for each query using a Singer Instruments RoToR hda robot . Digital images of the double or triple mutant colonies were captured and analyzed using ColonyImager software [43] to determine colony pixel sizes . These data were compared to colony pixel size data obtained from quintuplicate screens of Y8835 [96] , a control query strain ( ura3Δ::natMX ) , against the same deletion array . Each plate of the deletion array contained a two-colony perimeter of the same his3Δ::kanMX strain [96] . The 148 colonies on the inner perimeter compete for nutrients with their neighbors in a similar way as colonies located within this perimeter . These colonies provided a reliable measure for the normalization of colony size across all plates in the five control and nine query screens . Normalization followed the general scheme described by Collins et al . , [97] . The median pixel size ( area ) of the his3Δ::kanMX colonies on the inner perimeter of each plate ( the plate median ) and of all 98 plates ( experimental median , 343 pixels ) was determined and used to normalize the colony sizes on each plate as follows: Normalized size = unnormalized size×343/plate median . Following normalization , the mean colony size ( ± standard deviation ) was calculated for each mutant ( represented by 10 colonies in the control screens and six colonies in each query screen ) and the data were filtered to remove outliers ( >2 standard deviations from each mean ) . After recalculation of the mean and standard deviation for each colony , the difference in the pixel size of each mutant on the array was computed between the control and query screens . The significance of this difference was assessed by performing a two-tailed t-test ( with unequal variance ) of the null hypothesis that the sizes of the colonies in the control and query screens are statistical the same . The fitness of the query strains relative to wild-type was determined by comparing the median his3Δ::kanMX colony sizes on the inner perimeter for all plates in each query screen versus the control screen ( i . e . 3108 colonies for each query and 5180 colonies for the control ) . The resulting relative fitness values ( Wscs3Δ = 0 . 95 , Wyft2Δ = 0 . 96 and Wscs3Δ yft2Δ = 0 . 89 ) were used to calculate genetic interaction scores ( ε ) as described below . The fitness of each array deletion strain as a single mutant or as a double or triple mutant ( combining deletions of SCS3 or YFT2 or both genes ) relative to wild-type was determined from the ratio of the mean colony size of the mutant to the median his3Δ::kanMX colony size from the 5180 control colonies . Genetic interaction scores for digenic pairs were calculated as the difference between the observed and expected fitness values of the double mutants where expected fitness was calculated as the product of the fitness of the corresponding single mutants ( i . e . a multiplicative model , [41] , [42] ) . Thus , ε = Wxy – ( Wx * Wy ) where Wxy is the observed fitness of the double mutant , Wx is the observed fitness of the array deletion strain and Wy is Wscs3Δ or Wyft2Δ ( see above ) . For trigenic interactions , a similar multiplicative model was employed , ε = Wxyz – ( Wx * Wyz ) , where Wxyz is the observed fitness of the triple mutant , Wx is the fitness of the array deletion strain and Wyz is Wscs3Δ yft2Δ ( see above ) . The general applicability of this solution in the current study is satisfied by the modest 11% fitness defect ( Wscs3Δ yft2Δ = 0 . 89 ) of the scs3Δ yft2Δ query strain relative to wild type . Unless otherwise indicated , subsequent analyses employed stringent criteria to select interactions with a high probability of representing true positives . These criteria included ( i ) ≥40 pixel size difference between single mutant and double or triple mutant colonies; ( ii ) a p value ≤0 . 01 representing the probability that the colony sizes of the strains being compared are significantly different from one another and ( iii ) ε scores ≤−0 . 12 or ≥0 . 2 . Similar criteria for high stringency cutoffs were applied in the large scale study of Costanzo et al [42] . Selection of cutoffs for ε were also based on pairwise comparisons of 221 genetic interactions identified with two or more query strains ( Figure 3b–3c , Figure S4 ) . After removal of genes annotated as dubious ORFs in SGD , this analysis yielded a set of genetic interactions with 636 unique genes ( Table S1 ) . In addition to the metrics used for validating our screens we also compared our data to published SGA studies where genetic interactions have been reported for SCS3 . In an E-MAP of the early secretory pathway [83] , 29 aggravating genetic interactions with SCS3 were reported that had an S score <−1 . 5 . We scored 22 of these and found 14 ( 64% ) that met our stringent criteria ( pixel size difference , p value and ε ) . In addition , many genetic interactions with SCS3 were identified by Constanzo et al . , [42] . We downloaded these data from DRYGIN using the default values for SGA score and p value ( IεI>0 . 08 and p<0 . 05 ) . From this group we determined that 140 unique interactions were scored in our dataset . Forty-eight of these met our stringent criteria and a total of 64 interactions ( 46% ) were found when interactions were compared at the same p value limit ( p<0 . 05 ) . Biological process annotations for genes on the viable gene-deletion array were obtained from supplementary data file S6 of Costanzo et al [42] . We calculated the frequencies of GO terms in this list and among the 636 genes that interacted with SCS3 and/or YFT2 and then calculated enrichment by hypergeometric distribution . Growth at 30°C with shaking was measured at using a Bioscreen C Microbiology Reader ( Growth Curves USA ) , which recorded OD600 readings from 100-well plates every 15 minutes . A single colony was inoculated at a starting OD600 of 0 . 1 into SD/MSG media that contained 0 . 1% Nonidet-P40 . Doubling time and growth curve data were derived from three independent colonies per strain or condition as per established protocols [98] , [99] . HEK293 cells were imaged as described in [14] SI Materials and Methods: “Microscopy” section . For yeast imaging an upright Olympus BX61 microscope with a sensicam QE cooled CCD camera ( Black/White ) and IP Lab 4 . 0 . 8 software was used at the Analytical Imaging Facility at the Albert Einstein College of Medicine . Cells were viewed with a 100X NA = 1 . 4 oil objective and FITC filter . Sections ( 0 . 2 µm ) were collected for each image and combined into a single projection using Image J ( http://imagej . nih . gov/ij/ ) . Cells , either early log phase ( between OD600 nm 0 . 2 and 0 . 8 ) , freshly saturated overnight or 5 day stationary phase cultures , were stained with 0 . 5 µg/µl BODIPY 493/503 ( Invitrogen ) , for 5 to 10 min . prior to fluorescence microscopy . Cells were prepared as indicated from either SD/MSG , YPD , YPO or SC-Ino media and either imaged immediately or fixed in paraformaldehyde and permeablized in 0 . 1% Triton as described in [85] prior to staining and imaging . Methods for RNA preparation and Northern analysis have been reported elsewhere [100] , [101] . Yeast were grown to early log phase before harvesting or treatment and RNA extracted using the hot phenol method . End-labeled oligonucleotides specific for detection of Ino1 , Hac1 and Rpl28 mRNAs and U3 snRNA were hybridized , detected by phosphorimage and analysed using ImageQuant software as described previously [100] . Metabolic labeling of lipids in log phase wild type and SCS3 and YFT2 deletion strains was achieved by growing strains for 2 hours in 1 µCi/ml of [1-14C]acetate ( 57 mCi/mmol ) or for 6–8 generations in 10 µCi/ml 32P-orthophosphate . Lipids were extracted by the two-step 4°C method [51] . Sequential extracts in chloroform/methanol ( 17∶1 , v/v ) for 120 min and chloroform/methanol ( 2∶1 , v/v ) for 120 min were pooled , evaporated under N2 and dissolved in chloroform/methanol ( 1∶2 , v/v ) for thin layer chromatography . Labeled neutral and phospholipids were separated by one-dimensional TLC in hexanes∶ethyl ether∶acetic acid ( 80∶20∶1 ) [102] or chloroform∶ethanol∶triethanolamine∶H20 ( 30∶35∶35∶7 ) [103] , respectively . Plates were detected by phosphorimage and analysed with ImageQuant software . Extracts from 50 OD600 of log phase wild-type and SCS3 and YFT2 gene-deletion cells were prepared in triplicate and analyzed by mass spectrometry . Total lipid species are reported as nmol/108 cells . Mass spectrometry was performed at the Kansas Lipidomics Research Center Analytical Laboratory . | The ability to form lipid droplets is a conserved property of eukaryotic cells that allows the storage of excess metabolic energy in a form that can be readily accessed . In adipose tissue , the storage of excess calories in lipid droplets normally protects other tissues from lipotoxicity and insulin resistance , but this protection is lost with chronic over-nutrition . The FAT storage-inducing transmembrane ( FIT ) proteins were recently identified as a conserved family of proteins that reside in the lipid bilayer of the endoplasmic reticulum and are implicated in lipid droplet formation . In this work we show that specific functions of the FIT proteins are conserved between yeast and humans and that SCS3 and YFT2 , the yeast homologs of mammalian FIT2 , are part of a large genetic interaction network connecting lipid metabolism , vesicle trafficking , transcription , and protein synthesis . From these interactions we determined that yeast strains lacking SCS3 and YFT2 are defective in their response to chronic ER stress and cannot induce the unfolded protein response pathway or transcription of phospholipid biosynthetic genes in low inositol . Our findings suggest that the mammalian FIT genes may play an important role in ER stress pathways , which are linked to obesity and type 2 diabetes . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2012 | SCS3 and YFT2 Link Transcription of Phospholipid Biosynthetic Genes to ER Stress and the UPR |
Under nitrogen deprivation , the one-dimensional cyanobacterial organism Anabaena sp . PCC 7120 develops patterns of single , nitrogen-fixing cells separated by nearly regular intervals of photosynthetic vegetative cells . We study a minimal , stochastic model of developmental patterns in Anabaena that includes a nondiffusing activator , two diffusing inhibitor morphogens , demographic fluctuations in the number of morphogen molecules , and filament growth . By tracking developing filaments , we provide experimental evidence for different spatiotemporal roles of the two inhibitors during pattern maintenance and for small molecular copy numbers , justifying a stochastic approach . In the deterministic limit , the model yields Turing patterns within a region of parameter space that shrinks markedly as the inhibitor diffusivities become equal . Transient , noise-driven , stochastic Turing patterns are produced outside this region , which can then be fixed by downstream genetic commitment pathways , dramatically enhancing the robustness of pattern formation , also in the biologically relevant situation in which the inhibitors' diffusivities may be comparable .
The emergence of multicellularity , together with cell differentiation and the ensuing division of labor , conferred unique advantages to the survival of organisms and paved the way for the evolution of patterned complex forms such as those extant today . Among the remarkable diversity of organismal shapes , nearly periodic structures such as digits in a limb [1] , sensory bristles in Drosophila [2] , palatal ridges [3] , and stripes in zebrafish [4] represent a fundamental and ubiquitous motif , suggesting that common mechanisms may be at play behind these structures’ morphogenesis . A striking example of nearly periodic developmental patterns is displayed by cyanobacterial Anabaena sp . PCC 7120 filaments ( henceforth Anabaena ) [5 , 6] . In nitrogen-rich environments , Anabaena exhibits undifferentiated filaments of vegetative cells that carry out both oxygenic photosynthesis and assimilation of combined nitrogen sources such as ammonium or nitrates . However , when combined nitrogen sources become scarce , Anabaena can fix atmospheric nitrogen using nitrogenase , an enzyme whose function is abolished by minute amounts of oxygen . Thus , photosynthesis and nitrogen fixation are incompatible processes within the same cell , an incompatibility that the organism solves by the differentiation of some of its cells into heterocysts , cells that specialize in nitrogen fixation but carry out no oxygenic photosynthesis . Heterocysts contain an extra cell envelope relative to their vegetative counterparts . This cell envelope is comprised of two different layers , one made of glycolipids and the other of polysaccharide . The glycolipid layer appears to have a reduced permeability to gases , allowing heterocysts to maintain a micro-oxic environment [7] . A developmental pattern of individual heterocysts separated by nearly regular intervals of about 10–15 vegetative cells forms , with heterocysts supplying surrounding vegetative cells with fixed nitrogen products while receiving carbohydrate products from their neighbors in return . This characteristic lengthscale is independent of filament length . Since heterocysts lose the ability to divide , well-developed filaments grow by the growth and division of vegetative cells . When a vegetative cell interval becomes long enough , a new intercalary heterocyst forms in its midst , thereby maintaining the characteristic lengthscale of the developmental pattern . This organization represents one of the earliest experiments of differentiated multicellularity on Earth and can be traced back to more than 2 billion years ago [7] . The developmental cascade giving rise to de novo pattern formation from undifferentiated filaments is triggered upon nitrogen step-down by the concerted action of the NtcA and HetR protein regulators [8] . NtcA is activated by binding of 2-oxoglutarate , which accumulates in cyanobacteria under nitrogen deprivation [7] . HetR regulates itself through a positive feedback loop that not only amplifies its mean levels [9 , 10] but also enhances variations between cells or noise [11] . Levels of HetR grow in clusters of contiguous cells , but only one cell eventually commits fully to differentiation into a heterocyst , while the others revert into a regular , vegetative state . Commitment into a heterocyst state , which is irreversible , is mediated by the HetP protein [12] . Resolution of clusters is achieved by lateral inhibition effected by PatS , whose production is induced by HetR early after nitrogen step-down . The gene patS encodes a short peptide whose C-terminal domain is post-transcriptionally processed to yield the hepta-peptide PatS-7 with the sequence RGSGR , which is believed to diffuse to neighboring cells [13 , 14] . There , it interferes with the DNA-binding activity of HetR and causes its degradation , creating HetR gradients along filaments [15] . Immunity of HetR against PatS inhibition within the same cell has been proposed to be mediated by functions of the HetC and PatA proteins [10 , 13 , 16–18] . In contrast to PatS , production of the HetN protein takes place later during the differentiation process , and a HetN-derived signal produced predominantly at heterocysts [19–21] is also thought to diffuse between cells and inhibit HetR function there by mediating its post-translational decay . While HetN carries a hexapeptide ERGSGR in its sequence that is necessary for its inhibitory function [20 , 21] , neither the precise identity nor the mechanism of action of the actual HetN-derived signal is known . Overexpression of either PatS or HetN leads to complete suppression of heterocyst formation , whereas HetR overexpression can cause heterocysts to form , even under nitrogen-replete conditions [10] . The presence of activator and inhibitor species , cell–cell communication , an intrinsic lengthscale of patterns that is independent of filament length , and the de novo formation of patterns from a homogeneous state has suggested that a diffusion-driven Turing mechanism may be behind pattern formation in Anabaena [22] . In Turing’ s classic model of morphogenesis , two mutually interacting substances termed “morphogens” can diffuse within a continuum domain of fixed size . Nonhomogeneous patterns can arise from a homogeneous state , provided that one of the morphogens activates the production of the other , while the latter inhibits production of the former by feedback , and when the diffusivity of the inhibitor greatly exceeds that of the activator . In spite of various commonalities , there are substantial differences between the classic , two-component Turing model and pattern formation in Anabaena . The Turing instability requires diffusion of both activator and inhibitor species and a large difference in the two diffusivities . Lack of diffusion of one prevents pattern formation [23] , and a spatially homogeneous state remains stable . However , diffusion-driven Turing instabilities can arise in three-component models in which one of the species does not diffuse [24 , 25] . There is no evidence for diffusion of the activator HetR between cells along Anabaena filaments , and there are two HetR inhibitors instead of one: PatS and HetN . Moreover , the diffusion constants of PatS- and HetN-derived morphogens may be comparable . The equations for the Turing model are defined on a continuous spatial support of fixed size , whereas Anabaena filaments continually grow by cell growth and division . Anabaena patterns are intrinsically discrete , with a typical lengthscale of the order of 10 cells , far from any continuum approximation [26] . A schematic layout of the basic regulatory network leading to heterocyst differentiation is displayed in Fig 1 . Upon nitrogen step-down , patterns in Anabaena readily form , displaying a large degree of robustness and plasticity to variations in external conditions . For example , the fraction of heterocyst cells changes in response to illumination levels [27 , 28] and exogenous fixed-nitrogen levels [29 , 30] . This stands in stark contrast with the exquisite dependence of Turing patterns on initial conditions , their appearance only in a small region of parameter space [31] , and the large difference in the diffusivities of the activator and inhibitor morphogens that is required for the homogeneous state to be unstable . This so-called fine-tuning problem can be largely overcome and robustness enhanced if copy number fluctuations ( also called demographic noise ) , as stemming from , e . g . , gene expression noise , are significant , seeding the formation of stochastic Turing patterns in regions of parameter space where a homogeneous state is linearly stable [32–39] . Remarkably , these fluctuation-driven patterns can appear even when the diffusion constants of the activator and inhibitors are of similar magnitude and when only one species undergoes diffusion [40] . In general , the amplitude of fluctuation-driven patterns scales with the strength of the driving noise [41] . Giant amplification , however , can be produced by the interplay between noise and nonorthogonal eigenvectors of the linear stability matrix [42] . In this work , we present a model of developmental pattern formation in Anabaena that includes the HetR activator and its two inhibitors , PatS and HetN , as the three dynamical variables . We motivate this choice of variables by presenting evidence for the different spatiotemporal roles that PatS and HetN play during pattern maintenance . Furthermore , we incorporate demographic noise in a stochastic formulation of the model and explore the consequences of filament growth on pattern formation . We demonstrate that noise-driven , stochastic Turing patterns of sufficiently large amplitude ( proportional to the strength of finite size fluctuations ) to trigger commitment to differentiation can serve as a robust basis to describe developmental patterns in Anabaena .
The large variability in the choice of dynamical variables in previous theoretical studies highlights a lack of consensus as to what ingredients a minimal model should include to capture the essential features of development in Anabaena [43–48] . These studies have considered different combinations among HetR , NtcA , fixed nitrogen , HetN , and PatS . By and large , most models have been guided by the prevailing view that the functions of PatS and HetN inhibitors of HetR are well separated in time , with PatS acting during de novo pattern formation and HetN during pattern maintenance ( e . g . , [49] ) . A notable exception is the work by Zhu and colleagues [48] , who included both PatS and HetN during pattern maintenance . Here , we provide experimental evidence that sheds light on the different spatiotemporal roles that both PatS and HetN play during pattern maintenance , supporting the choice of HetR , PatS , and HetN as dynamical variables . To do so , we use the enhanced decline in autofluorescence due to photosynthetic activity in a proheterocyst compared to vegetative cells as a temporal reference point . This decline takes place following nitrogen deprivation due to the degradation of phycobilisome antennae , which are the main light-harvesting complexes in cyanobacteria [50] . An increase in fluorescence in an individual cell along a filament bearing a chromosomal hetN-gfp fusion is illustrated in the left snapshots in Fig 2A , together with corresponding snapshots of photosynthetic autofluorescence on the right . Significant production of HetN-GFP ( green fluorescent protein ) clearly takes place after the onset of decline in photosynthetic activity in the same cell . To quantify this delay , we plot in Fig 2B and 2C the fluorescence of HetN-GFP and autofluorescence , both normalized by the cell area , for a number of cells displaying behavior similar to that in Fig 2A . The traces of both autofluorescence and HetN-GFP fluorescence density of each cell have been shifted in time by the same amount so that the autofluorescence traces of all cells coincide at the midpoint of their decay . There are two salient features in these figures . First , all HetN-GFP traces collapse by this shift , demonstrating the high temporal precision of the developmental program; second , activation of HetN-GFP production is highly switchlike . Together , these features show that , under our experimental conditions , the onset of a significant decline in autofluorescence precedes HetN production by about 5 h . Note that there is a small decline in photosynthetic activity in all cells after nitrogen deprivation [51] . The increase in hetR expression in a pair of contiguous cells near the middle region of a vegetative interval between mature heterocysts , and the ensuing lateral inhibition that results in a unique incipient intercalary heterocyst is illustrated in the left panels in Fig 2D . The series of snapshots , taken at 90 min intervals , show the change in the expression of a chromosomally-encoded PhetR-gfp fusion in a cluster of vegetative cells . Shown in the panels to the right are a corresponding set of snapshots , illustrating the decline in photosynthetic autofluorescence in the cell that eventually exhibits higher fluorescence from PhetR-gfp expression and in which a new heterocyst will form . The snapshots show that the onset of expression from PhetR-gfp and the lateral inhibition of the neighbor cell clearly precede the onset of decline in photosynthetic activity . This is quantified in Fig 2E and 2F , where we show traces of fluorescence from PhetR-gfp and autofluorescence of cells that will become heterocysts , respectively , both normalized by cell area . The traces of both PhetR-gfp fluorescence and autofluorescence were shifted in time by the same amount as in the case of HetN-GFP ( Fig 2B and 2C ) . Since significant HetN production takes place well after the onset of reduction in autofluorescence , HetN cannot mediate the lateral inhibition that resolves clusters of cells with higher expression of PhetR-gfp into one new intercalary heterocyst . Thus , we posit that this lateral inhibition must be mediated by PatS , a role that is identical to the one it has during de novo pattern formation . Lastly , we demonstrate PatS production during pattern maintenance directly in the series of snapshots in Fig 2G taken at 2 . 5-h intervals . Shown is a filament expressing PpatS-gfp 33 h after nitrogen step-down . The filament displays two heterocysts separated by a vegetative interval . At later times , a new intercalary heterocyst forms within this interval , with clear production from the patS promoter . Quantification of events such as those in ( g ) is shown in Fig 2H–2I . Corresponding traces in both ( h ) and ( i ) have been displaced along the time axis by the same amount as in Fig 2B and 2C . Note that the onset of expression from PpatS-gfp takes place slightly before or at the onset in the decline in autofluorescence , consistently with the role we posited for PatS in mediating lateral inhibition during incipient intercalary heterocyst formation . Together , the above data support the notion that HetN and PatS are both necessary for pattern maintenance and lead us to choose HetR , PatS , and HetN as dynamical variables in our model . In order to justify a stochastic approach to model development in Anabaena and the use of master equations to describe the dynamics of the HetR , PatS , and HetN , it is important to show that copy numbers of regulators are typically small , and thus , number fluctuations lead to a high level of demographic noise . We present evidence that this is so in the case of production from the hetR promoter . We calibrated fluorescence measurements in terms of absolute copy numbers of GFP molecules nGFP from a PhetR-gfp fusion , exploiting the statistics of binary partitions of proteins between a cell and its daughters following cell division [52] . These measurements were carried out in filaments with a wild-type background ( see Methods ) . We obtained nGFP = 41 ± 17 for a typical cell under nitrogen-replete conditions and nGFP = 40 ± 14 for a vegetative cell between two heterocysts under nitrogen-poor conditions . Note that these numbers actually represent an upper bound on HetR numbers , since the lifetime of GFP is of many hours and GFP does not report on HetR degradation due to PatS- and HetN-derived signals . We consider a chain of Ω cells , with Ω fixed . Denoted by Ri , Si , and Ni are one individual of species HetR , PatS , and HetN , respectively . The index i runs from 1 to Ω and identifies the cell to which the individual belongs . The three species are produced at constant constitutive rates , here exemplified via the following chemical equations: ∅→αRRi∅→αSSi∅→αNNi ( 1 ) Furthermore , Ri regulates itself by positive feedback [9 , 53] ∅→βR ( riV ) 2K2+ ( riV ) 2Ri ( 2 ) where βR is the strength of the positive autoregulation of HetR and K2 is the dissociation constant of HetR dimers . We have assumed that the active form of HetR is dimeric [54] , even though a tetrameric form has been detected recently [55] . Here , ri denotes the total number of HetR molecules in cell i , and V stands for the volume of each cell . HetR activates production of PatS [54] , with strength βS ∅→βS ( riV ) 2K2+ ( riV ) 2Si ( 3 ) Note that there is no evidence in the literature for activation of HetN production by HetR . Additionally Ri , Si , and Ni undergo degradation at constant rates Ri→kR∅Si→kS∅Ni→kN∅ ( 4 ) PatS and HetN form complexes with HetR dimers , effecting post-transcriptional HetR degradation [15] Ri+Ri+Si→μS∅Ri+Ri+Ni→μN∅ ( 5 ) These terms are analogous in form to the stoichiometric down-regulation of mRNA targets by small RNAs in bacteria [56 , 57] . Molecules Si and Ni can diffuse along the chain , yielding Si→DSSjNi→DNNj ( 6 ) Here , j points to the cells adjacent to cell i . To proceed in the analysis , we introduce the discrete quantities ri , si , and ni to identify the total number of HetR , PatS , and HetN in each cell i at time t . The state of the system is therefore completely specified by the vector ( r , s , n ) of dimension 3Ω , where r = ( r1…rΩ ) , s = ( s1…sΩ ) and n = ( n1…nΩ ) . Introduce P ( r , s , n , t ) to label the probability for the system to be in state ( r , s , n ) at time t . Transitions from one state to another are dictated by the chemical equations listed above . Following standard notation , we assign T ( r′ , s′ , n′|r , s , n ) to characterize the transition rate from state ( r , s , n ) to state ( r′ , s′ , n′ ) , which is compatible with the former . A complete account of the transition rates in the model is provided ( S1 Text ) . Under the Markov approximation , the dynamics of the system is governed by a master equation which can be cast in the generic form ddtP ( r , s , n , t ) =∑r′ , s′ , n′≠r , s , n[T ( r , s , n|r′ , s′ , n′ ) P ( r′ , s′ , n′ , t ) −T ( r′ , s′ , n′|r , s , n ) P ( r , s , n , t ) ] ( 7 ) The master equation provides an exact description of the stochastic dynamics . In the limit where the volume of each cell V goes to infinity , the system becomes deterministic: the coupled dynamics of the continuous concentrations is described by a set of ordinary differential equations . The effect of fluctuations , stemming from the discreteness of the system and here exemplified by a finite carrying capacity V , can be in turn accessed by numerical simulations of the chemical reaction model via the Gillespie algorithm [58] . This method produces realizations of the stochastic dynamics that are formally equivalent to those found from the master equation . Notice that volume V solely enters the definition of the reaction rates associated to Eqs ( 2 ) and ( 3 ) . The other rates are independent of V . The finite size parameter V is , however , present in the rate equations that follow the aforementioned chemical reactions , as we will make explicit ( S1 Text ) . Analytical progress is also possible by invoking the so-called van Kampen system-size expansion . This amounts to effectively expanding the master equation in powers of V−1/2: to leading order ( V → ∞ ) , one obtains the deterministic equations , while next-to-leading contributions give finite V corrections . These latter take the form of linear stochastic differential equations that can be straightforwardly analyzed , especially in the case when the deterministic system has approached a stable fixed point . Notice that V stands for the volume of individual cells , not the actual copy number of the regulator molecules . In the literature , the van Kampen expansion is often implemented by assuming the number N of interacting entities as the relevant control parameter . This is particularly convenient when the inspected population stays constant over time . Conversely , when N gets modulated in time by the imposed dynamics , it is customary to adopt V as the reference extensive quantity , which encodes for the characteristic size of the system [59–61] . V and N are mutually related through the number density , a ( dimensional ) constant that is not made experimentally available for Anabaena . It is therefore not possible , at least at present , to establish a quantitative bridge between N and V . This latter quantity has to be regarded as a free control parameter , which can be tuned at will so as to control the strength of the imposed demographic noise . The van Kampen expansion is based on substituting the ansatz riV=ϕi+ξ1 , iVsiV=ψi+ξ2 , iVniV=ηi+ξ3 , iV ( 8 ) into the master Eq ( 7 ) . Here , ϕi , ψi , and ηi stand for the deterministic concentrations , associated with cell i , while ξ1 , i , ξ2 , i , ξ3 , i are the corresponding stochastic terms , triggered by finite size corrections . In the next section , we will focus on the deterministic mean field limit and investigate the conditions that underlie the process of pattern formation à la Turing . We will then turn to analyze the linear stochastic differential equations , obtained at the next-to-leading order of the van Kampen expansion , to report on the emergence of stochastic self-organized patterns . The derivation is lengthy ( S1 Text ) .
To find out the conditions that underlie a deterministic Turing instability , we carry out a linear stability analysis around the homogeneous fixed point ( ϕ* , ψ* , η* ) . We hence introduce small , nonhomogeneous perturbations ( δϕi , δψi , δηi ) and linearize around the fixed point , following [62] ( see S1 Text ) . To solve the obtained linear system requires expanding the imposed perturbation on the complete basis formed by the eigenvectors of the Laplacian operator Δ . The procedure yields the following matrix: Q= ( F−μS ( R* ) 2−μN ( ϕ* ) 2G−kS−μS ( ϕ* ) 20−2μNϕ*η*0−kN−μN ( ϕ* ) 2 ) + ( 0000DS000DN ) Λ ( α ) ( 10 ) where Λ ( α ) , α = 1 , …Ω stand for the real eigenvalues of the negative semidefinite matrix Δ . Here , F=−kR+βR2ϕ*K2 ( K2+ ( ϕ* ) 2 ) 2−2μSϕ*ψ*−2μNϕ*η* and G=βS2ϕ*K2 ( K2+ ( ϕ* ) 2 ) 2−2μSϕ*ψ* . The eigenvalue of Q with the largest real part , λmax ( Λ ( α ) ) , defines the dispersion relation and ultimately determines the response of the system to the imposed external perturbation . If the dispersion relation is positive over a finite domain in Λ ( α ) , the perturbation gets exponentially enhanced and eventually materializes in asymptotic patterns , whose spatial characteristics are set by the excited discrete wavelengths Λ ( α ) . When λmax < 0 , the perturbation fades away and the system converges to the unperturbed homogeneous solution . At variance with the conventional Turing analysis , the patterns are here established on a discrete and finite support . The dispersion relation that applies to the limiting continuum setting can be readily recovered by replacing Λ ( α ) with −k2 , k being the usual spatial Fourier frequency . The discrete dispersion relation λmax ( Λ ( α ) ) results in a collection of Ω points distributed on the smooth profile that is obtained under the idealized continuum representation . In Fig 3A , the dispersion relations are plotted for two distinct values of βS . Symbols refer to the discrete dispersion relation , while the solid lines stand for their corresponding continuum analogues . To access analytically the conditions for the onset of the instability , one can operate under the continuum approximation and adapt the prototypical Turing calculation to the case of interest where three species are made to mutually interact ( see S1 Text ) . We now freeze all parameters to nominal values except for βS and βR . The colored region of Fig 3B denotes the portion of the plane ( βS , βR ) where the instability can take place . In this region , the maximum of the dispersion relation is positive . We stress that with the exception of experimental measurements of the in vitro affinity of the RGSGR peptide to HetR [63] , there are no published experimental data to guide the choice of parameter values in our model . The smaller the ratio DS/DN is , the narrower is the instability region ( see S2 Fig when the ratio is equal to one ) . To test the validity of the theoretical predictions , we have numerically integrated Eq ( 9 ) . Initializing the concentration of the species at the stable homogeneous configuration after the injection of small perturbations , the system self-organizes and displays Turing patterns ( Fig 4A–4C ) . The patterns exhibit distinctive characteristic features that reflect the specific interactions at play between the different microscopic actors . A region with a high density of HetR induces an analogous crest in the concentration of PatS . This represents the nonlinear , sigmoidal activation of PatS by HetR . Conversely , high concentrations in HetR induces a depletion of HetN content , due to the nonlinear decay term in the equation for HetN ( third equation in Eq ( 9 ) ) . The typical separation between adjacent peaks can range from a few to tens of cells depending on the selected parameters and includes as a possible setting the experimentally observed scenario . A notable feature of the deterministic patterns we obtain is that the modulation in concentration of the different regulators is smooth and varies over a number of cells , whereas in developmental patterns in Anabaena , variation is more abrupt and localized on single cells ( see , e . g . , Fig 2 ) . Furthermore , the model does not reflect the temporal differences in the onset of production of PatS and HetN . These behaviors stem from the fact that we have not enforced any distinction between vegetative and heterocyst cells . Commitment and the ensuing differentiation into a heterocyst state must be governed by downstream genetic factors not included in our model , such as HetP and other proteins that share a functional domain with it , as discussed recently [12] . We posit that these genetic factors could also effectively stabilize transient noisy patterns outside the region in parameter space where the deterministic Turing instability takes place . In the following , we will show that stochastic patterns can indeed develop when the dispersion relation would predict the homogeneous fixed point to be stable . Endogenous demographic noise seeds a spatial modulation in the concentration of regulators , with nominal density peaks and associated characteristic spacing that quantitatively resemble those obtained when operating inside the region of deterministic order . The intrinsic ability of the stochastic system to self-organize beyond the boundary of the classical Turing instability , followed by fixation of transient patterns as a result of a commitment process that is triggered by sufficiently large concentration gradients in the biological system , can result in an extraordinarily robust mechanism for pattern formation in Anabaena .
The role played by demographic noise can be appreciated by performing numerical simulations at finite V using the Gillespie's algorithm [58] . Fig 4D–4F displays stochastic patterns , obtained for the same choice of parameters as in the deterministic setting ( V → ∞ ) ( see Fig 4A–4C ) . The corresponding dispersion relation has a positive real part ( upper curve in Fig 3A ) , and the recorded patterns display many similarities with their deterministic analogues . We now turn to stochastic simulations for a choice of parameters for which the deterministic Turing instability cannot develop ( lower curve in Fig 3A ) . The resulting patterns ( Fig 4G–4I ) are less defined but still visible to the eye , even when the system is initialized outside the region of deterministic order . The stochastic forcing produces sustained oscillations in space , which give rise to spatial patterns with distinct characteristic features , reminiscent of those obtained inside the region of deterministic order . To quantitatively substantiate this claim , we consider the next-to-leading approximation in the van Kampen expansion to obtain a closed analytical characterization of the stochastic fluctuations . Collecting terms in the expansion that scale proportionally to 1/V , one finds a Fokker–Planck equation for the distribution of fluctuations ( see S1 Text for details about the derivation ) . The Fokker–Plank equation is equivalent to the following system of coupled linear Langevin equations: dξq , idτ=∑l=13∑j=iΩMql , ijξl , j+χq , i ( 11 ) where ξq , i denotes the fluctuations that affect the concentration of species q on site i . Here , q = 1 ( HetR ) , 2 ( PatS ) , 3 ( HetN ) , and χq , i is a Gaussian white noise , with zero mean and correlator ⟨χq , i ( τ ) χl , j ( τ' ) ⟩ = Bql , ijδ ( τ−τ' ) . The 3Ω×3Ω matrices M and B are given in S1 Text . They depend on the solution of the deterministic equation and so are in principle time dependent . However , we are here interested in fluctuations about the stationary state: The mean field concentrations can be set to their constant equilibrium values ( ϕ* , ψ* , η* ) , and consequently , the matrices M and B lose their time dependence . They also have a nontrivial spatial dependence through the presence of the discrete Laplacian operator . To proceed in the calculation , we introduce an appropriate transform inspired by standard Fourier analysis but specifically designed to account for the discrete nature of the spatial support , including the enforced boundary conditions [64] . Label with v ( α ) the eigenvector of the discrete Laplacian operator Δ , relative to the eigenvalue Λ ( α ) . The temporal and spatially discrete transform f˜α ( ω ) of a generic function fi ( t ) is defined as f˜α ( ω ) =∫0+∞dτ∑j=1Ωfj ( τ ) vj ( α ) eiωτ . When applied to the Eq ( 11 ) , the transform disentangles the spatial coupling and yields the closed solution ξ˜q , α=∑l=13Fql−1χ˜l , α ( 12 ) where F is ( −iωI−M ( NS ) −M ( SP ) Λ ( α ) ) and M ( NS ) ( M ( SP ) , respectively ) stand for the nonspatial ( spatial , respectively ) component of M , as defined in S1 Text . We can then compute the power spectrum of fluctuations as Pq ( ω , Λ ( α ) ) =〈|ξ˜q , α ( ω ) |2〉=∑l , m=13Fql−1 ( Bml ( NS ) +Bml ( SP ) Λ ( α ) ) ( F† ) lq−1 ( 13 ) where ( F† ) −1 denotes the inverse of the adjoint of F . The non-spatial ( B ( NS ) ) and spatial ( B ( SP ) ) contributions to matrix B appear explicitly ( see S1 Text ) . In Fig 5A , we plot P1 ( 0 , Λ ( α ) ) as a function of the discrete wavelength Λ ( α ) , for the same parameter values as in the simulations of Fig 4G–4I . A clear peak is displayed , implying that noise promotes the spontaneous selection of a leading wavelength in the emerging patterns . More importantly , the latter coincides with the wavelength that becomes unstable when the system is taken inside the corresponding region of the deterministic instability . In this latter case , the pattern characteristics are shaped by the wavelength that maximizes the unstable dispersion relation ( upper curve in Fig 3A ) . In Fig 5B , we plot P1 ( 0 , ω ) versus the time frequency ω . To test the correctness of the theory , we carried out stochastic simulations using the Gillespie algorithm . The numerical power spectrum is reconstructed from an individual realization by applying the generalized transform introduced above . In Fig 5A and 5B red stars refer to the power spectra obtained from just one realization of the stochastic dynamics . The peaks are located as predicted by the theory , implying that the stochastic-driven patterns formation mechanism works efficiently for single Anabaena filaments . By averaging over many independent realizations , one recovers a perfect match with theoretical curves . In other words , for our model of Anabaena , the number of excited modes is sufficiently small that the analysis of the power spectrum proves successful in predicting the asymptotic outcome , as observed in the interesting paper by Maini and colleagues [65] . Summing up , stochastic corrections stemming from the finite size can eventually produce macroscopically ordered structures that quantitatively resemble those obtained under the deterministic Turing scenario . The differentiation into a heterocyst cell is then effected by downstream genetic factors , which get locally activated in response to large gradients of concentrations that result from the noisy component of the dynamics . According to the noise-driven mechanism proposed here , patterns can also arise when the diffusion constants are comparable DS ∼ DN , as demonstrated in S3 Fig . Indeed , the region of noise-driven instability is considerably larger than the corresponding deterministic region , as clearly depicted in Fig 6 .
The stochastic model that we have introduced can be modified to account for the growth of filaments , due to cell duplication [66 , 67] . The number of cells , Ω , is hence a stochastic variable , and the master equation should be modified to reflect this additional ingredient . More specifically , label with P ( r , s , n , Ω , t ) the probability of seeing the system in a configuration specified by the state vectors ( r , s , n ) , with Ω cells , at time t . Consistent with the above , we do not enforce on the model the differentiation from vegetative to heterocysts cells . We are in fact solely interested in the spontaneous emergence of patterned motifs in the regulators’ concentration that might anticipate the subsequent , genetic-driven commitment . For this reason , all cells composing the filaments can experience the duplication event . To implement the growth mechanism , the state vector r ( s and n , respectively ) is assumed to be of arbitrary size: the first Ω entries are different from zero and reflect the actual concentration in HetR ( PatS and HetN , respectively ) . All components ri ( si and ni , respectively ) with i > Ω are identically equal to zero and get progressively populated as the filament grows . Under these assumptions , the master equation that rules the dynamics of the system on a growing support is obtained by adding to the right hand side of Eq ( 7 ) the terms ∑r′ , s′ , n′≠r , s , n[Tdupl ( Ω|Ω−1 ) P ( r′ , s′ , n′ , Ω , t ) −Tdupl ( Ω+1|Ω ) P ( r , s , n , Ω , t ) ] ( 14 ) constrained to act on the first Ω cells of the lattice . Here , Tdupl ( ⋅|⋅ ) stands for the duplication rate that we set to a constant , ρ . For the sake of simplicity , in the expression for Tdupl , we do keep explicit track of the number of regulators . In our analysis , we assume that the amounts of HetN , PatS , and HetR get equally shared between daughter cells . The same qualitative conclusion as reported above holds , however , if binomial splitting of the genetic material is instead considered . From the master equation , one can readily obtain the 3Ω ordinary differential equations that govern the evolution of the concentration in the deterministic limit ( see S1 Text ) where Ω = Ω0eρVτ , Ω0 labeling the number of cells that initially compose the Anabaena filament . In the limit of a continuous spatial support , the growth yields an additional linear decay term , which scales with ρ , and time-modulated diffusion constants ( see S1 Text ) . Deterministic and stochastic simulations are reported in Fig 7A–7C and 6D–6F . Patterns are established and subsequently maintained with unaltered spacing , by successive insertion of high-density regions , where heterocysts would presumably localize . When the separation between heterocysts cells becomes large enough , the system self-organizes so as to enhance the concentration of HetR near the middle of the interval . This could anticipate the insertion of a new intercalary heterocyst to preserve the characteristic spacing , as seen in the experiments . The number of linearly unstable spatial modes increases with the filament size ( see S4 Fig ) , a mechanism that possibly facilitates the maintenance of the patterns . In the simulations reported in Fig 7 , we assumed that deterministic patterns could develop on the initial filament of size Ω0 . The conclusions remain unchanged , however , if patterns initially assumed fixed domain are instead stochastic in nature .
We have studied a theoretical model that describes the nearly periodic patterns observed when Anabaena filaments are subject to nitrogen deprivation . Our experiments , as well as those of others , informed our choice of dynamical variables and constrained the mathematical form their mutual interaction takes . Our goal has been to explore the consequences of our choice in an attempt to discover conditions and mechanisms for pattern formation and maintenance , rather than to fit specific model parameters and match experimental measurables such as heterocyst spacing distributions . To keep the model analytically tractable , only three dynamical variables have been considered . Thus , we have refrained from including effects such as the immunity of HetR against inhibition by PatS produced in the same cell as mediated by the HetC and PatA proteins [17 , 18 , 68]; biased inheritance of factors such as PatN that may influence a cell’s decision to differentiate [69]; enforcing a distinction between vegetative and heterocyst cells [44]; and factors such as HetP that modulate commitment to differentiation into a heterocyst state [12] . In an endeavor to choose proper dynamical variables that capture essential features of development in Anabaena , we have revisited the question of the need of two inhibitors of the master regulator of differentiation HetR . Our experiments challenge a clear-cut separation of the involvement of PatS and HetN in de novo pattern formation and pattern maintenance ( often found in the literature ) , respectively , and suggest instead that both PatS and HetN are present during pattern maintenance but have different spatiotemporal roles , as also proposed recently [48] . These experiments clearly establish that significant HetN production takes place only after a cell has committed to a heterocyst fate , recapitulating previous results [21] . HetN production sets up an inhibitory signal gradient that decays with distance from heterocysts and that prevents the formation of new intercalary heterocysts close to existing ones . Remarkably , activation of HetN production takes place with high temporal precision after the onset of the decline in photosynthetic autofluorescence and displays switch-like characteristics . Of note , the involvement of HetP and its homolog regulators during commitment has also been shown to result in a switch-like output [12] . In contrast , lateral inhibition processes observed in clusters of cells near the middle of vegetative intervals ( where levels of both fixed nitrogen products and the HetN-derived inhibitory signal are low ) take place before the observed decline in autofluorescence . We posit that only PatS can mediate this lateral inhibition process , as HetN is only produced at significant levels after the reduction in autofluorescence . This difference in timing of activation has been noted earlier [70] . Thus , PatS functions similarly both during de novo pattern formation , when primordial fluctuations in gene expression that exist under nitrogen-replete conditions are amplified to create an initial pattern , as well as during pattern maintenance , in the initial stages of incipient intercalary heterocyst formation [71] . It is possible that the transient activation of PatS required to resolve small clusters of cells exhibiting hetR activation into a single heterocyst may have prevented its detection during pattern maintenance in earlier works [72] , and to claims of a diminished role for PatS during the pattern maintenance stage [15 , 73] . The different spatiotemporal roles of PatS and HetN in the picture proposed here are reminiscent of the ideas embodied in the models of Turing and Wolpert [22 , 74] and illustrate that these ideas are not mutually exclusive and can work together [75] . Together , the above considerations led us to choose HetR , PatS , and HetN as dynamical variables , in accordance with a previous numerical study of a reaction–diffusion model in which roles of PatS and HetN similar to the ones envisioned here were proposed [48] . Being minimal , any perturbation to the model , such as removing one of the inhibitors , the positive autoregulation of HetR , or cell–cell communication , will destroy any Turing instability . This precludes a comparison with deletion mutants in Anabaena [11] . In contrast to previous models of Anabaena development , we have taken demographic noise into account explicitly by starting from master equations that embody production and degradation of individual molecules . This approach is fully justified by our measurements of a small copy number of GFP molecules produced from the hetR promoter , together with the significant levels of cell–cell heterogeneity ( 35% ) measured previously for the same promoter , under nitrogen-rich conditions [11] . A linear stability analysis of the steady-state solutions of the set of reaction–diffusion equations obtained in the deterministic limit of a linear noise approximation to the master equations leads to a region of instability in which deterministic Turing patterns form , provided DS > DN , in accordance with a previous numerical study [48] . However , this region shrinks severely as DS → DN , a limit that may be relevant to the biology of Anabaena . Much is known about the PatS inhibitory signal . The pentapeptide RGSGR in the C-terminal 5 amino acids of PatS prevents the DNA-binding activity of HetR in vitro [54]; its addition to the medium prevents differentiation of heterocysts [72] and therefore has been thought to constitute the PatS-derived signal . More recently , the accumulation of an RGSGR-containing product in cells adjacent to proheterocysts was detected by immunofluorescence , with a gradient extending over 5–6 cells [13 , 14] . The findings also indicated that an octapeptide containing the RGSGR motif was active as an inhibitor . In contrast to the above , less is known about the molecular identity of the HetN inhibitory signal . HetN bears an internal ERGSGR sequence that is identical to the C-terminal sequence of PatS , and deletion of this sequence results in the appearance of multiple contiguous heterocysts in the second round of heterocyst formation [20 , 21] . The existing evidence argues in favor of the HetN-derived signal being a peptide resulting from processing of the full protein that consists of little more than the RGSGR motif and which is transferred between cells via SepJ and/or FraC/FraD septal proteins [20 , 21] . Taken together , the above considerations and parsimony lead us to the notion that the diffusivities of PatS- and HetN-derived signals are comparable and thus that the limit DS ≈ DN likely reflects the biology of Anabaena . A direct consequence of this notion is that within the framework of the deterministic limit of our model , Turing patterns are unlikely to arise . Remarkably , and in line with previous theoretical investigations of model systems [32–34] , the next-to-leading order in the linear noise approximation clearly demonstrates the formation of stochastic Turing patterns outside the region in parameter space where deterministic patterns form . Gillespie simulations show that these patterns are clearly visible , in spite of their inherently transient character . Furthermore , they do arise when DS is close to DN , suggesting a novel robust scenario for the formation and maintenance of developmental patterns in Anabaena . These may be the result of transient stochastic patterns that arise from the resonant amplification of demographic noise outside the region of deterministic instability . When fluctuations in HetR concentration in particular cells reach sufficiently high levels , irreversible commitment to the formation of heterocysts may be triggered by downstream genetic processes , thereby stabilizing the transient stochastic patterns . In support of this notion , it has been observed experimentally that overexpression of HetR suffices to trigger differentiation and the formation of heterocysts , even under repressing , nitrogen-replete conditions [10] . A division of development into patterning followed by commitment stages has also been considered recently [12] . Furthermore , our measurements of low copy numbers of GFP molecules produced by the hetR promoter and the significant levels of expression noise measured under nitrogen-replete conditions [11] confirm that demographic stochasticity is significant in this system and justifies our approach . Overall , this scenario is considerably more robust than the classical Turing mechanism , as stochastic patterns form in a much larger region of parameter space than their deterministic counterparts and do not require large differences in diffusivities . Including time delay prior the initiation of HetN production constitutes a possible avenue of further investigation . Time delay is in fact known to impact the process of pattern formation . In [65] , typical Turing models are reported to lose robustness with the inclusion of delays . On other occasions , however , time delay can facilitate the onset of the instability [76 , 77] . Gauging the impact of the delay for the problem at hand is left for future analysis . The robust scenario we propose is not specific to Anabaena . We can speculate that such a mechanism can be active in situations in which a pattern arises from the amplification of gene expression fluctuations and not when a morphogen gradient is imposed , e . g . , in a Drosophila embryo . We stress that a restricted model that includes only nondiffusing HetR and diffusing PatS cannot yield nonhomogeneous patterns , even when demographic noise as well as domain growth are included . However , small , nonzero levels of hetN expression under nitrogen-replete conditions have been reported recently [21 , 78] , suggesting that the three-variable model that includes R , S , and N may be applicable to de novo pattern formation as well . In this context , we note that in addition to patS and hetN genes , two other genes have been found to encode the RGSGR motif [73] . An interesting feature emerging from the analysis of the effects of filament growth in the deterministic limit of our model is that the number of linearly unstable spatial modes increases with filament length ( see S1 Text ) . This finding is in line with the analysis of model systems [67] . Thus , growth promotes the instability of a homogeneous state and the formation of spatial patterns [79] . However , a linear stability analysis proved hard to carry out because differentiation and growth take place with similar temporal timescales , and incorporating growth as a function of time remains a challenging task for the future . To sum up , our work highlights the essential role demographic noise plays in both the robust formation and maintenance of developmental patterns in Anabaena . Far from being a passive byproduct of molecular processes , fluctuations in copy numbers are used actively by this organism of primitive origin in order to seed and maintain developmental patterns and thus solve the incompatibility between nitrogen fixation and photosynthesis . Our model constitutes the first example of the applicability of stochastic Turing patterning in the context of morphogenesis and , together with examples from ecology and epidemics [80] , underscores the generality of this robust mechanism in biological pattern formation .
The strains used in this study were obtained from conjugation with the wild-type Anabaena sp . ( also known as Nostoc sp . ) strain PCC 7120 as recipient; strain CSL64 bearing a chromosomally encoded PhetR-gfp transcriptional fusion; and strain CSL108 bearing a translational hetN-gfp-mut2 fusion as the only HetN version in a wild-type background . Both strains have been reported previously [11 , 21] . Anabaena producing GFP from a PpatS-gfp transcriptional fusion in a strain PCC 7120 , α megaplasmid has been constructed and reported previously ( CSVM17 ) [13 , 81] . We note that all fusions preserve the native ribosome binding site of each gene , and therefore , they faithfully report on the physiological time of expression of each gene . Strains were grown as described previously [82] . When required , antibiotics , streptomycin sulfate ( Sm ) , and spectinomycin dihydrochloride pentahydrate ( Sp ) were added to the media at final concentrations of 2 μg/mL for liquid and 5 μg/mL for solid media . The densities of the cultures were adjusted so as to have a chlorophyll a content of 2–4 μg/mL 24 h prior to the experiment following published procedures [83] . For time lapse measurements , filaments grown in BG110 + ammonium medium ( in the presence of Sm and Sp for the CSL mutants ) containing 2–3 μg/mL of chlorophyll a were harvested , washed three times with nitrogen-free ( BG110 ) medium , and concentrated 50 fold . An agarose low-melting gel pad ( 1 . 5% ) in BG110 medium with 10 mM NaHCO3 was made on a glass microscope slide . About 5 μL of culture were pipetted onto the pad and covered with a #0 mm coverslip , and this device was then placed on the microscope . Anabaena filaments within a device were followed as they developed , at 30°C in light . Filament growth and development within devices are similar to those in bulk cultures ( see section 1 of S1 Text ) . Images were taken every 30 min on a Nikon Eclipse Ti-E microscope controlled by the NIS-Elements software using a 60 N . A 1 . 40 oil immersion phase contrast objective lens ( Nikon plan-apochromat 60 1 . 40 ) and an Andor iXon X3 EMCCD camera . All the filters used are from Chroma . The filters used were ET480/40X for excitation , T510 as dichroic mirror , ET535/50M for emission ( GFP set ) , ET430/24x for excitation , 505dcxt as dichroic mirror , and HQ600lp for emission ( chlorophyll set ) . Samples were excited with a pE-2 fluorescence LED illumination system ( CoolLED ) . To calibrate fluorescence levels in terms of absolute numbers of protein molecules , we followed published methods [52] . In short , the fluorescence level of the i-th cell yi is proportional to the number of fluorescent molecules ni: yi = νni . The proportionality constant ν is given by an average involving the fluorescence levels of mother cells fi and their respective daughters f2i and f2i+1: ν=⟨ ( f2i−f2i+1 ) fi2⟩ Prior to the calculation of ν , a constant background stemming from the contribution of the autofluorescence of photosynthetic pigments in the same region of the spectrum was subtracted from the total GFP fluorescence signal in each cell . The background correction was measured in a wild-type Anabaena sp . PCC 7120 strain bearing no fluorescent reporter . The number of mother–daughter triplets used for the calculation was 51 . The sum of the fluorescence levels of the daughter cells was smaller than that of the respective mother cell by about 2% , primarily due to photo-bleaching . All image processing and data analysis was carried out using MATLAB ( MathWorks ) . Filament and individual cell recognition was performed on phase contrast images using an algorithm developed in our laboratory . The program's segmentation was checked in all experiments and corrected manually for errors in recognition . The total fluorescence from GFP and chlorophyll channels of each cell , as well as the cell area , were obtained as output for further statistical analysis . | Multicellular organisms , from simple to complex , often undergo a developmental process in which cells differentiate into various types , improving survivability under adverse conditions . We study experimentally and theoretically the developmental mechanism of pattern formation in Anabaena sp . PCC 7120 , a multicellular cyanobacterial organism of ancient origin , which forms one-dimensional patterns of single , nitrogen-fixing cells separated by nearly regular intervals of photosynthetic vegetative cells , under nitrogen-poor conditions . By following the developmental process at the level of single cells in real time , we show directly that two genes involved in the inhibition of a nondiffusing activator have different spatiotemporal roles and discuss why a classical , deterministic Turing mechanism may not describe pattern formation in this system . Our stochastic model , which incorporates inevitable fluctuations in molecular numbers or demographic noise , suggests a much more robust mechanism of pattern formation: Noise can seed the formation of transient , stochastic Turing patterns for parameter values in which deterministic patterns do not form . These patterns can then be fixed by downstream genetic commitment pathways . This robust scenario of pattern formation may apply to a wide range of developmental pattern-forming systems . | [
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"prote... | 2018 | Robust stochastic Turing patterns in the development of a one-dimensional cyanobacterial organism |
To better understand telomere biology in budding yeast , we have performed systematic suppressor/enhancer analyses on yeast strains containing a point mutation in the essential telomere capping gene CDC13 ( cdc13-1 ) or containing a null mutation in the DNA damage response and telomere capping gene YKU70 ( yku70Δ ) . We performed Quantitative Fitness Analysis ( QFA ) on thousands of yeast strains containing mutations affecting telomere-capping proteins in combination with a library of systematic gene deletion mutations . To perform QFA , we typically inoculate 384 separate cultures onto solid agar plates and monitor growth of each culture by photography over time . The data are fitted to a logistic population growth model; and growth parameters , such as maximum growth rate and maximum doubling potential , are deduced . QFA reveals that as many as 5% of systematic gene deletions , affecting numerous functional classes , strongly interact with telomere capping defects . We show that , while Cdc13 and Yku70 perform complementary roles in telomere capping , their genetic interaction profiles differ significantly . At least 19 different classes of functionally or physically related proteins can be identified as interacting with cdc13-1 , yku70Δ , or both . Each specific genetic interaction informs the roles of individual gene products in telomere biology . One striking example is with genes of the nonsense-mediated RNA decay ( NMD ) pathway which , when disabled , suppress the conditional cdc13-1 mutation but enhance the null yku70Δ mutation . We show that the suppressing/enhancing role of the NMD pathway at uncapped telomeres is mediated through the levels of Stn1 , an essential telomere capping protein , which interacts with Cdc13 and recruitment of telomerase to telomeres . We show that increased Stn1 levels affect growth of cells with telomere capping defects due to cdc13-1 and yku70Δ . QFA is a sensitive , high-throughput method that will also be useful to understand other aspects of microbial cell biology .
Linear chromosome ends must be protected from the DNA damage response machinery and from shortening of chromosome ends during DNA replication [1] , [2] . Chromosome ends therefore adopt specialized structures called telomeres , distinct from double-stranded DNA breaks elsewhere in the genome . Telomeric DNA is protected , or capped and replicated by a large number of different DNA-binding proteins in all eukaryotic cell types [2] , [3] . In budding yeast , numerous proteins contribute to telomere capping and amongst these are two critical protein complexes , the Yku70/Yku80 ( Ku ) heterodimer and the Cdc13/Stn1/Ten1 ( CST ) heterotrimeric complex [4] . Orthologous protein complexes play roles at telomeres in other eukaryotic cell types suggesting that understanding the function of the Ku and CST protein complexes in budding yeast will be generally informative about key aspects of eukaryotic telomere structure and function . In budding yeast Yku70 is a non-essential protein that has multiple roles in DNA repair and at telomeres , being involved in the non-homologous end-joining ( NHEJ ) DNA repair pathway , in the protection of telomeres and the recruitment of telomerase . The mammalian orthologue , Ku70 , has similar properties [5] . In budding yeast , deletion of the YKU70 gene ( yku70Δ ) results in short telomeres and temperature sensitivity [6] . At high temperatures , cells lacking Yku70 accumulate ssDNA at telomeres , which activates the DNA damage response and leads to cell-cycle arrest [7] , [8] , [9] . Cdc13 is a constituent of the essential budding yeast Cdc13-Stn1-Ten1 ( CST ) protein complex which is analogous to the CST complex found recently in mammalian , plant and fission yeast cells [10] , [11] . Cdc13 binds to ssDNA overhangs at telomeres and functions in telomerase recruitment and telomere capping [12] , [13] , [14] . Acute inactivation of Cdc13 by the temperature sensitive cdc13-1 allele induces ssDNA generation at telomeres and rapid , potent checkpoint-dependent cell cycle arrest [14] . cdc13-1 or yku70Δ mutations each cause temperature dependent disruption of telomere capping that is accompanied by ssDNA production , cell-cycle arrest and cell death [7] , [15] . Interestingly , the poor growth imparted by each mutation can be suppressed by deletion of EXO1 , removing the Exo1 nuclease that contributes to ssDNA production when either Cdc13 or Yku70 is defective [7] . However , cdc13-1 and yku70Δ mutations show a synthetic poor growth interaction [8] and different checkpoint pathways are activated by each mutation [7] . These latter observations , along with numerous others , show that CST and Ku complexes perform distinct roles capping budding yeast telomeres and that further clarification of their functions at the telomere is important to help understand how eukaryotic telomeres function . Many insights into the telomere cap and the DNA damage responses induced when capping is defective were first identified as genetic interactions . For example all DNA damage checkpoint mutations suppress the temperature sensitive growth of cdc13-1 mutants [16] , but only a subset of these suppress the temperature sensitive growth of yku70Δ mutants [7] . We reasoned that the roles of Cdc13 and Yku70 at telomeres could be further understood by quantitative , systematic analysis of genetic interactions between telomere capping mutations and a genome-wide collection of gene deletions . We used standard synthetic genetic array ( SGA ) approaches to combine the systematic gene deletion collection with cdc13-1 and yku70Δ mutations [17] , [18] . After this , strain fitnesses were measured at a number of temperatures by quantitative fitness analysis ( QFA ) . For QFA , liquid cultures were spotted onto solid agar plates and culture growth was followed by time course photography . Images were processed and fitted to a logistic growth model to allow an accurate estimation of growth parameters , such as doubling time . In other high-throughput experiments such as SGA or EMAP approaches , culture fitness is determined from colony size [17] , [18] , [19] . In QFA , analysis of growth curves of cultures grown on solid agar plates allows us to measure fitness more precisely . Through QFA we identify hundreds of gene deletions , in numerous different classes , showing genetic interactions with cdc13-1 , yku70Δ or both . One particularly striking example of the type of genetic interactions we measured by QFA is between deletions affecting nonsense mediated RNA decay pathways ( upf1Δ , upf2Δ , upf3Δ ) , cdc13-1 and yku70Δ . Additional experiments show that disabling nonsense mediated mRNA decay pathways , using upf2Δ as an example , suppresses the cdc13-1 defect but enhances the yku70Δ defect by increasing the levels of the telomere capping protein Stn1 . QFA is generally applicable and will be useful for understanding other aspects of yeast cell biology or studying other microorganisms .
To systematically examine genetic interactions between a genome-wide collection of gene deletion strains ( yfgΔ , your favorite gene deletion , to indicate any of ∼4200 viable systematic gene deletions ) and mutations causing telomere capping defects we crossed the knockout library to cdc13-1 or yku70Δ mutations , each affecting the telomere , or to a neutral control query mutation ( ura3Δ ) using SGA methodology [17] , [18] . Since both cdc13-1 and yku70Δ mutations cause temperature sensitive defects , we generated all double mutants at low , permissive temperatures before measuring the growth of double mutants at a number of semi-permissive or non-permissive temperatures . We cultured yku70Δ yfgΔ strains at 23°C , 30°C , 37°C and 37 . 5°C , cdc13-1 yfgΔ strains at 20°C , 27°C and 36°C and ura3Δ yfgΔ strains at 20°C , 27°C and 37°C and measured fitness . Double mutant fitness was measured after spotting of dilute liquid cultures onto solid agar . We estimate approximately 100 separate cells were placed in each of 384 spots on each agar plate . Fitness of thousands of individual cultures , each derived from spotted cells , was deduced by time course photography of agar plates followed by image processing , data analysis , fitting of growth measurements to a logistic model and determination of quantitative growth parameters ( Figure 1 ) [20] , [21] , [22] . We fitted logistic growth model parameters to growth curves allowing us to estimate maximum doubling rate ( MDR , population doublings/day ) and maximum doubling potential ( MDP , population doublings ) of approximately 12 , 000 different yeast genotypes ( e . g . cdc13-1 yfg1Δ , yku70Δ yfg1Δ , etc . ) at several temperatures . At least eight independent biological replicates for each strain at each temperature were cultured and repeatedly photographed , capturing more than 4 million images in total . To rank fitness we assigned equal importance to maximum doubling rate and maximum doubling potential and defined strain fitness as the product of the MDR and MDP values ( Fitness , F , population doublings2/day ) . Other measures of fitness can be derived from the sets of logistic parameters available from Text S1 . Figure 1A shows approximately 170 example images , corresponding to eight independent time courses for each of three pair-wise combinations of yku70Δ , ura3Δ and upf2Δ mutations . These example images clearly show , qualitatively , that upf2Δ yku70Δ strains grow less well than yku70Δ ura3Δ strains , which in turn grow less well than upf2Δ ura3Δ strains at 37°C . These fitness measures are consistent with numerous earlier studies , showing that yku70Δ mutants do not grow well at high temperatures , but also demonstrate a novel observation , that the upf2Δ mutation enhances the yku70Δ defect and this is further investigated below . Images like those in Figure 1A were processed , quantified , plotted and logistic growth curves fitted to the data ( Figure 1B ) . We applied QFA to all genotypes at each temperature , as the three examples in Figure 1C illustrate . QFA of cdc13-1 yfgΔ , yku70Δ yfgΔ and ura3Δ yfgΔ double mutant libraries was performed at a number of temperatures and therefore a variety of informative comparisons were possible . For example to help identify gene deletions that suppress or enhance the yku70Δ temperature dependent growth defect it is useful to compare the fitness of yku70Δ yfgΔ cells incubated at 37 . 5°C , with that of control , ura3Δ yfgΔ , cells incubated at 37°C . In Figure 2 , genes which , when deleted , suppress the yku70Δ phenotype at 37 . 5°C will be positioned above the linear regression line and enhancers of yku70Δ defects below the line . yfgΔ mutations that result in low fitness when combined with the neutral ura3Δ mutation will be found on the left and those with high fitness on the right of the x-axis . The location of each gene in Figure 2 indicates the effect of each deletion on fitness of yku70Δ strains versus the effect of the deletion on fitness of ura3Δ strains . The regression line drawn through all data points ( solid gray line ) indicates the expected yku70Δ yfgΔ fitness given the fitness of the corresponding ura3Δ yfgΔ mutant . The line of equal growth ( dashed gray line ) shows the expected positions of yku70Δ yfgΔ strains if they grew similarly to ura3Δ yfgΔ strains . Comparing the linear regression with the line of equal growth , it is clear that yku70Δ mutants grow poorly relative to control ura3Δ mutants , as expected due to the temperature dependent telomere uncapping observed in yku70Δ mutants . Figure 2 also highlights large numbers of yku70Δ yfgΔ strains growing significantly better than expected , given the fitness of the equivalent ura3Δ yfgΔ mutation at 37°C ( red data points , Figure 2 ) and these yfgΔ genes can be classified as yku70Δ suppressors . There are also large numbers of yku70Δ yfgΔ strains that grow worse than expected and these are classified as yku70Δ enhancers ( green data points , Figure 2 ) . Three further example plots comparing growth of yfgΔ cdc13-1 versus yfgΔ ura3Δ at 20°C; yfgΔ cdc13-1 versus yfgΔ ura3Δ at 27°C and yfgΔ ura3Δ at 37°C versus yfgΔ ura3Δ at 20°C are shown in Figure S1 and others can be found on our supporting information data files website . We estimated genetic interaction strength ( GIS ) as the vertical displacement of each yku70Δ yfgΔ normalised mutant fitness from the expected normalised fitness , with expected fitness given by a linear regression model ( see Text S1 , experimental procedures ) . GIS is dimensionless . This method is equivalent to defining GIS as the deviation of observed fitness from that expected if a multiplicative model of genetic interaction were correct . In all , more than 30 , 000 genetic interaction strengths , together with their statistical significances , were calculated ( Tables S1 , S2 , S3 , S4 , S5 , S6 ) . Table 1 summarizes the numbers of statistically significant genetic interactions observed under the different conditions of telomere capping . Table 1 clearly illustrates that many more genetic interactions are observed under conditions of mild telomere uncapping ( cdc13-1 strains at 27°C and yku70Δ strains at 37 . 5°C ) and that at these temperatures around 5% of gene deletions can show strong suppressing or enhancing interactions ( GIS >0 . 5 ) . In order to compare the effects of gene deletions on cell fitness when combined with cdc13-1 or yku70Δ induced telomere cap defects , it was particularly useful to compare the GIS of each gene with respect to cdc13-1 or yku70Δ after induced telomere uncapping . Figure 3 summarises how different gene deletions interact with the two types of telomere capping defect , suppressing , enhancing or showing no strong interaction with each telomere cap defect . For example , genes that when deleted significantly suppress temperature sensitivity of both cdc13-1 and yku70Δ mutants appear in the top right of this plot ( Figure 3 , region 3 ) . EXO1 is in this area as expected because Exo1 generates ssDNA at telomeres in both types of telomere capping mutants ( Figure 3 , region 2/3 , arrow ) [7] . Deleting components of the checkpoint sliding clamp ( 9-1-1 complex ) and its clamp loader , suppress cdc13-1 but have minor effects on growth of yku70Δ mutants [7] . DDC1 , RAD17 and RAD24 are in region 2 , as expected . MEC3 , encoding the third component of the sliding clamp was missing from our knock out library and was not tested . Gene deletions that disrupt the telomerase enzyme directly ( est1Δ , est3Δ ) enhance the temperature sensitivity of both mutations and so appear in region 7 . Genes that , when deleted , suppress cdc13-1 yet enhance the yku70Δ temperature sensitivity ( Figure 3 , region 1 ) represent a novel telomere-related phenotype and interestingly include three major components of the nonsense mediated RNA decay pathways ( UPF1 , UPF2 , UPF3 ) . It is reassuring that the UPF genes cluster so closely in Figure 3 because this strongly suggests that positioning of genes on this plot is an accurate measure of the function of the corresponding gene products in telomere biology . The position of YKU80 in the bottom right corner of region 8 is informative . The negative interaction of yku80Δ with cdc13-1 is expected since it is known that yku80Δ ( and yku70Δ ) mutations reduce fitness of cdc13-1 mutants , even at permissive temperatures [8] . However , the positive effect of yku80Δ on the growth of yku70Δ mutants appears , at first , surprising . The positive epistatic effect simply reflects the fact that yku70Δ , yku80Δ and yku70Δ yku80Δ double mutants are all similarly unfit at high temperatures . We have confirmed that in the different W303 genetic background that yku70Δ , yku80Δ and yku70Δ yku80Δ double mutants are all similarly unfit at high temperatures . According to the multiplicative model of epistasis the fitness of the yku70Δ yku80Δ double mutants is significantly higher than expected based on the fitness of the single mutants . Thus , by this criterion , yku80Δ suppresses the yku70Δ fitness defect . These data can be explained if neither single sub-unit of the Ku comlex retains a telomere capping function in the absence of the other . It is reasonable to hypothesize , based partly on the behaviour of UPF1 , UPF2 and UPF3 genes , that genes having similar genetic interactions with cdc13-1 and yku70Δ under particular conditions which are proximal in Figure 3 share similar functions in telomere biology . For example , genes that function similarly to EXO1 and for example , regulate ssDNA at uncapped telomeres might appear close to EXO1 in Figure 3 . Similarly , genes with strong effects on telomerase function might appear in region 7 . Consistent with this hypothesis , it is clear from Figure 3 that many genes encoding members of the same protein complex , or proteins which work together to perform a particular function , often have similar genetic interaction profiles and are located in similar positions on this plot . Examples in Figure 3 include: NMD pathway ( UPF1 , UPF2 , UPF3 , region 1 ) ; OCA complex ( regions 1 & 4 ) ; clamp-loader and clamp-like complex ( RAD24 , DDC1 , RAD17 , region 2 ) ; telomerase ( EST1 , EST3 , region 7 ) and dipthamide biosynthesis ( JJJ3 , DPH1 , DPH2 , DPH5 , regions 8 & 9 ) genes , as well as the numerous other complexes highlighted by the key at the bottom of Figure 3 . Table 2 shows the number of genes found in each section of Figure 3 . Table 3 lists 19 different sets of genes that are functionally or physically related and that cluster in Figure 3 as well as the single genes EXO1 , RIF1 , RIF2 and TEL1 also found in interesting positions . EXO1 is in its expected position but it is interesting that RIF1 and RIF2 are found in different positions in Figure 3 , suggesting they have different functions in telomere biology . Further experiments in the W303 genetic background confirm the different interactions of RIF1 and RIF2 with cdc13-1 ( Xue , Rushton and Maringele , submitted ) . TEL1 encodes the ATM orthologue and is required for telomere maintenance and it clusters very near components of telomerase , in region 7 . Groupings such as these and their positioning on this type of plot help generate testable , mechanistic predictions about the roles of proteins/protein complexes on telomere capping in budding yeast . For example , we predict that NMD genes ( which we examine further in this study ) and dipthamide synthesis genes have opposing effects on both Cdc13-mediated and Yku70-mediated telomere capping , because they lie in opposite corners of Figure 3 . The QFA experiments summarised by Figure 3 were performed in a high-throughput manner with the systematic knock out collection in the S288C genetic background and the fitness of different query mutants was measured in slightly different types of media . It was therefore conceivable that some of the genetic interactions scored were due to: defects in the knock out collection , such as incorrect mutations being present or the presence of suppressor mutations , the S288C genetic background , subsets of the cell populations that progressed through the mass mating , sporulation and germination that occur during SGA or media differences . To test whether genetic interactions identified by QFA with cdc13-1 or yku70Δ strains were robust observations we retested a subset of interactions in the W303 genetic background , on rich media , after construction of strains by individual tetrad dissection by manual spot test . Figure 4 and Figure S2 show the behaviour of a number of gene deletions chosen from different regions in Figure 3 to test the effects in W303 . In all we measured 26 genetic interactions between 13 gene deletions and cdc13-1 or yku70Δ . Of these we estimate that 20/26 interactions were as expected , 5/26 difficult to classify , and 1/26 , due to elp6Δ , opposite to that expected after QFA . In particular exo1Δ , mlp1Δ , mlp2Δ , mak31Δ and dph1Δ mutations suppress the growth defects of yku70Δ strains in W303 at 36°C , consistent with their position on the right side of Figure 3 and exo1Δ , rad24Δ , upf1Δ and upf2Δ strongly suppress cdc13-1 at 26°C consistent with their position near the top of Figure 3 . upf1Δ , upf2Δ , rrd1Δ and pph3Δ mutations all reduced growth of yku70Δ strains at 36°C consistent with their position on the left of Figure 3 , while elp6Δ , mak31Δ , dph2Δ , rrd1Δ and pph3Δ mutations all enhanced cdc13-1 growth defects consistent with their positions near the bottom of Figure 3 . Other genes have more subtle effects , the oca1Δ and oca2Δ mutations had marginal effects on yku70Δ strains but improved growth of cdc13-1 strains ( Figure S2 ) . Interestingly the elp6Δ mutation enhanced the cdc13-1 defect at 26°C , as expected , but suppressed the yku70Δ strain growth defect at 36°C , the opposite of what was expected from Figure 3 . Further experiments will be necessary to clarify the role of Elp6 and other elongator factors in cells with uncapped telomeres . However , overall , it is clear that the majority of genetic interactions identified by QFA are reproducible in smaller scale experiments in a different genetic background . Suppressors and enhancers of the cdc13-1 and yku70Δ phenotypes were most easily identified at semi-permissive temperatures for the query mutations ( Figure 2 , Figure 3 ) , however QFA at other temperatures also proved informative . For example , comparison of the fitness of yfgΔ ura3Δ strains at 37°C versus 20°C , allowed us to identify temperature sensitive mutants ( Figure S1C and Table S9 ) . Of the 57 genes which were categorized with a phenotype of “heat sensitivity: increased” in the Saccharomyces Genome Database ( http://www . yeastgenome . org ) , as identified by low though-put experiments , which were also present in the knockout library we used , 45 ( 79% of total ) were identified as being significantly heat sensitive by our independent QFA . 2-dimensional GIS plots , like Figure 3 , also proved useful for identifying broader patterns of genetic interactions . For example , we observed a difference between the effects of deleting small and large ribosomal subunit genes on the growth of telomere capping mutants ( Figure S3A , S3B ) . Gene deletions which affect the small ribosomal subunit are generally neutral with both cdc13-1 and yku70Δ mutations ( Figure S3A , S3B red ) . In contrast , disruptions of large ribosomal subunit function suppress the effect of cdc13-1 on average and enhance that of yku70Δ ( Figure S3A , S3B blue ) . Although the basis for this novel observation is unknown it may be related to the finding that the large ribosome sub-unit is subject to autophagy upon starvation , whereas the small ribosome sub-unit is not [23] . Positive and negative regulators of telomere length [24] , [25] , [26] also showed differing distributions in GIS comparisons – gene deletions which suppress the yku70Δ defect are more likely to result in long than short telomeres ( Figure S3C ) . This is perhaps to be expected since yku70Δ mutants , on their own , have a short telomere phenotype . Importantly , over 90% of genes identified as suppressors of cdc13-1 in a previous study [20] showed a positive GIS with cdc13-1 ( Figure S2D ) , demonstrating that QFA reproduces conclusions derived from qualitatively scored visual inspection . It should be noted however , that the improved sensitivity of QFA has allowed identification of significantly more enhancing mutations than were indentified in the preceding , qualitatively scored study [20] . QFA is sensitive enough to permit identification of genetic interactions even where gene deletions combined with the control ura3Δ query mutation impart a poor growth phenotype . For example , deletion of all three SPE genes resulted in low fitness that was strongly rescued by cdc13-1 ( Figure S1B , blue , Figure 3 region 2 ) . Interestingly it has recently been reported that increasing spermidine levels increases lifespan in organisms such as yeast , flies and worms [27] , but no previous connection with telomeres has been made . Telomere-driven , replicative senescence is thought to be a significant component of the ageing phenotype . Our observations of interactions between SPE genes and cells with uncapped telomeres may ultimately lead to experiments to provide insight into the mechanisms by which spermidine affects lifespan . One of the most striking results obtained from QFA experiments was the effect of deleting nonsense mediated RNA decay genes on growth of cells with telomere capping mutations . Deletion of any of the NMD genes UPF1 , UPF2 or UPF3 suppresses the cdc13-1 telomere capping defect but enhances the yku70Δ defect ( Figure 3 , region 1 ) . We wanted to understand the basis of this interesting interaction and decided to further analyze the NMD genes . We also investigated EBS1 , a gene that has proposed roles in both the NMD pathway and telomere function [28] , [29] , [30] and was identified previously as interacting with CDC13 [20] , [31] . EBS1 had less strong , but qualitatively similar GISs to UPF genes in our analysis ( Figure 3 , region 1∼2 ) , suggesting that the position of EBS1 in Figure 3 was due a partial defect in nonsense mediated RNA decay . One potential mechanism by which UPF genes and EBS1 affect telomere capping is if they regulate the levels of telomere capping proteins . Indeed , UPF genes have been shown to regulate transcripts of genes involved in telomere function [32] , [33] . The effect of EBS1 on these transcripts has not so far been reported . Therefore we compared mRNA levels of three NMD targets with roles in telomere regulation ( STN1 , TEN1 and EST2 ) in upf2Δ , ebs1Δ and wild-type strains . Transcripts of STN1 and TEN1 were increased significantly in upf2Δ and ebs1Δ , mutants whereas EST2 transcripts were increased only in upf2Δ strains ( Figure 5A ) . We conclude that both EBS1 and UPF2 modulate expression of STN1 and TEN1 , but the effects of ebs1Δ are modest compared to those of upf2Δ . Furthermore , elevated levels of Stn1 protein were detected in both ebs1Δ and upf2Δ mutants ( Figure 5B ) . Consistent with the measured mRNA levels of STN1 , the increase in Stn1 levels was smaller in ebs1Δ strains than upf2Δ strains . Thus we concluded that the effects of UPF2 and EBS1 could be due to the effects on Stn1 and possibly Ten1 levels . Increased Stn1 and Ten1 levels are known to suppress the cdc13-1 defect [33] , [34] . To test whether elevated levels of Stn1 or Ten1 proteins could reproduce the enhancement of the yku70Δ defect observed in ebs1Δ and upf2Δ mutants , we over-expressed Stn1 and Ten1 independently of NMD by providing extra copies of STN1 and TEN1 on plasmids . Both single copy ( centromeric; Figure 5C ) and high copy ( 2 µ ) Stn1-expressing plasmids [35] suppressed the temperature sensitivity of cdc13-1 strains and enhanced the temperature sensitivity of yku70Δ strains ( Figure S4A ) , mimicking the upf2Δ and ebs1Δ phenotypes . In contrast , Ten1-expressing plasmids [35] did not affect the growth of either cdc13-1 or yku70Δ mutants ( Holstein; data not shown ) . We therefore conclude that both UPF2 and EBS1 affect telomere capping by modulating expression of STN1 . However , it is also possible that UPF2 and EBS1 affect telomere capping by modulating expression of genes other than STN1 . To test this and the relative contribution of STN1 versus any other mechanisms , it would be informative to reduce STN1 expression in upf2Δ mutants . Such experiments might be difficult to perform and interpret since both centromeric single-copy and 2 micron multi-copy STN1 plasmids suppress the cdc13-1 defect to similar extents ( Figure S4A ) , suggesting there is not a simple correlation between Stn1 levels and effects on growth of cdc13-1 and yku70Δ mutants . Since the effect of ebs1Δ was milder than that of upf2Δ on the fitness of cdc13-1 and yku70Δ cells ( Figure 3 ) , we hypothesized that if ebs1Δ imparts a mild NMD defect , an ebs1Δ upf2Δ double mutation would result in the same phenotype as upf2Δ on its own . Figure 5D shows that both upf2Δ and ebs1Δ mutations suppress cdc13-1 temperature sensitivity and exacerbate yku70Δ temperature sensitivity in the W303 genetic background . We also confirmed that upf1Δ upf2Δ double mutants suppress cdc13-1 temperature sensitivity and exacerbate yku70Δ temperature similarly to either single mutant ( Figure S4B ) . It is clear that the effects of ebs1Δ are less strong than upf2Δ mutations and interestingly upf2Δ ebs1Δ double mutants have slightly stronger effects on growth of both cdc13-1 and yku70Δ mutants , suggesting that ebs1Δ effects are not solely due to defects in nonsense mediated RNA decay ( Figure 5D ) . We therefore conclude that , at least with respect to telomere capping , EBS1 and UPF2 act partially through different pathways . We do not yet understand these differences , but they may be related to the homology between Ebs1 and the telomerase protein Est1 . It is simple to hypothesize why increased Stn1 levels , caused by inactivation of nonsense mediated mRNA decay pathways , suppress the cdc13-1 defect , presumably by stabilizing the Cdc13-1/Stn1/Ten1 complex at telomeres . It is less simple to explain why increased Stn1 levels enhance the yku70Δ-induced telomere-capping defect . Our hypothesis is based on the facts that the Stn1 protein can inhibit telomerase activity [36] , [37] and that Yku70 interacts with and helps recruit telomerase to telomeres [38] , [39] . Thus we hypothesized that yku70Δ causes a partial defect in telomerase recruitment , one that is exacerbated by the upf2Δ mutation that causes high levels of Stn1 , thus inhibiting telomerase activity . To test the simplest version of this hypothesis , that yku70Δ and upf2Δ mutations reduce the amount of telomerase binding to telomeres , we performed ChIP analyses . We examined binding of the Est2 sub-unit of telomerase in yku70Δ , upf2Δ and yku70Δ upf2Δ double mutants . Interestingly we observed a significant reduction in binding of telomerase to telomeres in yku70Δ , upf2Δ and yku70Δ upf2Δ mutants ( Figure 5E ) . It is known that yku70Δ mutants recruit less telomerase to telomeres [39] but we are unaware of any other reports showing that upf2Δ mutants recruit less telomerase to telomeres . This observation most likely explains the short telomere phenotype of upf2Δ ( as well as yku70Δ ) mutants [24] , [25] . It is noteworthy that although the upf2Δ mutation causes a four-fold increase in the EST2 transcript , it causes a reduction in the amount of Est2 bound to telomeres . This suggests that the increased levels of Stn1in upf2Δ cells more than counteracts any mass action effects on telomerase recruitment to telomeres caused by EST2 over-expression . However , the simple hypothesis that yku70Δ upf2Δ mutants show a more severe capping defect because of a reduction in the recruitment of telomerase appears not to be valid . Further experiments will be necessary to better understand the complex interplay between Ku , nonsense mediated decay pathways , Cdc13 , Stn1 and telomere capping ( Figure 6 ) .
Systematic measurement of genetic interactions is a powerful way to help understand how cells and organisms function [40] , [41] . This is because genetic approaches examine the role of individual gene products , or individual residues in genes , in the context of the whole organism and can help dissect the effects of weak biochemical interactions that are important for cells to function [42] . Systematic SGA and eMAP experiments typically examine millions of genetic interactions and use comparatively crude measures of growth ( colony size ) to infer genetic interactions [19] , [41] . Here we have more accurately measured a smaller number of genetic interactions , focusing on interactions that affect budding yeast telomere function . The telomere is an important and interesting subject for systematic genetic analysis because it is a complex , subtle and in some senses paradoxical nucleic acid/protein structure that plays critical roles during human ageing and carcinogenesis . One paradox of telomeres is that many DNA damage response proteins , which induce DNA repair or cell cycle arrest when interacting elsewhere in the genome , induce neither response at telomeres but instead play important roles in telomere physiology . We used Quantitative Fitness Analysis ( QFA ) to accurately assess the fitness of many thousands of yeast strains containing mutations that affect telomere function in combination with other deletion mutations . To assess fitness , cells were grown in parallel , in 384 spot arrays on solid agar plates . Photographs of plates were taken , images processed and analysed and growth curves for each culture generated . The growth curves are in essence very similar to those observed in liquid culture , with clear exponential and saturation phases ( Figure 2 from Lawless et al . 2010 ) and can be summarized with as few as three logistic growth parameters . The major advantage of QFA over parallel liquid culture methods to measure yeast fitness is that many more cultures can be examined in parallel . For example we routinely follow the growth of about 18 , 000 parallel cultures ( 4 , 500 yeast strains , incubated at four different temperatures ) , whereas parallel liquid culture based methods are generally restricted to up to 200 parallel cultures [43] . QFA is similar to SGA or EMAP approaches but typically four times fewer strains per plate are cultured ( 384 spots versus 1536 colonies ) [17] , [18] , [19] , [41] . A further difference between QFA and SGA is that in QFA , which has a liquid growth phase , double mutants are cultured for longer before fitness is assessed . This means that that in QFA , synthetically sick double mutants often show poorer growth than is observed in SGA experiments simply because the more divisions that occur the easier it is to observe growth defects . There is a risk with QFA that during the comparatively long culturing period that suppressors or modifiers will arise . In the experiments we performed in this paper the double mutants show conditional , temperature sensitive defects and were generated in permissive conditions where there was little selection for suppressors/modifiers . The principal advantage of QFA over SGA and EMAP is that QFA provides more accurate fitness measurements that can be measured at higher culture densities . The accuracy of QFA is indicated by the tight clustering of genes affecting particular biochemical pathways/functions in Figure 3 . QFA is also lower throughput than “bar code” based assays where up to 6000 independent strains compete in a single culture [44] . One principal difference between QFA and bar code competition methods is that fitness measures are absolute , rather than comparative . Comparison of genetic interactions observed in yeast cells containing cdc13-1 or yku70Δ mutations , affecting telomeres in different ways , has generated numerous new insights into telomere biology . For example , we have identified at least 19 groups of genes , each representing a particular protein complex or biological process , that significantly affect growth of cells with telomere capping defects in different ways and these are highlighted in Figure 3 . Each of these groups of genes , as well as numerous individual genes , warrant further investigation to characterize how they influence the telomere cap . In this paper we followed up just one striking observation that deletions of NMD pathway genes suppress the cdc13-1 temperature-sensitive phenotype and enhance the yku70Δ temperature sensitive phenotype . In upf2Δ strains , levels of STN1 transcripts and levels of Stn1 protein increased . Our detailed follow-up observations are consistent with the hypothesis that the NMD pathway influences Cdc13- or Yku70-mediated telomere capping through modification of Stn1 but not Ten1 levels ( Figure 6 ) . As well as helping generate hypotheses about the roles of individual gene products at telomeres QFA will be ideal for developing , constraining and testing dynamic , systems models of the effects of complex biological processes on telomere function . Any model describing cellular growth and division as an outcome of the complex interaction of gene products e . g . [45] could usefully be parameterized and tested by QFA . We expect QFA to be widely applicable to other quantitative phenotypic screens in budding yeast and other microbial systems .
Library strains created using SGA in this study were cultured in SD/MSG media [17] with appropriate amino-acids and antibiotics added – Canavanine ( final concentration , 50 µg/ml ) ; G418 ( 200 µg/ml ) ; thialysine ( 50 µg/ml ) ; clonNAT ( 100 µg/ml ) ; hygromycin B ( 300 µg/ml ) . Media were made lacking arginine when using canavanine and lacking lysine when using thialysine . W303 genetic background strains were cultured in YEPD ( ade ) . Cell lysis and western blot analysis were performed as previously described [46] . Antibody 9E10 from Cancer Research UK was used to detect the C-Myc epitope and anti-tubulin antibodies , from Keith Gull , Oxford , UK , used as loading controls . RNA extraction and RT-PCR were performed as previously described [47] . RNA concentrations of each sample were normalized relative to the loading control , BUD6 . Chromatin immunoprecipitation was performed as previously described with minor modifications [48] . Mouse anti-myc ( 9E10 ) or goat anti-Mouse antibodies were used for the immunoprecipitations . Immunoprecipitated DNA was quantified by RT-PCR using the SYBR Green qPCR SuperMIX-UDG w/ROX kit ( Invitrogen , 11744500 ) . Rectangular , single chamber , SBS footprint plates ( omnitrays; Nunc , Thermo Fisher Scientific ) were filled with 35 ml molten agar media using a Perimatic GP peristaltic pump ( Jencons ( Scientific ) Limited , Leighton Buzzard , UK ) fitted with a foot switch . 96-well plates ( Greiner Bio-One Ltd . ) were filled with liquid media or distilled H2O ( 200 µl per well ) using a Wellmate plate-filler with stacker ( Matrix Technologies , Thermo Fisher Scientific ) . Solid agar to solid agar pinning was performed on a Biomatrix BM3-SC robot ( S&P Robotics Inc . , Toronto , Canada ) using either 384-pin ( 1 mm diameter ) or 1536-pin ( 0 . 8 mm diameter ) pintools . Inoculation from solid agar to liquid media was performed on the Biomatrix BM3-SC robot using a 96-pin ( 1 mm diameter ) pintool . Resuscitation of frozen strain collections ( from liquid to solid agar ) was performed on the Biomatrix BM3-SC robot using a 384-pin ( 1 mm diameter ) tool . Re-array procedures were carried out using the BM3-SC robot equipped with a 96-pin rearray pintool . Dilution and spotting of liquid cultures onto solid agar plates was performed on a Biomek FX robot ( Beckman Coulter ( UK ) Limited , High Wycombe , UK ) equipped with a pintool magnetic mount and a 96-pin ( 2 mm diameter ) pintool ( V&P Scientific , Inc . , San Diego , CA , USA ) . Both the Biomatrix BM3-SC and the Biomek FX were equipped with bar-code readers ( Microscan Systems , Inc . ) and the bar-codes of plates involved in each experiment were recorded in robot log-files . All strains , strain collections oligonucleotide primers and plasmids are described in Text S1 . Single gene deletion collections ( a gift from C . Boone ) were stored at −80°C in 384-well plates ( Greiner BioOne ) in 15% glycerol and when required , were thawed and pinned onto YEPD + G418 agar . Strains were then routinely pinned onto fresh YEPD + G418 agar plates approximately every two months but were re-pinned from frozen stocks after approximately 6 months . An array containing 6 replicates of 12 telomere-related genes , 14 replicates of his3Δ and 6 replicates of 37 randomly chosen genes was created from the original deletion collection ( SGAv2 ) . This array ( plate 15 in our deletion mutant collections ) was designed to quickly check that gene deletions with familiar phenotypes were behaving as expected and to also provide high numbers of replicates for a small number of genes ( 49 ) allowing more robust statistical analysis . This collection was SGAv2p15 . Collection SGAv3 was made by re-arraying each of the 15 plates of SGAv2p15 , randomly , with the exceptions that all his3Δ strains on the plate periphery [17] were not moved and genes which were in the corner area of plates in SGAv2p15 were specifically moved to non-corner positions in SGAv3 . Liquid-to-solid agar 384-format robotic spot tests were performed as follows . Colonies were inoculated from solid agar SGA plates into 96-well plates containing 200 µl appropriately supplemented liquid SD/MSG media in each well . These were grown to saturation ( usually three days ) , without shaking , at 20°C . Cultures were resuspended , diluted approximately 1/100 in 200 µl H2O and spotted onto appropriately supplemented solid SD/MSG media plates which were incubated at different temperatures . SGA query strains DLY5688 ( cdc13-1 flanked by LEU2 and HPHMX ( HygromycinR ) ) , DLY3541 ( yku70Δ::URA3 ) and DLY4228 ( ura3::NATMX ) were crossed to SDLv2p15 and SDLv3 in quadruplicate , giving eight biological replicate crosses each . Fitness of each strain under different conditions was assayed in 384-spot growth assays . As previously [20] , growth at 36°C was used as an indication of failure of the SGA process or spontaneous reversion in SGA screens where cdc13-1 was the query mutation . In this study , repeats with modeled Trimmed Area >25000 after 6 days at 36°C ( provided this included no more than 3 repeats for a single gene deletion ) were stripped out . In each SGA experiment , a small number of strains were missing from the starting mutant array ( due to mis-pinning , strains being lost , replaced etc . ) . These experiment-specific missing strains; together with genes affecting selection during SGA; and experiment-specific genes situated within 20 kb of SGA markers; were removed from analysis . Solid agar plates were photographed on an spImager ( S&P Robotics Inc . , Toronto , Canada ) . The integrated camera ( Canon EOS 40D ) was used in manual mode with a pre-set manual focus . Manual settings were as follows: exposure , 0 . 25 s; aperture , F10; white balance , 3700 K; ISO100; image size , large; image quality , fine; image type , . jpg . Using the spImager software , the plate barcode number and a time stamp ( date in year , month , day and time in hour , minute , second ) were incorporated as the image name ( e . g . DLY00000516-2008-12-24_23-59-59 . jpg ) . The image analysis tool Colonyzer [21] was used to quantify cell density from captured photographs . Colonyzer corrects for lighting gradients , removing spatial bias from density estimates . It is designed to detect cultures with extremely low cell densities , allowing it to capture a wide range of culture densities after dilute spotting on agar . Colonyzer is available under GPL at http://research . ncl . ac . uk/colonyzer . We directly compared QFA of pinned 1536- colony format versus spotted 384- culture format and found that the range of normalized 384 spot fitness is approximately 4 times that estimated from 1 , 536 colony growth curves ( Lawless et al . , in prep ) . We also find that 384 spot fitness estimates adequately captures the strong temperature dependent growth of cdc13-1 mutants , whereas 1536-format growth estimates do not , and that analysis of growth in 384 spot format captures a much higher dynamic range of cell densities than 1536 colony format ( approx 1 , 000 versus 20 fold , see Fig . 2 , Lawless et al , 2010 ) . For these reasons we chose to perform QFA of telomere capping mutants arrayed as 384 spotted cultures . Strain array positions on a 384-spot layout ( plate , row , column ) were defined in a comma-separated text file and tracked using bar-codes reported in robot log-files . Data was stored in a Robot Object Database ( ROD ) as described previously [20] . Screen data is exported from ROD in tab delimited format ( Table S7 ) ready for modeling and statistical analysis ( see below ) . Culture density ( G ) was estimated from captured photographs using the Integrated Optical Density ( IOD or Trimmed Area; Table S7 ) measure of cell density provided by the image-analysis tool Colonyzer ( Lawless et al 2010 ) . Observed density time series were summarised with the logistic population model , which is an ODE describing self-limiting population growth . It has an analytical solution G ( t ) : Modelled inoculum density ( G0 , AU ) was fixed ( at 43 AU in this case ) , assuming that all liquid cultures reached the same density in stationary phase before water dilution and inoculation onto agar . Logistic parameter values r ( growth rate , d−1 ) and K ( carrying capacity , AU ) were inferred by least squares fit to observations , using optimization routines in the SciPy Python library ( code available from http://sourceforge . net/projects/colonyzer/ ) . For least-squares minimisation , initial guesses for K were the maximum observed cell density for that culture . For r , we constructed initial guesses by observing that G' ( t ) is at a maximum when t = t*: Linearly interpolating between cell density observations we estimated the time of greatest rate of change of density . We then estimated r as: A quantitative measure of fitness was then constructed from the optimal parameters . The particular measure we used was the product of the maximal doubling rate ( MDR , doublings . d−1 ) , which is the inverse of the doubling time and the maximal doubling potential ( MDP , doublings ) . These phenotypes were quantified using logistic model parameter estimates as follows . We estimate the minimum doubling time T which the cell population takes to reach a density of 2G0 ( assuming that the culture is in exponential phase immediately after inoculation ) : MDP is the number of divisions the culture is observed to undergo . Considering cell growth as a geometric progression: These two phenotypes provide different information about the nature of population fitness and both of them are important , reflecting the rate of growth ( MDR ) and the capacity of the mutant to divide ( MDP ) under given experimental conditions . Our chosen measure of fitness ( F = MDR×MDP ) places equal importance on these two phenotypes . To estimate GIS , F is obtained for a particular temperature for both the QFA screen of interest and a second QFA screen using a control query mutation , ura3Δ , which is assumed to be neutral under the experimental conditions , approximating wild-type fitness . Experimental and control strain fitnesses are analysed for evidence of epistatic interactions contradicting a multiplicative model of genetic independence [49] ( used due to the ratio scale of the phenotype ) . We denote the fitness of the query ( or background ) mutation Fxyz , that of a typical deletion from the yeast knockout library FyfgΔ and double mutant fitnesses as Fxyz yfgΔ . Genetic independence therefore implies:and re-arranging gives:where M = Fxyz/Fura3Δ is a constant independent of the particular knockout , yfgΔ . Thus , after normalising fitnesses ( ) so that the means across all knockouts for both the experimental ( QFA , xyz yfgΔ ) and control ( cQFA , ura3Δ yfgΔ ) mutation strains are equal to 1 , evidence that is significantly different from is evidence of genetic interaction . Thus for each knockout a model is fitted of the form: where i = 1 , 2 , j = 1 , . . , ni is the jth normalised fitness for treatment i ( cQFA = 1 , QFA = 2 ) , µ is the mean fitness for the knockout in the control QFA , γ1 = 0 , γ2 represents genetic interaction and εij is ( normal , iid ) random error . Typically ni is 8 ( 4 replicates each of SGAv2p15 and SGAv3 ) , but is sometimes a larger multiple of 8 for strains that are repeated in the libraries ( e . g . those on plate 15 ) . The model is fitted in R using the lmList command . For each knockout the fitted value of γ2 is recorded as an estimated measure of the strength of genetic interaction ( with the sign indicating suppression or enhancement ) and the corresponding p-value is used as a measure of statistical significance of the effect . The p-value is corrected using the R function p . adjust to give a FDR-corrected q-value , and it is this q-value which is thresholded to give the lists of statistically significant genetically interacting strains ( see Figure 2 ) . The R code used for the statistical analysis of data from ROD and Colonyzer is available from the authors on request and sample logistic analysis output is presented in Table S8 . Stringent lists of genetic interactors for each query mutation and growth condition ( Tables S1 , S2 , S3 , S4 , S5 , S6 ) were compiled by imposing a 5% FDR cutoff and arbitrarily removing genes with −0 . 5< GIS >0 . 5 . Raw output data and hyperlinked supplementary tables , together with detailed legends for interpretation of data files are available from: http://research . ncl . ac . uk/colonyzer/AddinallQFA/ | Telomeres , specialized structures at the end of linear chromosomes , ensure that chromosome ends are not mistakenly treated as DNA double-strand breaks . Defects in the telomere cap contribute to ageing and cancer . In yeast , defects in telomere capping proteins can cause telomeres to behave like double-strand breaks . To better understand the telomere and responses to capping failure , we have combined a systematic yeast gene deletion library with mutations affecting important yeast telomere capping proteins , Cdc13 or Yku70 . Quantitative Fitness Analysis ( QFA ) was used to accurately measure the fitness of thousands of different yeast strains containing telomere capping defects and additional deletion mutations . Interestingly , we find that many gene deletions suppress one type of telomere capping defect while enhancing another . Through QFA , we can begin to define the roles of different gene products in contributing to different aspects of the telomere cap . Strikingly , mutations in nonsense-mediated mRNA decay pathways , which degrade many RNA molecules , suppress the cdc13-1 defect while enhancing the yku70Δ defect . QFA is widely applicable and will be useful for understanding other aspects of yeast cell biology . | [
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] | 2011 | Quantitative Fitness Analysis Shows That NMD Proteins and Many Other Protein Complexes Suppress or Enhance Distinct Telomere Cap Defects |
In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies , and used these to study how enzyme functions have evolved . We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution . Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates , whilst the evolution of new chemistry is much less common . Despite that , by bringing together so much data , we can provide a comprehensive overview of the most common and rare types of changes in function . Our analysis demonstrates on a larger scale than previously studied , that modifications in overall chemistry still occur , with all possible changes at the primary level of the Enzyme Commission ( E . C . ) classification observed to a greater or lesser extent . The phylogenetic trees map out the evolutionary route taken within a superfamily , as well as all the possible changes within a superfamily . This has been used to generate a matrix of observed exchanges from one enzyme function to another , revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E . C . classes are more prevalent than others . Surprisingly a large proportion ( 71% ) of all known enzyme functions are performed by this relatively small set of 276 superfamilies . This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life .
Enzymes , as biological catalysts , are critical for life , with a significant proportion ( approximately 45% ) of gene products annotated as having an enzyme function [1] . Moreover , they are often the targets for pharmaceutical drug development , with a large number of approved drugs acting to modify the behaviour of enzymes implicated in human disease as well as disease causing pathogens [2] . Much of our understanding about how enzymes perform their reaction chemistry is derived from the study of their three-dimensional atomic structure . In combination with a variety of chemical and biochemical experiments it is possible to propose reaction mechanisms for many different enzymes [3] . An enzyme's function , and in particular the reaction chemistry it catalyses , is encapsulated by a hierarchical classification system developed and maintained by the Enzyme Commission ( E . C . ) [4] . It consists of a four-level descriptor , with the first three levels broadly categorising the overall chemistry and the fourth level being a serial number that is assigned to differentiate the substrate specificity . It is important to note that there is no correlation between the differences between the reactions catalysed and the numerical identifiers in the E . C . classification; so E . C . number 1 . 1 . 1 . 1 is no more similar to 1 . 1 . 1 . 2 than it is to 1 . 1 . 1 . 25 . In general , it is possible to organise and classify proteins into families and superfamilies based on similarities between sequence and/or structure . Very distant relationships between proteins can usually be more successfully detected through analysis of their three-dimensional atomic structures rather than by sequence alone [5] . To this end , a number of classifications of protein three-dimensional structure have been developed to capture evolutionary relationships , most notably CATH [6] and SCOP [7] . Both of these classifications use protein structural domains as the discrete entity , with a protein being made up of one domain or more in which case it is described as having a multi-domain architecture ( MDA ) . Domains often combine in multiple different ways creating different MDAs , often with different functions . Domains can be classified into superfamilies based on a detectable evolutionary relationship . A number of studies have been undertaken on collections of superfamilies whose membership predominantly consists of enzyme structures and sequences [8] , [9] , [10] , [11] , [12] , [13] , [14] as well as numerous studies on single superfamilies , in addition to the insights made as part of enzyme design and re-design efforts [15] , [16] , [17] , [18] . These analyses have observed that , whilst there is often conservation of some aspects of chemistry between relatives in enzyme superfamilies , there are examples of relatives which have diversified to perform very different functions ( as defined by the overall reaction they perform ) , and/or to use different chemical mechanisms ( the method by which the substrates transform ) , and/or act with different specificity . The route by which this functional diversity is achieved has proved to be complex . Changes to residues can subtly affect the binding of substrates , metal ions , or cofactors altering the chemistry performed . In some cases the recruitment or loss of domain partners can modulate the function [9] . All of these detailed studies have been undertaken manually on a relatively small number of superfamilies ranging in number from one to thirty . Understanding these evolutionary relationships is critical in the light of the continual flood of data from genomic projects , as it is often these insights that provide the best route for predicting function [19] . To address this challenge , we have developed FunTree [20] , a system for exploring the functional relationships and their evolution between three dimensional structure and function in enzymatic superfamilies ( http://www . ebi . ac . uk/thornton-srv/databases/FunTree/ ) . We apply the pipeline to analyse enzyme superfamilies in CATH , using robust structurally-informed multiple sequence alignments to build phylogenetic trees , which are then annotated with structural and functional data . Relationships between metabolites , obtained by exploiting tools for comparing small molecules , are displayed on the phylogenetic tree . We have chosen two specific superfamilies to illustrate the value of combining structural and functional data to explore evolutionary changes . Analyses of these functional changes in 276 well-defined enzyme superfamilies has allowed us to present a preliminary overview of the evolution of novel enzyme functions in order to begin to gather , catalogue and classify the emergence of the catalytic reactions necessary for life .
In order to understand the phylogenetic relationships and divergence of functions between protein domains , the first step is to identify related domains using both three dimensional data , based on CATH definitions , and sequence data . However , some of the structural superfamilies in CATH are highly diverse , containing very distant relatives with pairwise sequence similarities less than 10% . Whilst the core of the structural domain is generally well conserved , in some superfamilies distant relatives may exhibit different structural embellishments to this core [21] , [22] . In these cases it can be very difficult to align all the structural domain representatives of the superfamily robustly . Therefore , we have developed a protocol for identifying groups of structurally similar relatives within a superfamily that can be well aligned and superimposed in 3D . These are termed ‘structurally similar groups’ ( SSG ) . Sequence relatives were added to each SSG and then all sequences were multiply aligned and used to derive the phylogenetic tree for that SSG ( see Materials and Methods for full details ) . An established species tree [23] guided the phylogenetic tree and bootstrap values at the braches are shown . Modification of a domain's function can also be achieved by changing the multi-domain context i . e . by changing the domain partners [24] or through the duplication and diversification of domains [25] . To explore how the addition of domains can affect a given domain's function , a different sub-clustering of the superfamily was made based on the unique multi-domain architecture identified using ArchSchema [26] . Proteins that contain the superfamily domains in the same order are clustered together . These clusters are termed multi-domain architecture ( MDA ) groups . We used MACiE [27] , a manually curated database of enzyme mechanisms designed to provide a wide range of E . C . defined functions , to identify 276 enzymatic CATH superfamilies with adequate structural and functional data suitable for processing by the FunTree pipeline . The superfamilies included representatives from 189 different fold groups and all four CATH classes . For each superfamily and their SSG/MDA clusters , we generated a number of visualisations of the data . The principal visualisation is the sequence and structure based phylogenetic tree decorated with its associated annotations . The results of the small molecule clustering are rendered as a separate dendrogram and an overview of the functional variability is supplied as an un-rooted tree of the E . C . hierarchy . Additionally , at the superfamily level , we show the multi-domain architectures using the ArchSchema graphing software . A summary of the protocol is shown in Figure 1 . FunTree intimately links the chemical functions ( as defined by the reactions and the substrates catalysed ) of a superfamily of enzymes with their structures and evolutionary history . We use two superfamilies to illustrate how FunTree captures and describes changes in function . These two superfamilies exemplify two different paradigms of enzyme evolution . We then integrate the functional changes of all 276 superfamilies , giving for the first time an overview of our current knowledge of the scope and evolution of the ‘reactions of life’ as known today . The phosphatidylinositol ( PI ) phosphodiesterase superfamily ( CATH id 3 . 20 . 20 . 190 ) is relatively structurally conserved with all domain structure representatives in one structurally similar group ( SSG ) . There are only four different MDAs , with only one change in domain partner having a corresponding change in function ( see Figures 2 and 3 and Figure S1 ) . However , detailed structural analysis of the binding of the cognate ligand for this MDA reveals that the second domain does not have any direct molecular functional role ( see Figure S2 ) . Thus the single domain performs all the molecular functionality observed within this superfamily . The phylogenetic tree for this SSG has three distinct clades . The first clade ( C1 ) contains a variety of general and specific phosphodiesterases ( see Text S1 ) from bacteria and eukaryotes all performing a hydrolase reaction using a metal co-factor and the same catalytic machinery , although relatives use different substrates and generate different products . In the second clade ( C2 ) in the phylogenetic tree the enzymes become lyases , which are found in bacteria and protozoan trypanosomes rather than mammals , changing between a hydrolase ( E . C . 3 . 1 . 4 . 11 ) and a lyase ( E . C . 4 . 6 . 1 . 13/4 . 6 . 1 . 14 ) . The mammalian and bacterial phosphodiesterases all follow the same initial mechanism , the intramolecular addition of a hydroxyl group adjacent to the phosphate with elimination of the first alcohol substrate; however , for the bacterial enzymes in the second clade the metal cofactor is not present . This results in the cyclic intermediate leaving the active site prior to hydrolysis ( thus defining a lyase rather than a hydrolase ) whereas in the mammalian case , the intermediate is strongly bound and hydrolysis occurs within the enzyme . In both cases a pair of histidine residues act as general acid/base catalysts in the mechanism . The structure-informed sequence alignment reveals that none of the three metal chelating residues are conserved between these two clades . A fourth phosphodiesterase enzyme is also found in the third clade ( C3 ) but this acts on 2-lysophosphatidylcholine ( E . C . 3 . 1 . 4 . 41 ) and is involved in the generation of venom in Sicariid spiders [28] . It utilizes a very different substrate to the rest of the superfamily and is reported to have a markedly different mechanism [29] . Although the two histidine residues and the metal cofactor are still present , the histidines act in a nucleophilic manner forming a covalent bond between the phosphate and enzyme ( see Figure S3 ) . Other residues are less conserved ( see Figure S4 ) . Taken together , the data suggest that the changes in mechanism have occurred through modulation of existing residues rather than gain/loss of structural elements or loop regions . The outlying position of the clade in the phylogenetic tree , in combination with the available supporting literature catalogued by MACiE , clearly supports a mechanistic change for this grouping . It is not possible to determine the cause of this change in mechanism with currently available information . Analysis of changes in E . C . class , sub-class ( 2nd level ) and substrate specificity ( 4th level ) within a superfamily indicate transitions that have occurred since the protein diverged from its common ancestor . This superfamily has undergone a single transition between the hydrolase and lyase classes with no changes occurring at the sub-class level ( summary shown in Figure S5A ) , and lyases are only seen in bacteria and trypanosome protozoa . There has been a diversification in the substrates within the hydrolases , which are known to utilize five different substrates to date . No such diversification in substrate utilization is seen for the lyase performing enzymes within this superfamily . This superfamily provides an interesting example of relatives undertaking similar overall functions but with quite different mechanisms despite similarity in structures and identical catalytic residues in the same locations . It also illustrates a complex enzyme evolution that would have been difficult to predict from a simple examination of structures and active sites but has been revealed by bringing together a diverse range of information in FunTree . The Ntn-terminal type amide hydrolasing superfamily ( CATH id 3 . 60 . 20 . 10 ) is relatively structurally diverse , with three SSGs ( See Figure 4 and Figure S6 ) . SSG 1 contains just the amidophosphoribosyltransferase ( E . C . 2 . 4 . 2 . 14 ) . SSG 2 contains the glutamine-dependent asparagine synthetases ( E . C . 6 . 3 . 5 . 4 ) and the arginine beta-lactam-synthase ( E . C . 6 . 3 . 3 . 4 ) as well as glutamine-fructose-6-phosphate transaminases ( E . C . 2 . 6 . 1 . 16 ) . All use different substrates , though some are shared ( glutamate between E . C . 2 . 6 . 1 . 16 and E . C . 6 . 3 . 5 . 4 and ATP between E . C . 6 . 3 . 5 . 4 and E . C . 6 . 3 . 3 . 4 ) and have different MDAs . Some of the domain relatives in this superfamily form part of the proteasome , a large and important multi-subunit complex found in all major kingdoms that undertakes the vital function of eliminating proteins that are mis-folded , harmful or unnecessary [30] . In this context , the enzyme loses its preference for glutamine and acts generally to cleave the peptide bond ( E . C . 3 . 4 . 25 . 1/E . C . 3 . 4 . 25 . 2 ) , though the preference for glutamine has been observed with post-glutamyl peptidolytic activity using synthetic peptides [31] . All of the proteasome related structures and sequences are found in SSG 3 and are grouped with a proteasome-related protein-degradation machine HsIVU [32] . All consist of members with a single domain , though in vivo they are part of a large multi-subunit machine . A separate set of structures and sequences , which are singleton SSGs ( ie they contain only one structure in the SSG ) , are associated with glutamate synthesis ( E . C . 1 . 4 . 1 . 13 , 1 . 4 . 1 . 14 and 1 . 4 . 7 . 1 ) . All use glutamate but differ in the co-factor that they use which varies between NAD+ , NADP+ and ferredoxin . There is another singleton with a very different function ( E . C . 3 . 5 . 1 . 11 - penicillin amidase ) associated with it . Examination of the known structures and mechanisms reveals that this function is performed by one of the other domains in the enzyme and is also found in some multi-domain enzymes where the Ntn-terminal type amide hydrolasing domain is not found . This highlights the care needed when associating function with an enzyme with multiple domains . We performed the same analysis as for the previous superfamily cataloguing changes in E . C . numbers at various levels ( see Figure S5B ) . This is a more complicated superfamily , with transitions between four classes ( oxidoreductases , transferases , hydrolases and ligases ) . There are also changes within the transferase class at the sub-class level , indicating a change to the group that is being transferred . In addition there is diversification in substrate specificity within the oxidoreductases and hydrolases . The domains in these SSGs and the unclustered singletons are present in different domain combinations ( see Figure 4 ) . It can be seen from the ArchSchema graph ( see Figure 5 ) that changes in function correlate with changes in MDA ( i . e . domains with the same MDA have the same function ) . In fact structural differences between the domains are largely related to unstructured linker regions that are in close proximity to the domain partners and may be facilitating interactions with the domain partners . In SSG3 a helical embellishment to the domain core is involved in mediating contacts with protein partners in the biological unit . The correlation observed between MDAs and E . C . functions suggests that changes in the domain partners contribute to changes in the function . Analysis of mechanistic and structural domain partners ( details provided in Figures S7 and S8 and Text S1 ) reveals that the Ntn-terminal type amide hydrolasing domain primarily generates an amine ( generally ammonia ) from hydrolytic cleavage of the amide bond , which is then used by a second domain in a variety of ways . It is the combinations of domains that produce the range of functions observed within this superfamily . The 276 superfamilies processed in the current version of FunTree account for approximately 15% of all known domain assignments in CATH-Gene3D . The top 10% of superfamilies in our data ( <30 superfamilies ) ranked by number of SSGs account for 1 , 064 , 627 sequences ( 49% of sequences in our data ) with on average 5 SSGs per superfamily . The rest of the superfamilies have on average only one SSG per superfamily . Whilst the majority ( ∼75% ) of the superfamilies contain only one structurally similar group ( SSG ) , indicating that most superfamilies show limited structural divergence ( see Figure 6A ) a few superfamilies have a very large number of SSGs with the largest being the P-loop containing nucleotide triphosphate hydrolases with 27 SSGs . Although the structures show limited divergence , if the sequence diversity is measured using ScoreCons [33] , the majority of SSGs are highly diverse ( Figure 6C ) . Most superfamilies contain fewer than ten different multi-domain architectures ( Figure 6B ) and compared to the SSG alignments , the degree of sequence diversity within MDAs is relatively evenly spread with some being highly diverse and others very conserved ( Figure 6D ) . This accords with previous observations [34] . There is some correlation between the number of SSGs and MDAs ( Pearson correlation value of 0 . 77 ) . This is expected since structural modifications and decorations to the central core facilitate new interactions with domain partners , as in the Ntn-terminal type amide hydrolasing superfamily . However , the number of unique multi-domain architectures in each superfamily correlates poorly with the number of unique E . C . numbers ( Pearson correlation value of 0 . 57 ) . This indicates that , although in some families a domain partner brings an increase in functional diversity , surprisingly there are a number of families where most of the functionality is present in the single domain , for example the terpene synthases/cyclases and the phosphatidylinositol-phosphodiesterases . The distribution of the number of associated functions for each superfamily , as defined by the E . C . number and our general observations indicate that , although the majority of members of an enzymatic superfamily share a common function , some superfamilies have the ability to accommodate many diverse functions ( see Figures 6E and 6F ) . The top five ‘polymath’ enzyme superfamilies with multiple functions are: the NADH binding domain , P-loop containing nucleotide triphosphate hydrolases , Class 1 aldolases , S-adenosyl-L-methionine-dependent methyltransferases , trypsin-like serine proteases , and Type I PLP-dependent aspartate aminotransferase-like superfamily . All these have more than 50 functions as defined by E . C . to the 4th level in each superfamily . Although a significant number of our superfamilies have both general chemistry and substrate diversity present in the superfamily e . g . the NADH binding domain superfamily , most functional diversity comes from utilising multiple substrates with 177 ( 67% ) superfamilies where changes in E . C . occur only at level 4 i . e . change in substrate specificity . This builds upon our previous smaller scale studies [35] as well as observations made by Glasner and co-workers [12] and more recently by Khersonsky and co-workers [36] . E . C . numbers attributed to these 276 superfamilies ( including relatives where the domain is in different MDA contexts ) account for 71% of the 2 , 676 E . C . numbers assigned to known enzymes , with the E . C . numbers associated with single domain enzymes accounting for approximately 36% . The high coverage of enzyme functionality from just 276 superfamilies , given that this represents only 15% of known domains , is surprising . Moreover , just 45 superfamilies account for 50% , of all sequences assigned E . C . numbers with 31 superfamilies in which the single domain accounts for 25% . From this we can postulate that a limited repertoire of structural frameworks has evolved to carry out a large proportion of reactions required for all of life . Moreover , it is clear that generating new chemistry does not necessarily require large leaps , such as the evolution of novel protein structures or large structural re-arrangements , but can be made by small local changes e . g . residue substitutions or small insertions or deletions . Functional changes can also arise from changes in MDA and less frequently insertion/deletion of unstructured regions . This is perhaps not surprising since residue changes in the active site can easily induce changes in chemistry . Superfamilies supporting a wide range of enzyme functions predominantly adopt one of a few relatively highly populated superfamilies , such as the TIM barrel or Rossmann-like fold , which both possess large surface clefts likely to tolerate residue mutations [21] . We also observe that the addition of another domain or set of domains can bring a function associated solely with those domains and not with the superfamily domain ( see Figure S9 and S10 ) i . e . acquisition of function by domain addition . These domains can bring confusion as to where the function is originating and the role ( if any ) that the superfamily domain under scrutiny contributes to that function . The contribution of these additional domains to the functional repertoire of a superfamily has been taken into account . The major reason for this work was to explore the evolution of enzyme function; therefore we examined the range of E . C . classes found within each superfamily . We did this first by using the phylogenetic tree derived by FunTree to identify the evolutionary route taken within a superfamily to exchange the enzyme function from one reaction to another . It can be seen from Figure 7A that overall the exchanges within an E . C . class are proportionally the most abundant , while exchanges between classes are generally fewer . Using the evolutionary route we are able to determine the structural changes associated with the functional shifts , which can include modulation of functional residues or through the loss or gain of loop regions . For all changes in function in the phylogenetic tree found at a bifurcation with a single function represented on each side of the divide , we catalogued whether known catalytic site residues were aligned to gapped ( with a minimum gap space of three places ) region . The requirement for high quality annotations and single functional changes means that the total number of exchanges catalogued is small ( 1 , 107 compared to 3 , 335 exchanges catalogued in all trees ) . Only 5% of functional shifts are associated with the addition/deletion of loops and these functional changes split equally between changes in E . C . classes and within E . C . classes . In addition , we also catalogued where these exchanges corresponded to a change in the multi-domain architecture ( MDA ) . This showed that 27% of changes were associated with changes in the multi-domain architecture , although this is an upper estimate as it includes enzymes where acquisition of function is through the addition of a domain and not by changes in the domain under scrutiny . However , by counting E . C . exchanges using the FunTree derived phylogenetic trees E . C . exchanges occurring between SSGs will be missed ( see Figure S11A ) . In addition , some EC changes occurring more than once during evolution will be double counted . Whilst , this information may provide interesting insights as to which changes in chemistry have been more favoured during evolution , we were interested in understanding the full range of possible E . C . changes within a superfamily . Therefore , we have also explored E . C . exchanges by counting all possible changes within a superfamily . For example , if one member of a superfamily is a transferase ( E . C . 2 ) and another a hydrolase ( E . C . 3 ) , it is reasonable to assume that a direct transition between one class and another may have occurred at some point since the proteins diverged from a common ancestor . Therefore , all-by all counting ( see Figure 7B ) allows the possibility of changes within superfamilies that are not captured by known sequence relatives or that have been missed due to the necessity of building separate trees for different structural clusters in the superfamily . For example , for the exchanges occurring within the E . C . 1 class using the phylogenetic tree to count all exchanges misses 85 possible exchanges . By combining information from the 276 superfamilies we can generate an E . C . exchange matrix summing all possible changes within the superfamilies . This gives 2502 unique exchanges at all levels of the E . C . classification , with 354 exchanges ( approximately 15% of total exchanges observed ) in the primary E . C . level . In addition to counting all possible changes , the all-by-all counting permitted a random model of expected changes to be generated based on all the E . C . numbers present in the dataset , with pairs of E . C . numbers picked at random to generate the matrix . Comparison using a χ2 test of the two matrices shows they are significantly ( P-value <10−16 ) different . The most striking difference is that exchanges within a class ( along the matrix diagonal ) , are much more common than would be expected . These interchanges represent 85% of all changes observed , with most occurring in the first three classes as expected due to the higher number of divisions in E . C . 1 , E . C . 2 and E . C . 3 . If the exchanges within a class are removed from the matrices ( see Figure 7C ) we observe that , as expected , the number of divisions in the classification of each class heavily dominates the exchanges calculated in the random model . However , this distribution is not seen in the observed exchanges , with exchanges between the oxidoreductase , transferase , hydrolase classes occurring less than expected , while exchanges between the lyase , isomerase and ligase classes occurring more frequently than expected . In some cases the addition or removal of a step in the reaction changes the enzymes classification from one class to another , as exemplified in the phosphatidylinositol phosphodiesterase superfamily , exchanging from hydrolase to lyase . Some of the changes between classes reflect the structure of the E . C . classification . For example , there are some instances where an enzyme is classified as a lyase but includes hydrolysis as part of its mechanism . If the changes are considered for just the single domain enzymes ( see Figures S11B and S11C ) , though the absolute counts are less by approximately 68% , they are proportionally similar to those found across the whole superfamily . This reinforces the observations made earlier that changes in chemistry and specificity are often achieved within a single domain alone . Figure 7D , based on the exchanges reported in Figure 7B , shows the proportion in each superfamily of exchanges occurring at each level of the E . C . classification across all 276 superfamilies . Superfamilies show a variety of behaviours , with some only changing at the fourth level , whilst others ( and often smaller families ) are dominated by changes at the primary level . As the second or third level broadly represent the bond type or functional group , the lack of observed changes at this level indicates that changes within a superfamily to the bond type/functional group are much less likely than changes to overall chemistry or substrate specificity . The differences in the number of changes in the 1st level and 4th level of the E . C . number over the 2nd and 3rd levels is interesting , but difficult to interpret without detailed analysis of the chemistry of the reaction . This work is in progress . It clearly reflects the structure of the E . C . classification and needs deeper analysis . The overall distribution of total changes at each level of the E . C . classification ( see the insert to Figure 7D ) shows that changes at each of the first three levels are less likely than a change at the fourth level . Also , over all superfamilies , changes in the chemistry ( the combination of the first three E . C . levels ) are less likely than changes in substrate specificity ( the fourth level ) .
In this study we have benefited from exploring distant evolutionary relationships , captured by structural comparisons in the CATH classification . Whilst some extremely distant relatives cannot easily be aligned because of the degree of structural change during evolution , our analyses have exploited robust structural groupings within CATH superfamilies to identify general trends in the evolution of function in enzyme superfamilies . A caveat to our study relates to the problems in functional annotations in public databases , some of which are unreliable and some of which can be limited , for example by the lack of promiscuity data , which is rarely adequately explored . In addition , the E . C . classification system does not lend itself easily to providing an automatic means to quantitatively compare two reactions , since it does not capture the mechanism of the enzyme . A significant proportion of the reactions required for life are performed by a relatively small number of superfamilies so it can be postulated that a few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life , this considerably develops previous observations [37] . Using the phylogenetic trees to define the evolutionary route taken within a superfamily to change function , we were able to generate the E . C . change matrix . The large numbers of changes at the E . C . 4th level in the summary of E . C . changes in phylogentic trees compared to the low number of E . C . class changes indicates that changes in specificity occur mostly at the leaves of the trees , while more fundamental changes in chemistry occur at the root of the tree . Further work is required to ascertain when in evolution these changes occurred . Therefore a large amount of enzyme diversity occurs through evolution rather than de novo invention . Although , of course , new enzymes must have evolved at some stage , probably very early in the evolution of life . To identify the small number of ‘original’ enzyme progenitors requires more work and more experimental data . This study focuses on divergent evolution and does not consider cases of parallel evolution of enzyme function where two completely unrelated enzymes are able to catalyse the same reaction , sometimes by different mechanisms . Current analysis has shown that on average there are about two unrelated enzymes for each E . C number [38] . Previous studies have suggested some evidence for convergent evolution , and this needs further exploration . We found diversity of function within superfamilies at all levels of the enzyme classification , with changes between some E . C . classes occurring more frequently than others , though this in part reflects the human-devised nomenclature . There is also a large variation between individual superfamilies and SSGs/MDAs; some are highly diverse while others are almost mono-functional . Most seem to possess diversity at the 3rd level of E . C . or above , indicating a change in reaction chemistry as well as possessing diversity in the substrate metabolites . This can be driven by plasticity of the active site as well as the ability to recruit domain partners ( e . g . Ntn-type amide hydrolases superfamily ) . Our analysis has reinforced the observation that enzyme evolution is incredibly complex , with many different routes being taken to obtain different reactions , mechanisms and specificities within a superfamily . Such routes involve gene duplication followed by sub-functionalisation . The basis of such sub- functionalisation can be twofold: Firstly , by alteration of the enzyme structure , either by mutations , local insertions and/or deletions within a domain , or secondly by changes in multi-domain architecture as exemplified in the Ntn-terminal type amide hydrolasing superfamily . From a chemistry perspective , these structural changes can affect the overall reaction or the substrates , as exemplified in the phosphatidylinositol phosphodiesterase superfamily . The tools we have developed in FunTree bring together all the relevant data to help understand the molecular basis for each reaction change , but still require detailed inspection of enzyme mechanisms ( as captured in MACiE ) and three-dimensional structures to achieve a thorough understanding much has we have already done for the phosphatidylinositol phosphodiesterase and Ntn-type amide hydrolases superfamilies . These superfamilies provide exemplars of the type of analysis that is possible using the resource . The primary level of the E . C . classification can be summarised by simple chemical reactions ( see Figure S12 ) . We hoped it might be possible to understand the E . C . exchange matrix based on the simple reactions . However these overall reactions have many steps , with typically three to six steps and varying between one and sixteen steps . To understand and extract the paradigms that underlie , for example , a change from a lyase to transferase , we need to inspect all lyase-transferase exchanges to see if common routes exist . We can then ask what are the most common paradigms ? Knowing a reaction , can we predict which exchanges are most likely to occur ? Can we predict new substrates or new chemistries ? By beginning to gather , catalogue and classify the emergence of catalytic reactions we can analyse such shifts in functionality across and within enzyme superfamilies and this may help in designing new enzymes as well as aid in function prediction from sequence and structure .
Although domains in a superfamily share a common structural core , distant homologues can show considerable variation outside this core , making it hard to robustly superimpose all domains within some superfamilies . Therefore we identified structurally similar groups ( SSGs ) of non-redundant domains with greater than 35% sequence identity which could be superimposed with a root mean squared deviation of less than 9 Å . Multiple structure alignments were generated using CORA [39] . These alignments were used to generate a structure based sequence profile for the SSG using MELODY ( part of the FUGUE [40] fold recognition software ) . Sequence relatives for each CATH superfamily are provided by CATH-Gene3D and included only if part of the reviewed section of UniProtKB [41] . These are scanned against sequences of all the CATH superfamily domains of known structure using BLASTp [42] to determine which SSG they should be assigned to . They are then aligned to the structure-based sequenced profile of that SSG using FUGUEALI ( also part of the FUGUE software ) . These structure-based sequence alignments are used to perform the phylogenetic analysis . A domain is often part of a larger protein containing other domains that may be contributing to the protein's overall function , thus alignments of the entire protein sequence are also useful . We group together domains within a superfamily sharing the same domain partners and multi-domain architecture ( MDA ) . For each superfamily in the FunTree dataset , protein sequences having the same MDA are aligned . CATH-Gene3D defines the MDA of each protein by initially scanning the sequence against hidden Markov models built from CATH domains . Any unassigned sequence regions large enough to constitute a domain are checked against Pfam and if a non-overlapping Pfam domain is found then it is included in the MDA . Sequences with the same multi-domain architectures are clustered using ArchSchema [26] . The sequences of each cluster are then aligned using MAFFT [43] , and the alignment used to perform the phylogenetic analysis . Some superfamilies can have an extremely large number ( tens of thousands ) of associated sequences . This can lead to problems in both generating the alignments and calculating the phylogenetic trees , so the sequences are first filtered to reduce redundancy and numbers . If a family contains more than a few hundred sequences , the sequences are filtered by taxonomic lineage and uniqueness of function , retaining only unique representatives of each . The alignments from the SSGs and MDAs are used to generate a phylogenetic tree built with TreeBest ( as described in the methods for compiling the TreeFam database [44] ) . As this method incorporates species phylogenies to building gene trees , a species tree is generated using the NCBI taxonomic [23] definition of species relationships for those species found in the SSG/MDA . Sequence , structural and functional data is collected from public repositories . Comparisons of metabolites are undertaken and presented with the phylogenetic tree . As it is not always clear the contribution of individual domains in a MDA , a search is undertaken to remove sequences with MDAs that have ambiguity about the contribution of the superfamily domain to the novel function ( see Text S1 ) . | Enzymes , as biological catalysts , are crucial to life . Understanding how enzymes have evolved to perform the wide variety of reactions found across all kingdoms of life is fundamental to a broad range of biological studies , especially those leading to new therapeutics . To unravel the evolution of novel enzyme function requires combining information on protein structure , sequence , phylogeny and chemistry ( in terms of interacting small molecules and reaction mechanisms ) . We have developed a protocol for integrating this wide range of data , which we have applied to a relatively large number of families comprising some very diverse relatives . This has permitted us to present an initial overview of the evolution of novel enzyme functions , in which we observe that some changes in function between relatives are more common than others , with most of the functionality observed in nature confined to relatively few families . Moreover , we are able to identify the evolutionary route taken within a superfamily to change the enzyme function from one reaction to another . This information may help in predicting the function of an enzyme that has yet to be experimentally characterised as well as in designing new enzymes for industrial and medical purposes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"evolutionary",
"biology",
"proteins",
"biology",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] | 2012 | Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies |
Genome-wide association studies typically target inherited autosomal variants , but less studied genetic mechanisms can play a role in complex disease . Sex-linked variants aside , three genetic phenomena can induce differential risk in maternal versus paternal lineages of affected individuals: 1 . maternal effects , reflecting the maternal genome's influence on prenatal development; 2 . mitochondrial variants , which are inherited maternally; 3 . autosomal genes , whose effects depend on parent of origin . We algebraically show that small asymmetries in family histories of affected individuals may reflect much larger genetic risks acting via those mechanisms . We apply these ideas to a study of sisters of women with breast cancer . Among 5 , 091 distinct families of women reporting that exactly one grandmother had breast cancer , risk was skewed toward maternal grandmothers ( p<0 . 0001 ) , especially if the granddaughter was diagnosed between age 45 and 54 . Maternal genetic effects , mitochondrial variants , or variant genes with parent-of-origin effects may influence risk of perimenopausal breast cancer .
Genome-wide association studies ( GWASs ) that compare affected individuals and controls have identified many inherited genetic variants associated with complex diseases [1] . Nevertheless , effects of single nucleotide polymorphisms ( SNPs ) tend to be small [2] and much of the heritability for major diseases remains unexplained . For example , the most important GWAS-derived SNPs for breast cancer explain little of the risk [3] . Four other genetic mechanisms ( henceforth referred to as “nonstandard” ) are overlooked in a typical GWAS . Sex-linked genetic variants on the Y or the X chromosome are often not considered , although polymorphic X loci may be relevant to breast cancer [4] . The mother's genome can also exert effects on the developing fetus , of consequence for both birth outcomes and adult phenotypes [5] , [6] , [7] . Such maternally-mediated prenatal effects remain unexplored for breast cancer , though a prenatal influence on adult risk is suggested by the fact that birth weight is a risk factor [8] . A third mechanism involves variants in mitochondrial DNA , as reported for breast cancer in African-American women [9] . Finally , parent-of-origin effects ( e . g . due to imprinted polymorphic autosomal genes ) can also influence risk , as exemplified by a report based on Icelandic families [10] , where the effect of an allele related to breast cancer differed depending on whether its origin was maternal or paternal . Each of these nonstandard mechanisms produces asymmetry in family history data . We define inter-lineage asymmetry as the presence of a higher ( or lower ) risk either for the mother and her progenitors compared to the father and his progenitors , or for descendants of female cases compared to descendants of male cases . Although Table 1 includes sex-linked effects , we will not consider them further here . A maternally-mediated prenatal effect should produce increased risk in an affected individual's mother's ( but not father's ) progenitors , in a pattern where risk diminishes toward earlier generations . By contrast , a parent-of-origin effect could show a diminishing pattern of increased risk in the affected individual's father's progenitors if only the paternally inherited copy is expressed . The action of these understudied mechanisms can be discerned by studying family histories . The presence of disease in a proband statistically induces enrichment in their progenitors and progeny for risk-related alleles . For the mechanisms in Table 1 , that enrichment manifests as inter-lineage asymmetry . To quantify that asymmetry , we define several inter-lineage relative risks , whose exact definitions ( and magnitudes ) depend on the familial relationship to the affected proband of the individuals whose risks are being compared . We denote mother , father and child by M , F , and C , and extend the same notation for progenitors , e . g . , as MM for the mother's mother and MMF for the mother's mother's father . C will denote the grandchild when considering parents of parents . Let , , denote the events that the child , mother , father has the disease , respectively , with analogous notation for other relatives . Let , denote the events that a female ( girl ) , male ( boy ) in the population has the disease , respectively . We can compare risk in the proband's grandparental generation directly by comparing two sex-matched grandparents , either grandmothers ( MM vs . FM as ) or grandfathers ( MF vs . FF as ) , without normalizing to the population . By contrast , assessing asymmetry in risk between mothers and fathers of affected individuals requires normalizing those risks to risk for females versus males in the general population . Thus , the inter-lineage parent relative risk is the risk for the proband's mother compared to that for females in the population divided by the risk for the proband's father compared to that for males in the population , expressed symbolically as . Investigators have also looked prospectively for asymmetry , by comparing risk in the offspring of male versus female affected individuals [11] . The inter-lineage son ( daughter ) relative risk is the risk for sons ( daughters ) of affected mothers divided by the risk for sons ( daughters ) of affected fathers . Let and denote the events that a son or daughter , respectively , has the disease . We express the inter-lineage relative risk for sons as and that for daughters as . Epidemiologic studies sometimes assemble very large case-control samples or cohorts at elevated risk [12] , [13] and ascertain extensive family history data for affected families , enabling powerful comparisons of disease rates for maternal versus paternal lineages . Although studies related to birth defects have made use of multigenerational data [11] [14] , [15] , the huge consortia assembled for case-control and cohort studies of diseases like cancer [16] have thus far not probed for these less accessible genetic mechanisms . The NIEHS Sister Study enrolled 50 , 884 women who each had a sister diagnosed with breast cancer . We here use data from that cohort to compare rates of breast cancer in maternal versus paternal grandmothers . We develop general results to relate the inter-lineage relative risks in progenitors to the inter-lineage relative risks in descendants . Under simplifying assumptions , we calculate how large a maternally-mediated prenatal effect or a parent-of-origin effect would have to be to explain any particular inter-lineage asymmetry and conclude that those causative relative risks would have to be substantial to produce the inter-lineage asymmetry evident in the Sister Study .
We analyzed family history data for a large number of cases of breast cancer , to compare the risk of breast cancer in the maternal versus the paternal grandmother . The Sister Study [17] enrolled 50 , 884 women aged 35 to 74 in the United States and Puerto Rico between 2004 and 2009; each had a sister diagnosed with breast cancer . The unaffected sisters are being followed for newly incident breast cancer and other conditions . Participants completed a detailed family history questionnaire and were old enough that the data effectively encompass their grandmothers' lifetime risks . The Sister Study secured informed consent and was carried out with human-subjects approval and oversight from the NIEHS Institutional Review Board and the Copernicus Group Institutional Review Board . To assess asymmetry , we calculated odds ratios ( which approximate the relative risks for maternal versus paternal grandmothers ) using families where exactly one of the two grandmothers had breast cancer , i . e . , discordant grandmother pairs . Using algebra we then derived formulae to assess the likely strength of the mechanisms that could underlie an observed asymmetry in family history . Foreseeing applications beyond breast cancer , we similarly derived expressions to assess the degree of asymmetry that those nonstandard mechanisms would produce in maternal versus paternal progenitors , and the degree of asymmetry produced in the offspring of affected male versus female individuals .
Our analysis used 32 , 929 women , each from a distinct family , where each woman was the full sister of a case and could report breast cancer history for both grandmothers . Of these grandmothers , 3046 on the maternal side ( 9% ) and 2639 ( 8% ) on the paternal side had developed breast cancer . These reported rates are in general agreement with expected rates for their birth cohort [18] . However , presumably reflecting heritability , the probability that at least one of the two grandmothers had developed breast cancer decreased as a function of the youngest age at diagnosis of a sister in the participating family ( Figure 1 , Table 2 ) . Figure 2 shows the inter-lineage odds ratios ( which approximate the relative risks , ) for maternal versus paternal grandmothers , using families where exactly one of the two grandmothers had breast cancer , i . e . , discordant grandmother pairs . That is , we calculated the ratio of maternal to paternal grandmothers among the discordant pairs . The estimated overall odds ratio for a positive family history on the maternal versus paternal side was 1 . 18 ( 95% CI 1 . 11 , 1 . 24 , p<0 . 0001 ) . The inter-lineage odds ratio depended on the granddaughter's age at diagnosis ( Figure 2 ) , being most pronounced for cancers diagnosed in the age decade 45–54 , i . e . in the perimenopausal years , and much less pronounced for later-diagnosed or very young onset cancers . We next wanted to relate the magnitude of the excess in maternal grandmothers to possible nonstandard genetic mechanisms . Suppose a genetic variant carried by the mother has a prenatal effect on fetal development , hence on later risk to her offspring . We quantify the asymmetry induced by such a maternal effect ( Text S3 ) . Let the relative risk for the offspring of a mother with one ( two ) copies of the variant allele be relative to the risk for the offspring of a mother with no copies . For simplicity we consider an outcome where gender itself does not influence risk and take ( log-additive risk model ) . Then mothers of affected offspring are enriched for the risk allele and so are their mothers , that is the maternal grandmothers . Consequently , the mothers themselves have greater risk than fathers whenever ( Figure 3 ) . Note that both causative and protective maternal effects produce increased risk in the maternal lineage , reflecting the fact that whichever of the two alleles confers higher risk will tend to be over-represented in the maternal lineage of a proband . Also , note that a given small inter-lineage relative risk is induced by a more extreme maternally-mediated relative risk . This attenuation of seen in the inter-lineage parent relative risk reflects the fact that the mother of a case could have inherited the susceptibility allele that influenced her offspring's risk from either her mother or her father and in the event it came from her father it would not have affected her own risk . In general , we expect weaker asymmetry in the grandparental generation than in the parental generation . Assessment of risk to grandparents requires repeated use of the V matrix to calculate conditional genotype probabilities for their mothers , i . e . , the sampled proband case's great-grandmothers ( Text S3 , Table S2 ) . Because the relative risk for grandparents is a linear function of the relative risk for parents ( Text S4 ) , both sets of curves can be displayed in one figure with a simple scale change ( Figure 3 ) . Returning to breast cancer , we see that if the modest perimenopausal asymmetry we saw ( about 1 . 3 ) were due entirely to a maternally-acting SNP with a frequency of 10% and the effect obeyed a log-additive risk model , the relative risk for an offspring whose mother carries one copy of that SNP would be about 4 . 0 . Most diseases affect both males and females , so that , even if risk is maternally mediated , both grandmothers and grandfathers can contribute to the asymmetry analysis . In that event , the inter-lineage grandparental odds ratio ( relative risk ) is estimated by dividing the number of discordant pairs where the affected grandparent ( either grandmother or grandfather ) is on the maternal side by the number of discordant pairs where the affected grandparent is on the paternal side . If the condition is not rare so that some families contribute two discordant pairs , a within-cluster resampling approach [19] or generalized estimating equations approach [20] can accommodate family-based dependencies . Another plausible source of asymmetry in family history involves parent-of-origin effects such as genetic imprinting . Such an effect was reported for breast cancer by Kong , et al [10] based on Icelandic family data . For simplicity we consider a situation where only the maternally-inherited copy at a risk-related locus is expressed ( Table S2 ) . Suppose the relative risk is I for offspring who carry a maternally-inherited copy of the variant compared to a risk of in individuals who do not ( although for simplicity we assume and I are the same for both sexes , they could be sex-specific ) ( Text S5 , Table S3 ) . For a parent-of-origin effect , here based on imprinting , maternal grandmothers of affected children show greater risk than paternal grandmothers whenever ( Figure 3 ) . Again the same set of curves can serve , as there is a simple scale change involved in moving from asymmetry induced by log-additive maternal effects to asymmetry induced by a parent-of-origin effect ( Text S6 ) . The observed grandparental relative risk of approximately 1 . 2 could reflect a polymorphic imprinted gene if the risk associated with a maternally-inherited allele ( I ) is 5 . Variants in mitochondrial DNA ( mtDNA ) can also produce asymmetry in families . A recent report found such a variant to be related to breast cancer risk in African-American women [9] . Since each person inherits virtually all their mitochondria from their mother , the asymmetry produced by this genetic mode of effect could show little or no diminution across generations of females , unless the mitochondria become heteroplasmic . The chain of mitochondrial inheritance is broken , however , by males: risk for the mother's father would on average return to the population risk because his mitochondria came from a separate maternal line . Returning to breast cancer , for a mitochondrial effect to explain the observed asymmetry it would have to confer about the same relative risk as that seen in the grandmothers , i . e . , on the order of 1 . 2–1 . 3 .
While case-control GWASs have revealed many SNPs related to susceptibility to complex diseases , nonstandard genetic mechanisms may also play a role . We considered three such mechanisms that can produce asymmetry in family histories: maternal genetic effects that influence risk via the prenatal environment; parent-of-origin effects , for example where the expression of an imprinted polymorphic gene variant depends on parental source; and effects of variants in the mitochondrial DNA , which are exclusively inherited from mothers . Our algebraic quantification of the relationship between inter-lineage asymmetry and its driving cause led us to conclude that , although the driving effect would be small if due to a mitochondrial variant , if a single variant acted through maternally-mediated prenatal effects or was subject to parent-of-origin effects , then that cause would be associated with a large relative risk , at least on the order of 4 or 5 , for breast cancer . Although a single-allele scenario seems unlikely , we wondered whether a single allele acting through a maternally-mediated genetic mechanism could explain the known increased risk seen in sisters . Under our simplifying homogeneity assumptions , one can show with a little added algebra that a single , log-additive maternal effect with a single-copy relative risk of 4 . 0 involving an allele with frequency about 0 . 07 would produce about the observed two-fold increased risk for sisters of cases . Although we have focused our methods and analysis on progenitors and descendants , siblings , aunts and uncles would also be informative . A prenatal maternal effect and a parent-of-origin effect where only the maternal allele is expressed share an interesting feature: A half sibling of a case would have risk similar to that of a full sibling if the shared parent is the mother , but no increased risk if the shared parent is the father . A Danish study reported that pattern of asymmetry in half-brothers of cases with the birth defect cryptorchidism [21] . Under a maternally-mediated prenatal effect , because siblings share the same mother , the relative risk for the siblings of a mother with an affected child versus siblings of that child's father should be greater than 1 . Thus , one could glean even more insight by comparing histories of maternal versus paternal blood-relative aunts and uncles , in addition to parents . Several small studies have compared rates of breast cancer in maternal and paternal relatives . A registry-based Swedish study found no difference in maternal and paternal grandmothers but included fewer than a thousand breast cancer cases [22] . Two studies of healthy adults found an excess of cancer reported for female relatives [23] , [24] , but presumably more fathers than mothers were estranged and participants were not asked if they knew about particular relatives' disease histories . In the Sister Study , out of 44 , 307 families 1 , 843 reported about their paternal grandmother but not their maternal grandmother , while 4 , 895 reported about their maternal grandmother but not their paternal grandmother , suggesting that knowledge about maternal versus paternal grandmothers is differential . Unlike other studies , however , we restricted our analysis to families where the status of both was reported to minimize information bias . Although our sample is large , with almost 33 , 000 families represented , cases reported among sisters and grandmothers were not generally validated clinically . Nevertheless , our participants are sisters of women with breast cancer; and they have proven themselves an informed and dedicated cohort , providing bio-samples , completing lengthy questionnaires and maintaining commitment to follow-up with a very low dropout rate . Moreover , though the study staff did not obtain medical records for cases whose sister enrolled in the Sister Study , we did request medical records for 1422 affected sisters who joined our family-based add-on “Two Sister Study” [25] . Their diagnosis of breast cancer was confirmed by medical records for all but 3 ( who had lobular carcinoma in situ ) of 1251 . We made some simplifying assumptions ( HWE , Mendelian transmission , random mating , rare disease , effect of only a single locus ) and thus our figures depict idealized settings , which are not fully appropriate for the Sister Study . There are also effects secondary to ascertainment . The Sister Study is more likely to enroll unaffected women from larger families who have lower genetic risks . Consider two families , each with a single daughter with breast cancer and suppose one family has 10 daughters and the second has only two daughters . The first family is more likely to be in our study because any one of the 9 unaffected daughters can join , but the same large family has demonstrated lower genetic risk with only one of 10 affected , compared to the second family with 1 of 2 affected . The Sister Study consequently would have sampled families with less genetic enrichment for the allele under study than the two-fold increase presumed . Because this ascertainment effect should distort the inter-lineage asymmetry toward the null , the estimate of 4 for a maternally-mediated prenatal effect may be too low . We implicitly assumed that the reported father is the biological father , but reported paternity can be incorrect . However , out of a subset of 602 families in the Two Sister Study where DNA was acquired , only 5 fathers failed the paternity test . Moreover , the observed strong pattern where asymmetry was related to the age at diagnosis of the granddaughter could not be explained by misidentified paternity . Another issue is that in a large series of cases such as the Sister Study , some affected sisters may unknowingly have been adopted; however , we expect that proportion to be small . Also , unaware adoptees would report the wrong history on both the maternal and the paternal side , driving estimates toward symmetry . Reporting bias and self-selection may be at work . One might expect a woman with both a sister and a mother with breast cancer to be more likely to join the Sister Study than a woman with only an affected sister . Also , women whose mother had breast cancer may undergo more regular screening . However , the rate of breast cancer reported for mothers was about 18% , which does not exceed expectation based on having a first degree relative with breast cancer . With combined data including as many as 45 , 000 cases , large consortial efforts are underway [16] to study the genetics of cancers and other complex diseases . We believe that important clues could be elicited by also studying asymmetries in the reported family histories for those cases . Although we saw evidence for inter-lineage asymmetry based on the family histories from the Sister Study participants , with more breast cancer in the maternal lineage , one cannot differentiate among the three nonstandard mechanisms using only phenotypic family histories of affected individuals . Only family-based genotype data will enable an investigator to identify the genetic mechanism and identify relevant variants [26] , [27] . In summary , susceptibility to complex disease can be influenced by inherited autosomal gene variants , as most GWASs assume , but can also be influenced by sex-linked genes , maternally-mediated prenatal effects , parent-of-origin effects , and mitochondrial variants . These under-studied genetic mechanisms are best explored through family studies . Yet even without access to genetic data on family members , evidence that those phenomena play a role can be adduced through careful analyses of family history data from large assemblages of cases . Breast cancer appears to be subject to genetic mechanisms that produce family history asymmetry , particularly when diagnosed in the perimenopausal years . | Genetic studies often collect family histories from diagnosed individuals . Some diseases exhibit inter-lineage asymmetry: mothers and their progenitors have higher ( or lower ) risk than fathers and their progenitors , and descendants of female cases have higher ( or lower ) risk than descendants of male cases . We describe how certain non-standard genetic mechanisms might underlie that asymmetry and make substantial contributions to disease susceptibility . Besides variants on sex chromosomes , these mechanisms include variants in the mother's genome that influence fetal development and hence later risk , variants in the mitochondria that modulate risk , and susceptibility variants in particular inherited genes whose expression depends on whether the variant came from the mother or the father . Applying our ideas to a study of more than 30 , 000 families with breast cancer , we found that more maternal grandmothers of cases than paternal grandmothers of cases had breast cancer , giving evidence that such non-standard mechanisms may be important contributors to breast cancer risk . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"oncology",
"medicine",
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"cancer",
"epidemiology",
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"risk",
"factors",
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"biology",
"biology",
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] | 2014 | Asymmetry in Family History Implicates Nonstandard Genetic Mechanisms: Application to the Genetics of Breast Cancer |
Alpha-hemolysin ( α-HL ) is a self-assembling , channel-forming toxin produced by most Staphylococcus aureus strains as a 33 . 2-kDa soluble monomer . Upon binding to a susceptible cell membrane , the monomer self-assembles to form a 232 . 4-kDa heptamer that ultimately causes host cell lysis and death . Consequently , α-HL plays a significant role in the pathogenesis of S . aureus infections , such as pneumonia , mastitis , keratitis and arthritis . In this paper , experimental studies show that oroxylin A ( ORO ) , a natural compound without anti-S . aureus activity , can inhibit the hemolytic activity of α-HL . Molecular dynamics simulations , free energy calculations , and mutagenesis assays were performed to understand the formation of the α-HL-ORO complex . This combined approach revealed that the catalytic mechanism of inhibition involves the direct binding of ORO to α-HL , which blocks the conformational transition of the critical “Loop” region of the α-HL protein thereby inhibiting its hemolytic activity . This mechanism was confirmed by experimental data obtained from a deoxycholate-induced oligomerization assay . It was also found that , in a co-culture system with S . aureus and human alveolar epithelial ( A549 ) cells , ORO could protect against α-HL-mediated injury . These findings indicate that ORO hinders the lytic activity of α-HL through a novel mechanism , which should facilitate the design of new and more effective antibacterial agents against S . aureus .
Staphylococcus aureus is an opportunistic pathogen in humans and other mammals that causes many different types of infections , including superficial abscesses , septic arthritis , osteomyelitis , pneumonia , endocarditis , and sepsis [1] , [2] . The number of virulence factors secreted by S . aureus , including extracellular and cell wall-related proteins , determines its pathogenicity [3] . The virulence factor α-hemolysin ( α-HL ) is one of the most important factors produced by the majority of S . aureus strains and recent studies have demonstrated that it plays a major role in S . aureus pneumonia [4] . Previous studies using a mouse model of S . aureus pneumonia have shown that S . aureus strains that lack the hla gene ( and thus do not secrete α-HL ) cause less lung injury and inflammation than the hla positive strains [5] . The α-HL protein , isolated from the gram-positive pathogenic bacterium S . aureus , is a well-studied model that has been used to elucidate mechanisms of membrane insertion by soluble proteins . Studies have shown that α-HL can self-assemble on the lipid bilayers of the membranes of susceptible host cells to form a wide heptameric pore [6] . The protein is toxic for a wide range of mammalian cells , particularly erythrocytes and epithelial cells and serves primarily as a tool that converts host tissue into nutrients for any bacteria that expresses it [3] . In an effort to increase our understanding of the function of α-HL , the structure of the heptameric pore was resolved by X-ray crystallography to a resolution of 0 . 19 nm [6] . Contained within the mushroom-shaped homo-oligomeric heptamer is a 10 nm long solvent-filled channel that runs along the seven-fold axis and ranges from 1 . 4 nm to 4 . 6 nm in diameter . The lytic transmembrane domain forms the lower half of a 14-strand antiparallel β barrel , to which each protomer contributes two 6 . 5 nm long β strands . Considering the essential nature of the heptameric crystal structure , Ragle et al . used a modified β-cyclodextrin compound , IB201 , to prevent the α-HL-induced lysis of human alveolar epithelial cells ( A549 ) [7] . This protective effect does not result from the ability of β-cyclodextrin to impair formation of the oligomeric α-HL on the cell surface , supporting a role for this molecule in the blockade of the lytic pore . Previous investigations had demonstrated the use of unsubstituted β-cyclodextrin as an adapter molecule that is capable of lodging within the central pore of α-HL and can thus facilitate the use of the toxin as a biosensor [8] , [9] . The investigation of β-cyclodextrin using IB201 revealed that it blocks ion conductance through the assembled hemolysin pore , which supports the finding that β-cyclodextrin inserts into the pore itself . Although the inhibitory effect of β-cyclodextrin on ion conductance and red blood cell hemolysis were both observed in the low micromolar concentration range , this treatment strategy is passive . It is clear that prior to inhibition by β-cyclodextrin , the oligomeric α-HL on the cell surface has been formed and the cell has been damaged . Therefore , further research to identify new potent inhibitors is essential . In our previous study , we reported that baicalin ( BAI ) , a natural compound could bind with α-HL directly and inhibit the hemolytic activity of by restraining the conformation change of the binding cavity , “triangle region” ( residues 147–153 ) [10] . In this study , we found that another natural compound , oroxylin A ( ORO ) could inhibit the hemolytic activity of α-HL stronger . Surprisingly , based on molecular dynamics simulations and free energy calculations , a new mechanism of inhibition was obtained compared with baicalin ( BAI ) , which is that ORO bind to new active sites ( residues Thr12 and Ile14 ) of α-HL and inhibit the hemolytic activity of α-HL due to the binding of ORO to the critical “Loop” region of α-HL . All these results indicate that the “triangle region” ( residues 147–153 ) is not the only active site of inhibitor bound with α-HL and “Loop” region of α-HL also plays an important role in the inhibition of hemolytic activity of α-HL . With these approaches , we identified that ORO , which binds to the active site ( Thr11 , Thr12 , Ile14 , Gly15 and Lys46 ) of α-HL , is a potent inhibitor of the α-HL self-assembly process . These results could provide useful in the design of novel drugs for α-HL .
The studies that were performed to determine the minimal inhibitory concentration ( MIC ) showed that the maximum concentration of ORO tested was not able to inhibit the growth of S . aureus , which indicates that ORO has no antimicrobial activity against S . aureus . Our previous study showed that many natural compounds could inhibit the hemolytic activity of the culture supernatant of S . aureus by decreasing the expression of α-HL [11] . In the present study , we found that ORO cannot affect the production of α-HL in S . aureus ( Figure 1 ) . However , ORO attenuated the hemolytic activity of purified α-HL in a concentration-dependent fashion ( Figure 2B and 2C ) . Consequently , it is reasonable to deduce that ORO has a direct effect on α-HL . Based on the previous result , which showed that ORO inhibited the hemolytic activity of α-HL , we studied the binding of ORO to α-HL via molecular docking and molecular dynamics simulations using the AutoDock 4 . 0 and Gromacs 4 . 5 . 1 software packages , respectively . The initial structure of the monomeric α-HL was obtained from homology modeling , as previously reported [12] . The complex structure based on the docking results was used as the initial structure of the 200-ns molecular dynamics simulations and the preferential binding mode of ORO to α-HL was determined . The simulations show that ORO is a ligand that can bind to α-HL via hydrogen bonding and van der Waal interaction . Over the time course of the simulation , ORO localizes to the “Loop” region of α-HL , which is reported to participate in crucial protomer-protomer interactions during α-HL self-assembly and is therefore important in heptamer formation and cell lysis [6] , [13] . The predicted binding mode of ORO with α-HL is illustrated in Figure 3A and the electrostatic potentials of the residues around the binding site are mapped , as shown in Figure 3B , using APBS software [14] . In detail , the binding model of ORO to the Loop of α-HL ( Figure 3B ) revealed that the methyl group of the 4H-chromen-4-one moiety of ORO formed a hydrogen bond with the side chain of the Lys46 amino acid in α-HL . As shown in Figure 4 , the number of hydrogen bonds fluctuates mostly between 1 and 2 throughout the simulation time , which indicates that ORO and α-HL are always interacting via a hydrogen bond . Moreover , the neutral side chains of the Thr11 , Thr12 , and Ile14 residues of α-HL form Van der Waals interactions with ORO , as shown in Figure 3B . Thr11 and Thr12 anchor the benzene ring of ORO , and Ile14 and Gly15 play an important role in stabilizing the 4H-chromen-4-one moiety of ORO . In addition , the methoxy of the 4H-chromen-4-one moiety forms strong interactions with Ser16 and Lys46 , which will be confirmed by energy decomposition analysis . The root mean square fluctuation ( RMSF ) of the residues surrounding the ORO binding site of α-HL ( residues 1–50 ) in the α-HL-ORO complex and in free α-HL were calculated to illustrate the flexibility of these residues . The RMSF of these residues are shown in Figure 5 and clearly depict the difference in the flexibility of the binding site of α-HL due to the presence or absence of ORO . All of the residues in the α-HL binding site that is bound with ORO show a smaller degree of flexibility , with RMSF values less than 0 . 3 nm , when compared with the RMSF values calculated for the free α-HL , which indicates that these residues become more rigid after binding to ORO . These results indicate that the stabilization of the α-HL binding cavity in this complex is mostly due to residues Thr11 , Thr12 , Ile14 , Gly15 and Lys46 , as shown in Figure 3B . The MD results provide an approximate binding mode of the protein-ligand interaction of the α-HL-ORO complex . However , the contribution of the residues surrounding the binding site of α-HL is not clear . Therefore , the electrostatic , Van der Waals , solvation and total contribution of the residues to the binding free energy were calculated using the Molecular Mechanics Generalized Born Surface Area ( MM-GBSA ) method [15] , [16] . The calculation was performed over the 200 MD snapshots obtained from the last 50-ns simulation . The summations of the interaction free energies for each residue were separated into Van der Waals ( ΔEvdw ) , electrostatic ( ΔEele ) , solvation ( ΔEsol ) , and total contribution ( ΔEtotal ) . The energy contributions from the all residues of α-HL are summarized in Figure 6A . As shown , Lys46 has an appreciable electrostatic ( ΔEele ) contribution , with a value less than −2 . 3 kcal/mol ( Figure 6A and 6B ) . Because Lys46 is close to the methoxy of ORO and an electrostatic interaction exists , strong H-bonds are formed between α-HL and ORO . In addition , residues Thr12 ( with a ΔEvdw of −1 . 2 kcal/mol ) and Ile14 ( with a ΔEvdw of ≤−2 . 1 ) exhibit strong Van der Waals interactions with the ligand because of the close proximity between these two residues and the 4H-chromen-4-one moiety of ORO . The majority of the decomposed energy interaction , with the exception of the energy associated with Lys46 , originates from Van der Waals interactions . The electrostatic contribution from these key residues does not appear to have a significant influence on the formation of the α-HL-ORO complex . The total binding free energy of the α-HL-ORO complex , ΔGbind , and its detailed energy contributions , which were calculated using the MM-GBSA approach , are summarized in Table 1 . The ΔGbind can be divided into polar ( ΔGele , sol+ΔEele ) and nonpolar ( ΔGnonpolar+ΔEvdw ) energies . As shown , the primary contributor to the free energy of the binding of ORO to α-HL is ΔGnonpolar+ΔEvdw , with a value of −11 . 2 kcal/mol , whereas ΔGele , sol+ΔEele have a minor contribution of −6 . 4 kcal/mol . This difference arises from the intermolecular Van der Waals energy , which is mainly achieved from the ORO-binding α-HL residues . After summation of the solute entropy term ( 5 . 1 kcal/mol ) , an estimated ΔGbind of −12 . 5 kcal/mol was found , which suggests that ORO can strongly bind to and interact with the binding site of α-HL . The same procedure was followed with two α-HL mutants , T12A-HL and I14A-HL , to verify the accuracy of the binding site in the α-HL-ORO complex . A complex of each mutant with ORO was used as the preliminary structure for the MD simulations and the MD trajectories were analyzed using the MM-GBSA method . In addition , the T12A-HL and I14A-HL mutants were expressed and purified; the binding free energy and the number of binding sites between ORO and the two mutants were then determined using the fluorescence spectroscopy quenching method [17] , [18] . As illustrated in Figure 6D and 6F , ORO differentially binds to the two mutants and the WT-HL , an observation that was confirmed by pair interaction decomposition analysis of the free energy , as shown in Figure 6C and 6E . The major contributions to the free energy of the complexes of ORO with the α-HL mutants were residues Thr115 , Tyr118 , Pro103 , Phe120 and Ile142 . Furthermore , as shown in Table 1 , the MM-GBSA calculation predicted that T12A-HL and I14A-HL bound more weakly to ORO than WT-HL , with estimated ΔGbind values of −3 . 8 kcal/mol and −5 . 2 kcal/mol , respectively . Consequently , the calculations for T12A-HL and I14A-HL show that these mutants exhibited a decrease in the binding energy of approximately 7 to 8 kcal/mol compared to WT-HL . The experimental measurement of the binding free energy , shows that the interaction between ORO and WT-HL is highest , which means that WT-HL has the strongest ability to bind to ORO; the mutants exhibits the weaker ability , as shown in Table 2 . Importantly , because the calculated binding free energies are in good agreement with the experimental data shown in Table 2 , we believe that the MD simulations generated a reliable model of the α-HL-ORO complex . ORO inhibits the hemolytic activity of α-HL and binds to the “Loop” cavity of α-HL , which has been shown to be critical to its hemolytic activity [6] , [19] . Consequently , the conformations of the “Loop” region in the α-HL-ORO complex and in free α-HL were investigated using a MD simulated trajectory . As shown in Figure 7A , the distance between the Cα of Thr12 and the Cα of Thr19 in the complex ranged from 0 . 9 to 1 . 15 nm over the time course of the simulation , with an average distance of 1 . 05 nm ( Figure 7A and 7B ) . In the absence of ORO , the distance between these points ranges from 1 . 15 to 1 . 3 nm , with an average distance of 1 . 24 nm . However , the distance between the Cα of Thr12 and the Cα of Thr19 is 1 . 81 nm in the crystal structure of the α-HL heptamer , which is available in the Protein Data Bank under the accession number 7AHL . Dynamic fluctuations in the distance between the Cα of Thr12 and the Cα of Thr19 likely indicate that the conformation of the “Loop” region is restrained when ORO binds to these two residues . Through comparing the structure of the loop in α-HL-ORO complex with the crystal structure of α-HL monomeric observed in the X-ray structure of the oligomer ( PDB code: 7AHL ) , it is indicated that ORO blocks the required conformational transition of the loop by binding to the loop , which is the mechanism of decreasing the lytic activity of α-HL . The RMSD values also reflect the variation in the conformation change of the “Loop” region during the simulation time . As shown in Figure 8 , the RMSD values of the “Loop” region in free α-HL are ∼0 . 4 nm; these values are clearly higher than that of the α-HL-ORO complex , which is in the range of 0 . 323 nm . In addition , as shown by the RMSD trends displayed in Figure 8 , the RMSD data agreed with other types of measurements , reinforcing that the WT-HL and ORO complex displays very little variation in the conformation of the “Loop” during the MD simulation . Furthermore , significant differences were observed in the dynamics of the critical “Loop” region during the MD simulations of the wild type and free α-HL , which confirms the effect of inhibition of α-HL on cell lysis . As illustrated in Figure 9 , our models predict a marked conformational transition in the “Loop” region for the wild type complex to the free α-HL . A comparative analysis of these MD simulations suggests that the inhibitory activity of ORO is highest for the wild type α-HL , followed by the T12A mutant and then I14A . This conclusion , which is also supported by the mechanism of hemolytic inhibition , is in good agreement with experimental results . Data from a deoxycholate-induced oligomerization assay shows that the site-directed mutagenesis of T12A and I14A has no influence on the assembly of the SDS-stable oligomer , α-HL7 . However , the formation of α-HL7 was inhibited when treated with 8 µg/ml of ORO . This inhibitory effect was decreased with either of the two mutants , as shown in Figure 10 , with I14A showing a higher inhibition than T12A . These findings support one possible inhibition mechanism: the binding of ORO to the “Loop” region blocks the conformational change , which inhibits the self-assembly of the heptameric transmembrane pore , thereby decreasing the lytic activity of α-HL . Human alveolar epithelial ( A549 ) cells have previously been employed to investigate the influence of S . aureus on lung cell injury and α-HL has been found to be the major factor associated with their injury and death [7] . Because our previous results show that ORO blocks the self-assembly of α-HL , we speculated that ORO may protect A549 cells from S . aureus-mediated death . Consequently , A549 cells were stained with a live/dead ( green/red ) reagent following co-culture with S . aureus 8325-4 . The uninfected cells displayed the green fluorophore ( Figure 11A ) , indicating their live status . A549 cells were significantly affected by their co-culture with S . aureus , as reflected by the increase in the amount of red fluorophore observed ( Figure 11B ) . However , the addition of 8 µg/ml of ORO resulted in a significantly lower number of dead cells ( Figure 11C ) . Consistent with a previous study , treatment of A549 cells with the S . aureus strain DU1090 , which cannot produce α-HL , does not result in their death ( Figure 11D ) [11] . Furthermore , a lactate dehydrogenase ( LDH ) release assay was employed to quantitatively assess the influence of ORO on the protection of A549 cell injury and , as shown in Figure 11E , the addition of 1 to 8 µg/ml of ORO affords a dose-dependent protection . The 50% inhibitory concentration ( IC50 ) , which was calculated using OriginPro 8 . 0 ( OriginLab , USA ) , was 3 . 09 µg/ml . These results highlight the potential therapeutic effect of ORO , which merits further investigation .
Historically , vancomycin and linezolid have been the recommended empirical and definitive therapies for the treatment of methicillin-resistant S . aureus pneumonia . However , the emergence of multi-drug-resistant S . aureus , such as vancomycin-resistant S . aureus , makes S . aureus infection difficult to treat and increases its mortality rate [20] . Due to our increasing understanding of bacterial pathogenesis and intercellular cell signaling , several potential strategies have been developed for drug discovery , of which the anti-virulence strategy has the interest of most researchers [21] . S . aureus can secrete numerous surface proteins and exotoxins , which are involved in the process of pathopoiesis [22] . One of the most important is α-HL , which often leads to tissue damage . Our previous research has also shown that several natural compounds can protect mice against S . aureus pneumonia by decreasing the production of α-HL [11] . Based on the results of all these studies , we theorized that α-HL can be used as a target for the development of new drugs against S . aureus . The increasing interest in drug design based on the identification of novel virulence targets has created a demand for the structural characterization of protein-ligand complexes . X-ray crystallography is a traditional tool used to investigate the interaction of ligands and proteins in a complex , and many studies using this technique have been reported . In 1996 , Song et al . discovered the crystal structure of the α-HL heptamer; however , to date , the monomeric structure of α-HL remains unknown , suggesting that crystals of the α-HL monomer may be very difficult to obtain . Thus , it seems impractical to explore the structure of the ORO-α-HL monomer complex by crystallography . In the literature , computational chemistry combined with experimental confirmation has proven an effective and reliable method for exploring the interactions between ligands and proteins . Accordingly , in this study , we used a complementary approach that includes molecular dynamics simulations ( MD simulations ) , site-specific mutagenesis , and a fluorescence-quenching method to further explore ligand-protein binding sites . Specifically , we attempted to identify the mechanism by which ORO inhibits the biological activity of α-HL . Moreover , to explore the formation of the interaction between a protein and a ligand at the atomic level , we used the MM-GBSA method to determine the associated free energy profiles . It has been known for years , and was restated recently [23] , that free energy calculations from MD simulations can prove to be a powerful tool for exploring the process of ligand-protein binding when used in combination with mutagenesis experiments [24]–[27] . In addition , MM-GBSA calculations were performed on a series of derivatives of TIBO ( a substituted tetrahydroimidazole benzodiazepine thione ) to explore their potential as inhibitors of HIV-1 reverse transcriptase [28] . In the same study , the binding mode of a known drug was predicted with excellent agreement to the X-ray structure , which was discovered afterward . Other examples of studies with MD simulations include work by Biswa Ranjan Meher et al . [29] , who examined the binding of the inhibitor darunavir to wild-type and mutant proteins using all-atom MD simulations and MM-GBSA calculations , and work by Lstyastono et al . [30] , whose study focused on the elucidation of molecular determinants of G protein-coupled receptor-ligand binding modes by combining MD simulations and site-directed mutagenesis studies . There are a number of other such examples of studies that employ MD simulations [31]–[36] . In this study , we discovered that Van der Waals interactions play an important role in the stabilization of the binding site of α-HL-ORO . The key residues , Thr11 , Thr12 , Ile14 , Gly15 and Lys46 , in the complex were also identified , using residue decomposition analysis and mutagenesis assays . The conformational transition of the critical “Loop” region from the monomeric α-HL to the oligomer was blocked by the binding of ORO , which resulted in the inhibition of the hemolytic activity of α-HL . These findings indicate that ORO hinders the lytic activity of α-HL through a novel mechanism . This was confirmed by inducing the formation of the α-HL heptamer by Deoxycholate , the results of which indicate that addition of ORO inhibits the formation of the α-HL heptamer . In summary , we found that oroxylin A ( ORO ) , a natural compound without anti-S . aureus activity , can inhibit the hemolytic activity of α-HL . Based on the results of MD simulation , we confirmed that ORO could inhibit the hemolytic activity of α-HL by a new mechanism which is completely different compared with baicalin ( BAI ) . Through the analysis of the binding free energy of the complex formation using MM-GBSA method , the results show that the residues Thr11 , Thr12 , Ile14 , Gly15 and Lys46 , which surround the binding site of the α-HL-ORO complex , are key ORO-binding residues . Due to the binding of ORO , the conformation transition of the critical “Loop” region from the monomeric α-HL to the oligomer was blocked , which resulted in inhibition of the hemolytic activity of the protein . This novel mechanism was confirmed by experimental data using a deoxycholate-induced oligomerization assay . The whole results mentioned above indicate that the “triangle region” of α-HL is not the only active site of inhibitor and “Loop” region of α-HL also plays an important role in the inhibition of hemolytic activity of α-HL , which could facilitate the design of new and more effective antibacterial agents .
S . aureus 8325-4 , a high-level α-HL-producing strain , and its cognate α-HL-deficient mutant , DU 1090 , were used in this study . The S . aureus strains 8325-4 and DU 1090 were cultured in TSB to an optical density of 0 . 5 at 600 nm . Then , either the cultures were centrifuged and resuspended in DMEM medium for the live/dead and cytotoxicity assays . ORO ( purity>98 . 5% ) ( Figure 2A ) was obtained from Sigma-Aldrich ( St . Louis , MO , USA ) , and the stock solutions were prepared in dimethyl sulfoxide ( DMSO ) ( Sigma-Aldrich ) . For the in vivo studies , ORO was dissolved in sterile PBS . The minimum inhibitory concentrations ( MICs ) of ORO for S . aureus were evaluated using the broth microdilution method according to the Clinical and Laboratory Standards Institute ( CLSI ) guidelines . Hemolytic activity was measured as described elsewhere using rabbit red blood cells [7] . In brief , 100 µl of purified α-HL was pre-incubated in 96-well microtiter plates in the presence of either gradient concentrations of ORO or PBS control at 37°C for 10 min . Defibrinated rabbit red blood cells ( 100 µl; 5×106 cells per milliliter ) in PBS were then added to the wells and the mixtures were incubated at 37°C for 20 min using 1% Triton X-100 as a positive control . After centrifugation , the supernatants were removed and their absorption at 543 nm was measured . The percent hemolysis was calculated using the supernatant reading from an equivalent number of cells that had been lysed in 1% Triton X-100 . Western blot analysis was performed as previously described [37] . Briefly , S . aureus 8325-4 and DU 1090 were cultured at 37°C in TSB and different concentrations of ORO to an optical density at 600 nm of 2 . 5 . The cultures were collected by centrifugation and the supernatants were used in sodium dodecyl sulfate ( SDS ) -polyacrylamide ( 12% ) gel electrophoresis . The proteins were then transferred onto polyvinylidene fluoride membranes ( Roche , Basel , Swiss ) using a semi-dry transfer cell ( Bio-Rad , Munich , Germany ) . After blocking the membrane for 2 h with 5% Bovine Serum Albumin ( BSA ) ( Amresco , USA ) at room temperature , an anti-hemolysin primary polyclonal antibody ( Sigma-Aldrich ) was added at a 1 ∶ 5000 dilution . The membrane was then incubated overnight at 4°C and then for 2 h with a HRP-conjugated secondary goat anti-rabbit antiserum ( Sigma-Aldrich ) that was diluted to 1 ∶ 4000 . The blots were developed using Amersham ECL western blotting detection reagents ( GE Healthcare , UK ) . For rational drug discovery , modeling and informatics play an indispensable role in the identification of lead compounds and their most plausible mechanisms of action against particular biological targets [38] . Therefore , we have performed a homology-modeling study on α-HL . To date , the structure of monomeric α-HL is unavailable and only the crystal structure of the α-HL heptamer has been reported [6] . The model of the monomeric α-HL in solution was proposed based on homology modeling , as previously reported [12] . MODELLER [3] , version 9 . 9 , was used to generate structural models of α-HL based on the template structures of LukF ( PDB codes 1LKF_A ) , LukF-PV ( PDB code 1PVL_A ) , Gamma-hemolysin component A ( PDB code 2QK7_A ) and LukS-PV ( PDB code 1T5R_A ) . As a whole , the sequence identities between the templates Lukf-PV , LukF , Gamma-hemolysin component A , LukS-PV and the query monomeric α-HL are 30% , 31% , 26% , and 22% . The most important difference between template and query is located in “loop” region ( residues 1–50 ) , which is the critical region for the assembled hemolysin pore . The program optimizes the structure of the homology models by minimizing a global probability density function that integrates the stereochemical parameters and homology-derived restraints [39] . The best model was selected based on its DOPE score , and it was subjected to further 200 ns molecular dynamics using Gromacs 4 . 5 . 1 software package [40] . The geometry of ORO was optimized at the B3LYP/6-31G* level using the Gaussian 03 program [41] . The initial structure of α-HL was obtained from the homology modeling . To obtain the starting structure of the drug/α-HL complex for molecular dynamics ( MD ) simulation , a standard docking procedure for a rigid protein and a flexible ligand was performed with AutoDock 4 [42] , [43] . The Lamarckian genetic algorithm ( LGA ) was applied in the docking calculations . All of the torsional bonds of the drug were free to rotate while α-HL was held rigid . Then , the polar hydrogen atoms were added for α-HL using the AutoDock tools , and Kollman united atom partial charges [44] were assigned . A total of 150 independent runs were carried out with a maximum of energy evaluations to 25 , 000 , 000 and a population size to 300 . A grid box ( 50×40×49 ) with spacing of 0 . 1 nm was created and centered on the mass center of the ligand . Energy grid maps for all possible ligand atom types were generated using Autogrid 4 before performing the docking . The clusters were ranked according to the lowest energy representative in each cluster . Then , the ligand docking poses suggesting preferential binding to the loop region are three: Pose 1 , Pose 2 , and Pose 3 . Pose 1 has the lowest energy conformation ( −6 . 5 kcal/mol ) and the most populated cluster ( 28 ) compared with Pose 2 ( −5 . 6 kcal/mol , 15 ) and Pose 3 ( −4 . 9 kcal/mol , 7 ) , and then Pose 1 was chosen for further study . The lowest energy conformation in the most populated cluster was chosen for further study [45] . All of the simulations and the analysis of the trajectories were performed with Gromacs 4 . 5 . 1 software package using the Amber ff99sb force field and the TIP3P water model [40] , [46] . The α-HL-ORO system was first energy relaxed with 2000 steps of steepest-descent energy minimization followed by another 2000 steps of conjugate-gradient energy minimization . The system was then equilibrated by a 500 ps of MD run with position restraints on the protein and ligand to allow for relaxation of the solvent molecules . The first equilibration run was followed by a 200 ns MD run without position restraints on the solute . The first 20 ns of the trajectory were not used in the subsequent analysis to minimize convergence artifacts . The equilibration of the trajectory was checked by monitoring the equilibration of quantities , such as the root-mean-square deviation ( RMSD ) with respect to the initial structure , the internal protein energy , and fluctuations calculated for different time intervals . The electrostatic term was described with the particle mesh Ewald algorithm . The LINCS [47] algorithm was used to constrain all bond lengths . For the water molecules , the SETTLE algorithm [48] was used . A dielectric permittivity , ε = 1 , and a time step of 2 fs were used . All atoms were given an initial velocity obtained from a Maxwellian distribution at the desired initial temperature of 300 K . The density of the system was adjusted during the first equilibration runs at NPT condition by weak coupling to a bath of constant pressure ( P0 = 1 bar , coupling time τP = 0 . 5 ps ) [49] . In all simulations , the temperature was maintained close to the intended values by weak coupling to an external temperature bath with a coupling constant of 0 . 1 ps . The proteins and the rest of the system were coupled separately to the temperature bath . The structural cluster analysis was carried out using the method described by Daura and co-workers with a cutoff of 0 . 25 nm [48] . The ORO parameters were estimated with the antechamber programs [49] and AM1-BCC partial atomic charges from the Amber suite of programs [50] . Analysis of the trajectories was performed using PyMOL analysis tools and Gromacs analysis tools . In this work , the binding free energies are calculated using MM-GBSA approach supplied with Amber 10 package . We choose a total number of 200 snapshots evenly from the last 50 ns on the MD trajectory with an interval of 10 ps . The MM-GBSA method can be conceptually summarized as: ( 1 ) ( 2 ) where ΔH of the system is composed of the enthalpy changes in the gas phase upon complex formation ( ΔEMM ) and the solvated free energy contribution ( ΔGsol ) , while −TΔS refers to the entropy contribution to the binding . Eq . ( 2 ) can be then approximated as shown in Eq . ( 3 ) : ( 3 ) where ΔEMM is the summation of the van der Waals ( ΔEvdw ) and the electrostatic ( ΔEele ) interaction energies . ( 4 ) In addition , ΔGsol , which denotes the solvation free energy , can be computed as the summation of an electrostatic component ( ΔGele , sol ) and a nonpolar component ( ΔGnonpolar , sol ) , as shown in Eq . ( 5 ) : ( 5 ) The interactions between ORO and the all residues of α-HL are analyzed using the MM-GBSA decomposition process applied in the MM-GBSA module in Amber 10 . The binding interaction of each ligand-residue pair includes three terms: the Van der Waals contribution ( ΔEvdw ) , the electrostatic contribution ( ΔEele ) , and the solvation contribution ( ΔEsol ) . All energy components are calculated using the same snapshots as the free energy calculation . The binding constants ( KA of ORO to the binding site on WT-HL , T12A-HL and I14A-HL were measured using the fluorescence-quenching method , and the binding constants were converted to the binding energy by Eq . ΔGbind = RTlnKA . Fluorescence spectrofluorimetry measurements were carried out using a Horiba Jobin-Yvon Fluorolog 3–221 spectrofluorometer ( Horiba Jobin-Yvon , Edison , NJ ) . The measurements were acquired using a 280-nm excitation wavelength with a 5-nm band-pass and a 345-nm emission wavelength with a 10-nm band-pass . Details of the measurements were described previously [51]–[53] . Oligomerization assay was performed as described previously [54] , 200 ng WT-HL , T12A-HL or I14A-HL monomers was mixed with 5 mM deoxycholate separately , following the addition of ORO , the mixtures were incubated at 22°C for 20 min . Then 5× loading buffer without β-mercaptoethanol was added to the mixtures and incubated at 50°C for 10 min . 25 µl of each reaction mixture was loaded onto 12% sodium dodecyl sulfate ( SDS ) -polyacrylamide gel electrophoresis ( PAGE ) gels for electrophoresis . Gels were stained using the silver PlusOne staining kit ( GE Healthcare ) according to the manufacturer's instruction . Human lung epithelial cells ( A549 ) were obtained from the American Tissue Culture Collection ( ATCC CCL 185 ) and cultured in Dulbecco's modified Eagle's medium ( DMEM ) ( Invitrogen , CA , USA ) supplemented 10% fetal bovine serum ( Invitrogen ) . Cells were seeded in 96-well dishes at a density of approximately 2×105 cells each well . As described previously [55] , A549 cells were incubated with 100 µl of staphylococcal suspension with the addition of different concentrations of ORO or positive control PBS for 6 h at 37°C , DU1090 suspension was used as negative control . Cell viability was determined either using live/dead ( green/red ) reagent ( Invitrogen ) or by measuring lactate dehydrogenase ( LDH ) release using a Cytotoxicity Detection kit ( LDH ) ( Roche ) according to the manufacturer's directions . Microscopic images of stained cells were obtained using a confocal laser scanning microscope ( Nikon , Japan ) . LDH activity was measured on a microplate reader ( TECAN , Austria ) . The significance of hemolysis , LDH release assay results were determined using the two-tailed Student's t test . Differences were considered statistically significant when P<0 . 05 . S . aureus hla gene: NC_007795 . 1 | The mechanism controlling protein-ligand interactions is one of the most important processes in rational drug design . X-ray crystallography is a traditional tool used to investigate the interaction of ligands and proteins in a complex . However , protein crystallography is inefficient , and the development of crystal technology and research remains unequally distributed . Thus , it seems impractical to explore the structure of the α-hemolysin-ORO monomer complex by crystallography . Therefore , we used molecular dynamics simulations to investigate the receptor-ligand interaction in the α-HL-ORO monomer complex . In this study , we found that oroxylin A ( ORO ) , a natural compound with little anti-S . aureus activity , can inhibit the hemolytic activity of α-HL at low concentrations . Through molecular docking and molecular dynamics simulations , we determined the potential binding mode of the protein-ligand interaction . The data revealed that ORO directly binds to α-HL , an interaction that blacks the conformational transition of the critical “Loop” region in α-HL and thus prevents the formation of the α-HL heptameric transmembrane pore , which ultimately inhibits the hemolytic activity of α-HL . This mechanism was confirmed by experimental data . Furthermore , we demonstrated that ORO could protect against α-HL-mediated injury in human alveolar epithelial ( A549 ) cells . | [
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] | 2013 | Oroxylin A Inhibits Hemolysis via Hindering the Self-Assembly of α-Hemolysin Heptameric Transmembrane Pore |
Bax/Bak-mediated mitochondrial outer membrane permeabilization ( MOMP ) is essential for “intrinsic” apoptotic cell death . Published studies used synthetic liposomes to reveal an intrinsic pore-forming activity of Bax , but it is unclear how other mitochondrial outer membrane ( MOM ) proteins might facilitate this function . We carefully analyzed the kinetics of Bax-mediated pore formation in isolated MOMs , with some unexpected results . Native MOMs were more sensitive than liposomes to added Bax , and MOMs displayed a lag phase not observed with liposomes . Heat-labile MOM proteins were required for this enhanced response . A two-tiered mathematical model closely fit the kinetic data: first , Bax activation promotes the assembly of a multimeric complex , which then catalyzes the second reaction , Bax-dependent pore formation . Bax insertion occurred immediately upon Bax addition , prior to the end of the lag phase . Permeabilization kinetics were affected in a reciprocal manner by [cBid] and [Bax] , confirming the “hit-and-run” hypothesis of cBid-induced direct Bax activation . Surprisingly , MOMP rate constants were linearly related to [Bax] , implying that Bax acts non-cooperatively . Thus , the oligomeric catalyst is distinct from Bax . Moreover , contrary to common assumption , pore formation kinetics depend on Bax monomers , not oligomers . Catalyst formation exhibited a sharp transition in activation energy at ∼28°C , suggesting a role for membrane lipid packing . Furthermore , catalyst formation was strongly inhibited by chemical antagonists of the yeast mitochondrial fission protein , Dnm1 . However , the mammalian ortholog , Drp1 , was undetectable in mitochondrial outer membranes . Moreover , ATP and GTP were dispensable for MOMP . Thus , the data argue that oligomerization of a catalyst protein , distinct from Bax and Drp1 , facilitates MOMP , possibly through a membrane-remodeling event .
Mitochondria are well known to be essential for cell life , as they produce ATP and other products of key biosynthetic pathways . Intriguingly , mitochondria are also often critical for cell death [1]–[4] . In vertebrates , apoptotic cell death typically involves a canonical “intrinsic” apoptotic pathway that depends on mitochondrial outer membrane permeabilization ( MOMP ) . MOMP is induced by the pro-apoptotic Bcl-2 family proteins Bax and/or Bak [5]–[12] . During MOMP , supramolecular pores are formed that are permeable even to large proteins . These pores lead to cell death in two ways: first , they allow proteins normally residing in the mitochondrial intermembrane space to be released into the cytoplasm , where these proteins then activate or enhance caspase-dependent death pathways . In particular , cytochrome c and Smac/DIABLO promote Apaf-1-dependent activation of Caspase-9 and the “executioner” caspases-3 , -6 , and -7 , leading to apoptosis [13]–[18] . Second , even when this Apaf-1-dependent pathway is blocked , outer membrane pores lead to cell death by initiating a slow but progressive loss of mitochondrial function . As a result , cellular energy stockpiles become depleted and DNA replication slows to a halt , within two cell division cycles [19] . Normally , this disruption of mitochondrial bioenergetic function leads to an absolute loss of clonogenic survival . However , when the protein GAPDH is overexpressed and caspases are inhibited , some cells manage to survive and proliferate . To do so , they must first maintain energy by boosting glycolysis and autophagy . Then , they must restore the mitochondrial network by biogenesis , starting from a small remnant of mitochondria that evade MOMP [4] , [20] . The precise molecular mechanism of MOMP is not yet understood . Earlier , we showed that the process of Bax/Bak-dependent MOMP that occurs in apoptotic cells could be reproduced in vitro using resealed dextran-loaded mitochondrial outer membrane vesicles ( OMVs ) [9] , [21] . This demonstrates that the core MOMP machinery is intrinsic to the outer membrane and does not require components from the intermembrane space , the inner membrane , or the mitochondrial matrix . ( Of course , the ability of isolated OMVs to undergo MOMP does not imply that interior mitochondrial structures are irrelevant to apoptosis . For example , crista junctions can influence cell fate by controlling the retention of proteins such as cytochrome c and Smac within cristae [22]–[25] . ) Previous studies from others and ourselves have modeled Bax-dependent membrane pore formation with simple in vitro systems consisting of protein-free liposomes mixed with recombinant Bax and “direct activator” BH3-only proteins—for example , cBid and BimS ( e . g . , [9] , [21] , [26] , [27] ) . However , it has been unclear whether liposome systems accurately reflect the physiological events in mitochondria . Here , to help elucidate the mechanisms of MOMP in native mitochondrial membranes , we undertook a thorough biochemical kinetic analysis of Bax-dependent MOMP using isolated outer membranes . Our data show that the native membranes display a more sensitive and kinetically more complex response to Bax than that observed with liposomes . Heat-labile MOM proteins are required for this enhanced response . Thus , although liposome systems have revealed an intrinsic pore-forming function of Bax , they do not fully reproduce the permeabilization process in apoptotic mitochondria . Our kinetic studies confirmed the existence of an outer membrane-resident “receptor” for cBid [28] , [29] . Moreover , our studies revealed a reciprocal relationship between the effective concentrations of cBid and Bax . This is evidence for a transient collision interaction between cBid and Bax and is clear experimental support for the “hit-and-run” hypothesis of cBid-induced direct Bax activation . Importantly , we developed a mathematical model to explain the observed kinetics of OMV permeabilization . This , in combination with biochemical studies , revealed some unexpected aspects of Bax-dependent MOMP . In particular , we showed that , during the lag phase , activated Bax triggers the multimerization of a catalyst molecule that facilitates another Bax-dependent event , the formation of large membrane pores . However , our data show that the catalyst is distinct from Bax , and contrary to what is often assumed , Bax oligomerization is unrelated to the kinetics of MOMP . Furthermore , Bax insertion and membrane recruitment were temporally early events , beginning just after the addition of Bax . The catalyst assembly reaction displayed phase transition-like behavior , raising the possibility that it involves a membrane-remodeling event . Based on recent studies [30] , [31] , a prime candidate for the catalyst was the fission-related protein Drp1 . Considering this , we tested the effect of compounds ( analogs of mdivi-1 ) that were originally identified as mitochondrial fission inhibitors in yeast . These compounds inhibit the GTPase activity of Dnm1 , the yeast ortholog of Drp1 . Although the active mdivi-1 analogs do not affect recombinant Drp1's GTPase activity , they do inhibit mitochondrial fission in mammalian cells and MOMP in isolated vertebrate mitochondria [31] . Strikingly , we found that mdivi-1 analogs inhibited the formation of the MOMP-related catalyst . However , because Drp1 was undetectable in mitochondrial outer membranes , and because GTP and ATP were not required for pore formation , our data argue that a MOM-resident protein distinct from Drp1 participates in catalyst assembly and serves as a target of chemical Dnm1 inhibitors . We propose that this catalyst activity promotes pore formation , by facilitating the redistribution of Bax in the MOM or by directly enhancing the pore-formation function of Bax .
To study the kinetics of MOMP , we continuously measured the release of OMV-entrapped fluorescent dextrans ( either Cascade Blue- or fluorescein-labeled ) , using specific antibodies that are excluded from intact OMVs but quench the fluorescence of released fluorophores . This quenching was rapid , as evidenced by the near-instantaneous drop in the fluorescence signal upon membrane permeabilization by Triton X-100 ( Figure 2A ) . Rat liver OMVs , like whole rat liver mitochondria , responded to neither cBid nor Bax proteins alone , even if cBid was added at high concentrations ( Figure S1A ) . As expected , OMVs became permeabilized by cBid and Bax added together ( Figure 2A ) , and the anti-apoptotic Bcl-xL protein abrogated this response ( Figure 2B ) . Consistent with earlier results , dextran release from Xenopus OMVs was induced by cBid alone and more potently by mixtures of cBid and Bax ( Figure S1B ) . Interestingly , both in rat liver and Xenopus OMVs , the kinetics were biphasic , with a pronounced early lag phase . Bax mixed with either recombinant BimS protein ( Figure S6C ) , or Bim BH3-domain peptide ( unpublished data ) produced similar biphasic kinetics . These complex MOMP kinetics were strikingly different from the monophasic kinetics of cBid/Bax-induced dextran release from protein-free liposomes ( Figure 2D ) . Both synthetic liposomes ( Figure 2D ) and liposomes formed from extracted mitochondrial phospholipids ( Figure S1D ) displayed monophasic permeabilization kinetics , similar to ones observed in other studies [27] , [37]–[39] . Moreover , OMVs , despite showing an initial lag , were substantially more responsive than liposomes to lower concentrations of Bax ( Figure 2C , D and Figure S1 ) . Thus , liposomes only partially reproduce the process of Bax-induced pore formation in native membranes . To determine whether the kinetics of dextran release from OMVs faithfully reflect those of MOMP in mitochondria , we developed a continuous kinetic assay for whole mitochondria that measures the ability of exogenous reduced cytochrome c to traverse the outer membrane [40] , [41] . Here , normal mitochondrial respiration was blocked by myxothiazol ( a Complex III inhibitor ) to prevent interference with MOMP assessment . Complex IV-dependent respiration driven by oxidation of external NADH was sustained by the continuous reduction of exogenous cytochrome c via the flavoprotein ( Fp5 ) -cytochrome b5 reductase complex [42] , [43] . Under control conditions , the rate of respiration was very low because exogenous cytochrome c cannot pass through the intact outer mitochondrial membrane . However , the addition of cBid and Bax increased the respiration rate , after a lag phase , and Bcl-xL inhibited this response ( Figures 2E and S1E ) . Thus , whole mitochondria and OMVs displayed very similar biphasic kinetics of cBid/Bax-induced permeabilization . From these studies , we conclude that the native MOM has a mechanism that potentiates Bax-mediated pore formation , after a certain lag . We considered the possibility that this lag was caused by a requirement for the accumulation or maturation of a factor within the membranes . However , we found that preincubation of the membranes ( with or without cBid ) prior to the addition of Bax did not eliminate the lag phase ( unpublished data ) . In other words , the lag phase does not represent a constitutive or cBid-induced process in the MOM , but is initiated by Bax ( upon activation ) . Another possible explanation for the lag phase could have been a time-dependent de-repression of Bax inhibition by endogenous anti-apoptotic Bcl-2-family proteins . To test this possibility , we preincubated OMVs with two BH3 peptides ( Bad and Noxa ) that together inhibit most of the anti-apoptotic proteins [21] or with ABT-737 , a chemical inhibitor of several anti-apoptotic proteins , including Bcl-xL [44] . The peptides only slightly accelerated cBid/Bax-induced dextran release ( Figure S2A ) , possibly reflecting the weak ability of Noxa to activate Bax directly [45] . ABT-737 also had no effect unless recombinant Bcl-xL was added to inhibit MOMP ( Figure S2B ) . These data indicate that endogenous anti-apoptotic Bcl-2-family proteins are not highly active in rat liver OMVs . An additional experiment performed with liposomes showed that when cBid/Bax-induced permeabilization was partially inhibited by Bcl-xL , the subsequent addition of an increased amount of cBid reversed the inhibition almost immediately ( Figure S2C ) . Thus , displacement interactions among Bcl-2-family proteins are rapid and unlikely to explain the lag phase . In liposomes , Bax integration into the membrane is relatively slow compared with tBid-Bax interaction and pore formation and thus apparently rate-limiting [26] . Because permeabilization kinetics were more complex in native mitochondrial membranes , we asked whether Bax integration was similarly rate-limiting in OMVs , or whether another step was responsible for the lag phase . For continuous measurements of Bax insertion in OMVs , we adapted a fluorometric method [26] that takes advantage of the increased fluorescence of Bax labeled with NBD [N , N′-dimethyl-N- ( iodoacetyl ) -N′- ( 7-nitrobenz-2-oxa-1 , 3-diazol-4yl ) ethylenediamine] that occurs upon membrane insertion . We could thus compare the kinetics of Bax integration ( increase in NBD fluorescence ) and MOMP ( loss of dextran-Cascade Blue fluorescence ) . As shown in Figure 3 , Bax began to integrate into the membrane almost immediately upon its addition in the presence of cBid , whereas dextran release began only after a 2–8 min lag . As expected , both the fluorescence increase and dextran release induced by Bax-NBD were prevented by Bcl-xL by cBid omission ( Figure 3A , B ) . To examine the kinetics of Bax association with membranes in another way , we re-isolated OMVs at various times after Bax addition , using a density gradient float-up method , and then measured Bax levels by immunoblotting [21] . The results show that cBid recruits Bax to OMVs well before the lag phase is completed ( Figure S3 ) . Thus , two independent methods demonstrate that Bax is recruited to outer membranes very early in the MOMP process . We conclude that the lag phase reflects another time-dependent event occurring in the native MOM , after Bax membrane insertion . We considered the possibility that this second event was merely the accumulation of a global threshold amount of integrated Bax . However , if that were true , we would expect a similar global buildup of Bax to be required in liposomes . But as liposomes exhibited no delay in pore formation following Bax insertion , that explanation is unlikely . To help explain the mechanism of MOMP , we developed a biochemical reaction model that fit the observed kinetic data ( see Materials and Methods ) . To enable this analysis we made the simplifying assumption that each vesicle releases all of its dextran content at once . ( Flow cytometric analysis of individual OMVs revealed that OMVs do display this mode of near-instantaneous dextran release; Kuwana et al . , unpublished ) . Importantly , this assumption implies that the normalized fluorescence of the OMV suspension , corresponding to the fraction of dextran molecules that remain entrapped in vesicles , also equals the fraction of intact vesicles ( which is what we model mathematically ) . Also implicit in this assumption is the idea that the formation of the first pore in a given OMV is sufficient to allow complete dextran release; this is consistent with observations made by cryo-electron microscopy that activated Bax can cause the formation of large openings in liposomes [28] . Importantly , we observed that dextrans of widely different sizes showed similar release kinetics ( Figure S4 ) . As we assumed that the vesicles all behave in a binary fashion ( either intact or permeabilized ) , it followed that , for the purposes of mathematical modeling , we could regard the OMVs to be mathematically equivalent to molecular entities , which also exist in binary states ( reacted or unreacted ) . We could therefore use a traditional enzyme kinetics approach to model the vesicle population . Although the vesicles individually behaved as binary entities , as an ensemble they became permeabilized in a non-synchronous ( quasi-stochastic ) manner , yielding an exponential-like dextran-release curve following the lag phase . Consistent with this , we found that Bcl-xL added at various times during the rapid phase prevented almost all subsequent dextran release ( Figure 2B ) . In other words , Bcl-xL preserved the integrity of most of the vesicles that had yet to be permeabilized . To explain the observed kinetics , we explored various basic reaction schemes ( Figure 4A ) . The simplest of these did not match the data , but a somewhat more complex reaction scheme yielded essentially a perfect curve-fit . This scheme involves two coupled reactions , where the first tier ( reaction I ) generates a catalyst for the second tier ( reaction II; Figure 4 and Materials and Methods ) . In reaction I , the catalyst is assembled from monomer subunits ( M ) that must first be activated ( to M* ) and then undergo multimerization ( to M*n ) . Reaction II is simply the conversion of an intact vesicle to a permeabilized vesicle , via the formation of a supramolecular pore . For each set of experimental conditions , we obtained a curve-fit of the equation in Figure 4A ( “Catalyst Assembly” ) to the time-course of continuous fluorometric measurements . This yielded values of two kinetic constants: k1 for the rate of catalyst assembly , which essentially characterizes the duration of the initial lag phase , and k2 for the rate of pore formation , which corresponds to the maximum slope of the rapid kinetic phase . The third variable parameter in the curve-fits is n , which corresponds to the average number of subunits assembled in the catalyst complex and is manifested in the sharpness of the transition between the two kinetic phases . Note that as n approaches 1 , the “Catalyst Assembly” model collapses into “Catalyst Activation . ” In general , we found that for reasonably good curve-fits , n had to be at least ∼12 . However , the theoretical curves were not especially sensitive to changes in n at such magnitudes , and therefore , we could not measure this parameter precisely . Figure 4A ( bottom ) shows examples with n = 4 ( a poor fit ) and n = 24 ( essentially a perfect fit to the experimental data ) . Figure 4B shows how this kinetic model can be accommodated into a molecular scheme by incorporating our data showing that the entire kinetic response , including the lag phase , was initiated by cBid or BimS-induced Bax activation , which is widely hypothesized to occur through a “hit-and-run” mechanism . Based on the extensive literature correlating Bax oligomerization with MOMP , the monomer M in our reaction scheme ( Figure 4 ) could have corresponded to Bax . To test this , we analyzed the dependence of the rate constants k1 and k2 on Bax concentration in our OMV system ( Figure 5A ) . Surprisingly , both kinetic constants displayed a nearly linear Bax-dose dependence over a wide range of Bax concentrations ( including the physiological range of 200–600 nM found in tumor cells; [9] ) , thus displaying an utter absence of both cooperativity and saturation . Thus , we were forced to conclude that in native membranes , Bax cannot correspond to the molecule M . Rather , Bax must act catalytically for the first reaction , promoting oligomerization of another molecule ( M ) . Because we found that the kinetic constant for the second reaction , pore formation , was directly proportional to Bax concentration , we conclude that Bax is rate-limiting and non-cooperative in this reaction also . To illustrate this point , we generated theoretical curves predicting dose-dependencies in the case of Bax cooperativity ( Figure S5 ) . These curves modeling Bax behavior as an oligomer are parabolic and under no conditions could be quasilinear . The lack of Bax cooperativity argues that Bax oligomerization is not a rate-limiting step in pore formation , as Lovell et al . also reported for liposomes [26] . From the standpoint of kinetics , Bax could either be a simple reactant or a catalyst in the pore formation reaction . Finally , the lack of saturability for both k1 and k2 implies that Bax does not need to interact with a stoichiometric “receptor” in the MOM , or at least that any such interactions are transient and not rate-limiting , even at high [Bax] . In contrast to these Bax dose-responses , with cBid the kinetic constants k1 and k2 showed plateaus , indicating saturability ( Figure 5B ) . This argues that cBid has a limited number of binding sites in the MOM , consistent with prior studies showing that one or more proteins are required for the Bax-activation function of cBid in MOMs [28] , [29] . It may seem paradoxical that k1 and k2 both showed saturable dependencies on [cBid] and linear dependencies on [Bax] , but this is expected if activated Bax enters into both the catalyst assembly and pore formation reactions ( tiers I and II ) , as portrayed in Figure 5D . Recently it was reported that Bax exists in a dynamic equilibrium between free and membrane-associated forms , and Bcl-xL can shift this equilibrium towards the free cytosolic form ( i . e . , “retrotranlocate” Bax ) [46] . Our previous data are consistent with this idea , in that only a fraction of the input Bax molecules become associated with OMVs after activation by cBid , and the even smaller amount of Bax association with OMVs that occurs spontaneously in the absence of cBid-induced activation can be blocked by Bcl-xL [47] . However , other groups reported that Bcl-xL and Bax can neutralize each other while remaining in the MOM [37] , [48] , [49] . Our data here do not discriminate between these possibilities and do not reveal whether Bax molecules , after initiating the assembly of the non-Bax catalyst , remain in the membrane or dissociate from it . Moreover , the activated Bax molecules that enter into the tier II reaction ( pore formation; Figure 5D ) are not necessarily the same ones that initiate the tier I reaction ( catalyst assembly ) . We next used kinetic analysis to analyze the interaction between cBid and Bax . By titrating cBid at two different Bax concentrations , we determined that at higher Bax concentrations , less cBid is required for MOMP ( Figure 5B , C ) . This reciprocal relationship argues that pore formation requires a transient molecular collision between cBid and Bax , rather than a stoichiometric high-affinity reaction . ( For two reactants in solution , simple mass action requires the probability of collision to be proportional to the product of the concentrations of the reactants . Here the reactions occur at or in a membrane , but a collision reaction would still entail an inverse relationship of reactant concentrations . ) Indeed , stoichiometric binding interactions between Bax/Bak and BH3-only proteins have been difficult ( although not impossible ) to detect ( e . g . , [26] , [50] ) . This has prompted some to argue that Bax and Bak become activated in cells only because BH3-only proteins sequester and inactivate their anti-apoptotic partners ( e . g . , Bcl-2 , Mcl-1 , and Bcl-xL ) ( e . g . , [51] ) . In contrast , our experiment provides a direct biochemical confirmation of the alternate hypothesis that cBid directly activates Bax by a “hit-and-run” ( catalytic ) mechanism [10] , [52] , [53] . A recent biophysical study , using EPR spectroscopy with synthetic membranes , reached a similar conclusion [54] . Together with the identification of an alternative BH3-domain binding site in Bax that could mediate this kind of Bax activation [50] as well as recent genetic experiments [55] , our data help validate the “direct activation” model for MOMP . To confirm experimentally that reactions I and II in our model ( Figure 4B ) reflect distinct processes , we analyzed the temperature dependence of cBid/Bax-induced dextran release . We obtained values of k1 and k2 at each temperature by curve-fitting the kinetic data and plotted k1 and k2 in a standard Arrhenius representation ( Figure 6 ) . In this style of plot , the slope of the curve is proportional to the activation energy of the reaction . The plot for k2 was essentially linear; in other words , the activation energy for k2 ( corresponding to the second reaction , pore formation ) was temperature-invariant . Strikingly , however , k1 exhibited two distinct slopes with a breakpoint at ∼28°C . Such a discontinuity in the Arrhenius plot is consistent with a transition in the biophysical state of the system ( possibly in membrane microdomains ) that facilitates formation of the catalyst ( complex M*n ) at temperatures above the breakpoint ( see Discussion below ) . Importantly , these data support our kinetic model by confirming that k1 and k2 correspond to independent reactions with distinct energy barriers . Several proteins present in the MOM ( other than Bcl-2 relatives ) have been proposed to facilitate MOMP either by promoting Bax action [30] , [31] , [56]–[58] or by anchoring cBid [28] , [29] . To determine whether proteins could account for the increased sensitivity of the native MOM to Bax , compared with liposomes , we inactivated MOM proteins using protease treatment , heat-inactivation , and chemical inhibition . We first tested if pretreatment of OMVs with proteinase K affected cBid/Bax-induced dextran release , as it was reported that a similar treatment of isolated mitochondria inhibits Bax oligomerization [59] . We found that VDAC , an integral membrane protein , was resistant to digestion , as were Bif-1 ( Figure S6B ) and some other MOM proteins visible in Coomasie Blue-stained gels ( unpublished data ) . In contrast , the MOM protein Tom20 was digested by proteinase K ( Figure S6B ) . Surprisingly , proteinase K treatment had no effect on OMV permeabilization ( Figure S6A ) . Thus , proteinase K-digestible proteins were dispensable for MOMP in our system . Next , we used heat treatment to inactivate MOM proteins more generally . According to a calorimetric study [60] , most proteins in rat liver mitochondria undergo denaturation at temperatures just below 70°C . Accordingly , we incubated OMVs at 68°C for 10 min and then assayed for Bax-induced dextran release . This heat pretreatment completely eliminated the response to mixtures of cBid and Bax that ordinarily caused robust MOMP ( Figure 7A ) , showing that MOM proteins are essential . ( Heat treatment did not cause noticeable aggregation of OMVs , as determined by dynamic light scattering; unpublished data . ) Such an effect could have been explained merely by the requirement of cBid for a heat-labile protein “receptor . ” To examine this possibility , we repeated these experiments using another BH3-only protein , BimS , which induced Bax-dependent MOMP with very similar kinetics ( Figure S6C ) . Additionally , we tested the effect of moderately elevated temperature ( 45°C ) , which can promote Bax activation in the absence of BH3-only proteins [61] . When Bax was pre-incubated alone at 45°C , it did not induce MOMP when added subsequently to OMVs . However , incubating OMVs at 45°C in the presence of Bax alone induced dextran release with biphasic kinetics like those seen at 25°C with cBid+Bax or BimS+Bax ( Figure S6D ) . We found that 68°C heat-inactivation of OMVs also inhibited dextran release induced by BimS+Bax or by Bax at 45°C , albeit less completely than with cBid+Bax ( Figure S6C ) . However , the inhibitory effect of preincubation at 68°C could be overcome by adding Bax at much higher concentrations ( Figure 7B ) , suggesting that heat-labile proteins enhance the pore-forming activity of smaller input amounts of Bax . Together , these data demonstrate that functional MOM proteins are indispensable for cBid activity and also strongly potentiate Bax function . One candidate MOMP-enhancing protein we considered was Drp1 . The potential role of this protein in MOMP has been studied both in cell biological [22] , [62]–[64] and biochemical [30] , [58] studies , but whether it plays an obligate role in MOMP is controversial . Downregulation of Drp1 in cells has been reported to delay cytochrome c release after the administration of apoptotic stimuli [22] , [30] , [65] . Recombinant Drp1 protein was shown to enhance the oligomerization of Bax in liposomes by inducing massive membrane remodeling [30] . Although Drp1 is not generally considered a resident MOM protein , it has been detected at various levels in some isolated mitochondria [22] , [66]–[68] . Some compounds that inhibit Dnm1 , the yeast ortholog of Drp1 , also block Bax/Bak-dependent cytochrome c release from isolated mouse mitochondria [31] , which suggests that Drp1 may be required for MOMP . We found that , in our rat liver OMV system , the Dnm1 inhibitors designated as mdivi-1 analogs “B” and “H” [31] inhibited cBid/Bax-induced OMV permeabilization ( Figure 7C , D ) . Analog H also efficiently inhibited cBid/Bax-induced dextran release from Xenopus egg OMVs ( unpublished data ) . An inactive analog ( 8L310s ) had no effect in either system ( unpublished data ) . These results raised the possibility that Drp1 is the catalyst , M . Surprisingly , however , Drp1 was undetectable by immunoblot in rat liver OMVs , purified rat liver mitochondria ( Figure S6E ) , and Xenopus egg mitochondria ( Figure S6F ) , although strong Drp1 bands were seen in cytosol . Furthermore , the inhibitory effect of mdivi-1 analogs was observed also in the absence of added GTP and Mg2+ and was unaffected by the Mg2+ chelators EDTA and EGTA ( unpublished data ) . Thus , GTP hydrolysis is not required for the functional target of mdivi-1 analogs . The in vitro membrane-remodeling activity of Drp1 described by Martinou and colleagues [30] requires ATP , not GTP . Our system lacks ATP and is unaffected by ATP addition ( unpublished data ) . Thus , in OMVs , the mdivi-1 analogs apparently do not target a canonical Drp1 activity . It remains formally possible , but unlikely , that trace amounts of Drp1 in the isolated outer membrane act in an unconventional manner to promote MOMP . The mdivi-1 analog H did not block the binding of Bax to rat liver OMVs ( Figure S3B , C ) , implying that this compound acts downstream of Bax recruitment to the membrane . The active mdivi-1 analogs were among the few agents we tested that could prolong the lag phase , suggesting that these compounds specifically inhibit reaction I . In contrast , elevated temperatures strongly facilitated reaction I . Figure 6 shows that a shift from 22°C to 45°C caused a 7-fold increase in the rate of catalyst formation ( in the absence of mdivi-1 analogs ) , but only a 2-fold increase in the rate of pore formation . Our kinetic model therefore predicts that elevated temperatures would oppose the effects of mdivi-1 analogs , particularly with regard to the lag phase kinetics ( catalyst assembly ) . To test this , we incubated OMVs at either 45°C or 25°C , in the presence or absence of mDivi-1 analog H . ( Control experiments assured that the inhibitory compound was not trivially degraded during the 45°C incubation; unpublished data . ) At 25°C , inhibition by analog H was virtually complete: no dextran release was observed during the 60-min experiment ( Figure 7D and unpublished data ) . However , at 45°C , despite the presence of the inhibitor , pore formation began to occur after an extended lag ( Figure 7E ) . Strikingly , the rate of dextran release ( pore formation ) was almost the same as in the absence of inhibitor . This result could be explained by two different mechanisms , which are not mutually exclusive . On the one hand , the data are consistent with our kinetic model's prediction that elevated temperatures would facilitate assembly of the catalyst in reaction I , albeit slowly because of inhibition by the mdivi-1 compound . The other possibility is that incubation at 45°C could increase the lateral mobility of integrated Bax , allowing Bax to migrate slowly to putative foci of pore formation , despite inactivity of the catalyst . For either mechanism , we surmise that raising the temperature helps alleviate the effects of membrane crowding [69] . Regardless of the mechanism , these data support our two-reaction model by confirming that catalyst activation and pore formation are distinct , experimentally separable processes .
Our data show that very large dextrans and smaller dextrans were released with similar kinetics ( Figure S4 ) . A slightly shorter lag phase for 10 kDa dextrans may indicate that smaller pores are assembled faster than larger ones . This is perhaps consistent with electrophysiological studies describing dynamic low and high conductance states of Bax-dependent channels [70] , [71] . However , Bax-induced pores permitting the unrestricted passage of very large dextran molecules ( Figure S4A ) are too large to be typical protein channels . Instead , our data , along with previous reports from others and ourselves , favor a mechanism for MOMP based on the formation of supramolecular lipidic pores [9] , [27] , . As opposed to the concept of discrete Bax channels [39] , [70] , lipidic pores would be at least partly framed by a toroidal lipid monolayer [72] . Such pores in the MOM could resemble the growing pores imaged in cBid/Bax-treated liposomes by Schafer et al . [28] using cryo-electron microscopy . Our cell-free OMV system , which is quite pure apart from the presence of attached ER ( Figure 1 ) and lacks energy sources , is not expected to recapitulate cellular processes involving the translocation of molecules from other cellular compartments to the MOM . Nevertheless , our studies help establish MOM-intrinsic mechanisms of pore formation triggered by activated Bax . In particular , our experiments revealed an important mechanistic feature: the Bax-induced assembly of a catalyst that in turn enhances Bax pore formation . This event is intriguing , as it likely represents a new point of apoptotic regulation that has not been reproduced in liposomes . Assembly of the oligomeric catalyst complex is relatively slow and highly cooperative . In contrast , Bax oligomerization is a “kinetically silent” ( not rate-limiting ) event , as shown by a linear dependence of the kinetic constants k1 and k2 on Bax concentration ( Figures 4 and S5 ) . Many studies , including our own , have observed that Bax/Bak oligomerization is correlated with MOMP , but it has never explicitly been shown that oligomerization is a requisite event . Our data showing a lack of Bax cooperativity now argue that , on the contrary , Bax oligomerization does not contribute to MOMP kinetics . Instead , we propose that integrated Bax monomers are the active agent , assisted by the catalyst complex . The catalyst could function via either of two general mechanisms , which are not mutually exclusive: ( 1 ) facilitating the pore formation mechanism per se and thereby perhaps lowering the threshold concentration of Bax required for pore formation , or ( 2 ) facilitating the accumulation of integrated Bax in putative membrane microdomains , which might serve as preferential sites of pore formation . Evaluating these possibilities will require further study . In the meantime , there are reasons to suggest that the catalyst acts at least via mechanism 2 . Firstly , activated Bax can permeabilize liposomes and thus has an intrinsic pore-forming function . Therefore , a partitioning of Bax into local domains is the simplest hypothesis that could explain an enhanced rate of pore formation . Secondly , this notion is also supported by the observation that high concentrations of Bax can overcome the block to MOMP caused by heat-inactivation of MOM proteins ( Figure 7B ) . However , the catalyst could also act by mechanism 1 . Indeed , we found that intermediate concentrations of mdivi-1 analog H affect both the lag and rapid kinetic phases ( Figure 7C , D ) , arguing that the catalyst also directly enhances the pore formation process . An intriguing possibility is that the catalyst could remodel the membrane , inducing a localized change in curvature . Perhaps , in a manner analogous to the activity recently reported for Drp1 in liposomes [30] , the mdivi-1-sensitive catalyst could induce a local membrane deformation that facilitates the concentration of Bax into membrane microdomains with altered curvature . Furthermore , such a remodeling event could add stress to the membrane , reducing the energy barrier for pore formation induced by Bax . In this way , the catalyst could reduce the local threshold Bax concentration for pore formation ( i . e . , act via mechanism 1 ) . Supporting the idea of a membrane-remodeling event is the observation of a sharp temperature-dependent change in activation energy for k1 that is reminiscent of a membrane phase transition ( Figure 6 ) . This led us to hypothesize that at temperatures above the transition point at ∼28°C , altered lipid packing facilitates activation of the catalyst molecule . Surprisingly , however , MOMs did not exhibit a large-scale membrane phase transition ( Figure S7 ) . Taking these observations together , we surmise that MOMs undergo Bax-dependent lipid rearrangements that are limited to microdomains and thus would not be measurable in the bulk membrane . Elevated temperatures would facilitate changes in the lipid packing in these small domains and thereby help promote activation of the catalyst molecule . Bax could be involved in a concerted remodeling of such membrane microdomains , both in its role as inducer of catalyst activation ( tier I in Figure 5D ) and in its role in the pore formation reaction ( tier II ) . Previous studies revealed the intrinsic ability of activated Bax to destabilize membranes . For example , activated Bax was observed by cryo-EM to increase the curvature of membrane regions in artificial lipid vesicles [28] . Remodeling mechanisms based on protein-induced curvature have been observed in other biological membranes [77] , [78] . Despite the inhibition of MOMP by mdivi-1 analogs , our data did not support a requirement for Drp1 . We found that pore formation in OMVs was independent of ATP and GTP . Thus , if Drp1 were involved , its action would involve a previously unreported type of activity . We attempted to observe an activity of exogenous recombinant Drp1 , but this protein did not stimulate MOMP in OMVs , nor did it restore MOMP to heat-inactivated OMVs ( unpublished data ) . More significantly , endogenous Drp1 protein was below the limits of detection in rat liver OMVs and mitochondria and in Xenopus OMVs ( Figure S6E , F ) , which all displayed similar biphasic MOMP kinetics . Our results suggest that , with regard to MOMP , the true vertebrate target of mdivi-1 analogs is not Drp1 . However , we cannot formally exclude the ( unlikely ) possibility that the catalyst manifests a noncanonical activity of a form of Drp1 that failed to be detected by the antibody . We considered one other candidate for the catalyst: the Bax-interacting protein Bif-1/Endophilin B1 . Like Drp1 , Bif-1 can form high-order oligomers and remodel membranes in vitro [58] , [79] . As with Drp1 , we hoped to see an effect of adding exogenous protein . However , recombinant Bif-1 did not stimulate MOMP when added to rat liver OMVs and even had a slightly inhibitory effect on MOMP when included during reconstitution of proteoliposomes from Xenopus OMV lipids and proteins ( [28]; unpublished data ) . More significantly , although a small fraction of Bif-1 was found in rat liver OMVs in a protease-resistant form ( Figure S6B ) , Bif-1 was not detectable in Xenopus OMVs ( Figure S6G; the antibody did recognize Bif-1 strongly in Xenopus cytosol ) As Xenopus OMVs respond to Bax with kinetics very similar to those of rat liver OMVs , the absence of detectable Bif-1 suggests that this protein is not the catalyst molecule M . ( However , the same caveat applies as stated above for Drp1 . ) The molecular identification of the catalyst awaits further investigation . In conclusion , our studies revealed some surprising aspects of Bax-induced pore formation in native mitochondrial outer membranes . In particular , our data were inconsistent with an involvement of oligomeric Bax and were also inconsistent with a requirement for Drp1 . Moreover , our kinetic studies revealed a two-tiered system of reactions that is not reproduced by simplified liposome models . In native MOMs , Bax induces the relatively slow formation of a multimeric catalyst complex that strongly facilitates the formation of supramolecular membrane pores . Assembly of this catalyst complex was influenced by temperature in a way that suggested dependence on membrane biophysical properties . We propose that the catalyst complex acts both to accelerate the redistribution of membrane-integrated Bax and to facilitate the process of pore formation directly , perhaps through a membrane-remodeling event . The catalyst , although still unidentified , could represent a novel point of regulation for apoptotic cell death and could represent an additional therapeutic target for diseases involving aberrant cell death or survival .
Mitochondria were isolated from the livers of male Sprague-Dawley rats by standard differential centrifugation techniques [80] . The isolation buffer contained 210 mM mannitol , 70 mM sucrose , 10 mM HEPES-KOH ( pH 7 . 4 ) , 2 mM EGTA , and 0 . 1% bovine serum albumin ( essentially fatty acid free , Sigma ) . Isolated mitochondria were further purified in a step gradient of iodixanol ( OptiPrep , AxisShield-Sigma ) as described previously [81] with some modifications . The mitochondrial pellet was resuspended in 36% iodixanol diluted in SHE buffer ( 250 mM sucrose , 10 mM HEPES-KOH , pH7 . 4 , 2 mM EGTA ) , to a final volume of 9–10 ml . The iodixanol gradients , consisting of 3 ml SHE , 5 ml 17 . 5% iodixanol , 5 ml 25% iodixanol , and ∼1 . 5 ml of the mitochondrial suspension in 36% iodixanol , were prepared in six 16 ml tubes . The gradients were centrifuged at 50 , 000×g for 2 h . Purified mitochondria were recovered from the 17 . 5%/25% interface . A fraction formed on the SHE/17 . 5% interface contained light mitochondria contaminated with endoplasmic reticulum ( ER ) . This fraction was collected for comparison with the purified mitochondrial fraction and OMVs . In addition , a crude ER fraction ( microsomes ) was isolated from the rat liver homogenate as described [81] and used for ER marker assays . For preparation of OMVs , purified mitochondria were diluted 7–8 times in a hypotonic buffer ( 10 mM KOH , pH 7 . 4 , 0 . 5 mM EGTA , 4 mM KCl ) and incubated for 10 min . After hypotonic treatment , mitochondria were centrifuged at 12 , 000×g for 10 min and the pellet was resuspended in 2 ml of the hypotonic buffer supplemented with 5 mg of either 70 kDa dextran-fluorescein ( Sigma ) or 10 kDa dextran-cascade blue ( Invitrogen ) . The suspension was homogenized in a 7 ml glass Dounce homogenizer using a tight-fitting pestle ( 40–50 strokes ) . The homogenate volume was then adjusted to 6 ml and OMVs were purified in iodixanol step gradients prepared in three 16 ml tubes . Each tube contained 2 ml of the homogenate , 5 . 5 ml 8% iodixanol , 5 . 5 ml 17 . 5% iodixanol , and 1 . 5 ml 25% iodixanol . The gradients were centrifuged at 50 , 000 g for 2 h and OMVs were collected from the 8%/17 . 5% interface . Unincorporated fluorescent dextran was separated from OMVs by the 8% iodixanol layer . Remaining mitochondria partially devoid of the outer membrane ( mitoplasts ) banded on the 17 . 5%/25% interface . This fraction was used in analyses of the purity of OMVs . Additional removal of external dextran was achieved by diluting OMVs 10-fold in the hypotonic buffer and concentrating them by centrifugation at 100 , 000×g for 20 min . The pellet was resuspended in 200 µl of the hypotonic buffer . Typical protein concentration in OMV suspension was ∼2 mg/ml as determined by BCA assay ( Pierce ) . All isolation steps were performed at 4°C . Xenopus Egg OMVs loaded with 70 kDa dextran-fluorescein were prepared as described previously [9] . Human Bcl-xL and cleaved human Bid ( cBid ) were generated as described [9] . For some experiments , we used a full-length Bid construct containing a thrombin digestion site in place of the caspase cleavage site [82] , or alternatively , commercial caspase-8-cleaved Bid ( R&D Systems ) ; these were equally active . Full-length human Bax was produced essentially as described by Suzuki et al . [83] . Recombinant BimS protein was a gift from Dr . Frédéric Luciano ( then of the Sanford-Burnham Medical Research Institute ) . This protein was prepared essentially as described for BimEL [84] , with induction by 1 mM IPTG for 1 h at 37°C ( Frédéric Luciano , personal communication ) . Bad and Noxa BH3 peptides [21] were obtained from AnaSpec . Analogs of mdivi-1 inhibiting the GTPase activity of Dnm1 , the yeast ortholog of Drp1 , were obtained from BioNet , UK . The active compounds used were 7L-365S [3- ( 2 , 4-dichloro-5-isopropoxyphenyl ) -2-sulfanyl-4 ( 3H ) -quinazolinone] and 8L-309S [7-chloro-3- ( 2 , 4-dichloro-5-isopropoxyphenyl ) -2-sufanyl-4 ( 3H ) -quinazolinone] , which correspond to mdivi-1 analogs B and H , respectively , in the original report [31] . An inactive analog 8L-310S [6-chloro-3- ( 2 , 4-dichloro-5-isopropoxyphenyl ) -2-sulfanyl-4 ( 3H ) -quinazolinone] was used as a negative control . To prepare defined liposomes , the following phospholipids were used: phosphatidylcholine ( PC ) and phosphatidylethanolamine ( PE ) from chicken egg , phosphatidylinositol ( PI ) and phosphatidylserine ( PS ) from soybean , and cardiolipin ( CL ) from bovine heart . The lipids ( in chloroform solution ) were obtained from Avanti Polar Lipids and mixed in the molar ratio found in isolated mitochondria [9]—that is , , PC∶PE∶PI∶PS∶CL = 47∶28∶9∶9∶7 ( mol/mol ) . When CL was omitted , the lipid ratio was PC∶PE∶PI∶PS∶CL = 54∶28∶9∶9 ( mol/mol ) . After lipid mixing , chloroform was removed by evaporation under an argon stream . The lipid film was lyophilized for 2–3 h . Dry lipids were resuspended in KHE buffer ( 150 mM KCl , 10 mM HEPES , pH 7 . 4 , 0 . 5 mM EGTA ) containing 70 kDa dextran-fluorescein ( 1 mg/ml ) and subjected to five freeze/thaw cycles . Unilamellar liposomes were prepared by extrusion through 200 nm polycarbonate filters using a Mini-Extruder ( Avanti Polar Lipids ) . Unentrapped dextran-fluorescein was removed by gel filtration using a Sephacryl S-500 HR column ( GE Healthcare Bio-Sciences ) . In some experiments , defined liposomes of the indicated composition were prepared by a detergent removal method as described previously [28] . To prepare liposomes from extracted mitochondrial lipids , phospholipids were extracted from isolated rat liver mitochondria according to the published protocol [85] . The extracted lipids were processed as described above and liposomes were formed by the extrusion method . OMVs loaded with Cascade Blue- or fluorescein ( FITC ) -labeled dextran ( 10 and 70 kDa , respectively ) were incubated in the KHE buffer in the presence of specific antibodies that bind and quench the fluorophores . The final concentrations of anti-Cascade Blue and anti-fluorescein antibodies ( Invitrogen ) were ∼300 and ∼80 µg/ml , respectively . The type of dextran used is specified in the figure legends . Both sets of fluorophore/antibody gave essentially the same results . Fluorescence measurements were performed with a POLARstar Omega microplate reader ( BMG Labtech ) using 400 nm excitation and 420 nm emission filters for cascade-blue and 485 nm excitation and 520 nm emission filters for fluorescein . The assay volume was 100 µl . Alternatively , cascade blue fluorescence was monitored in a FluoroMax-2 spectrofluorometer ( Horiba Jobin-Yvon ) using a 200 µl quartz cuvette; the wavelengths were set at 400 nm excitation/418 nm emission . Both the cuvette- and microplate-based assays produced the same results . Addition of the antibodies to intact OMVs typically resulted in a small ( 2%–10% ) decrease in the fluorescence signal reflecting quenching of residual external dye . Membrane permeabilization was then induced by cBid and Bax ( unless indicated otherwise ) and ensuing dextran release was measured continuously for 20–35 min at 25°C ( or at specified temperatures ) . OMV protein concentration in the assay buffer was 0 . 2–0 . 3 mg/ml for rat liver and 0 . 05 mg/ml for Xenopus egg OMVs . Triton X-100 ( 0 . 05%–0 . 1% ) was added to set a baseline for complete permeabilization of the vesicles . Normalized dextran content in OMVs was quantified using the equation: F = ( Ft−Ftriton ) / ( F0−Ftriton ) , where Ft is the fluorescence measured at a given time , Ftriton is the fluorescence measured in the presence of Triton X-100 , and F0 is the initial ( maximal ) fluorescence of the vesicles . OMVs were stored on ice in the dark and used within 1–2 d after preparation . No spontaneous leakage of the dyes from the vesicles was detected for at least 3 d after preparation . The same membrane permeabilization assay was performed with dextran-fluorescein loaded liposomes . Bax membrane insertion was measured using Bax labeled with NBD dye ( IANBD amide , Invitrogen ) as described previously [26] with some modifications . Bax contains two endogenous cysteine residues that react with NBD . For labeling , freshly prepared NBD solution was added to ∼400 µl Bax ( ∼6 µM ) in a labeling buffer . Labeling buffer was prepared as described [26] , except that the CHAPS concentration was reduced to 0 . 25% . The final concentration of NBD in the labeling reaction was ∼60 µM . The reaction mixture was incubated under constant stirring in the dark for 1 . 5 h at room temperature . Unbound dye was removed by gel-filtration using Sephadex G-25 columns ( PD-10 , Pharmacia Biotech ) . The protein was eluted using the labeling buffer without CHAPS . Fractions with labeled Bax were identified by measuring tryptophan fluorescence ( excitation 280 nm/emission 340 nm ) and NBD ( excitation 475 nm/emission 530 nm ) in an LS50B spectrofluorometer . The membrane-permeabilizing activity of Bax-NBD was the same as unlabeled Bax but declined significantly after freeze/thaw or storage for over 24 h at 4°C . For membrane insertion measurements , freshly prepared Bax-NBD was added to OMVs , and NBD fluorescence was measured in the POLARstar-Omega plate reader using 485 nm excitation and 520 nm emission filters . The assay conditions were the same as described for the dextran release measurements . OMVs were incubated with cBid and Bax as described above for the dextran release measurements . The assay mixture was then placed on ice and subjected to float-up density gradient centrifugation as described [21] or with an improved technique: the sample ( 200 µl ) was mixed with the equal volume of 50% iodixanol in SHE buffer . This mixture was overlaid with 1 and 0 . 4 ml layers of 17 . 5% iodixanol and SHE . The gradients were centrifuged in a microcentrifuge at 16 , 000×g for 70 min ( at 4°C ) . The membranes were recovered from the SHE/17 . 5% iodixanol interface , placed into a 0 . 1 µm microfiltration unit ( Millipore ) , and collected from the retentate after filtration ( centrifugation at 12 , 000×g for 8–10 min ) . Bax content in the membranes was determined by immunoblotting . Tom20 was analyzed as a loading control . Densitometry analysis of Western blots was performed using ImageJ software ( NIH ) . Bax band intensities were corrected for loading differences . Changes in Bax content were normalized to control ( loaded on the same gel ) . Time-dependent Bax binding to OMVs was normalized to the Bax signal obtained at the first time point ( ∼0 . 1 min after Bax addition in the presence of cBid ) . Isolated mitochondria and OMVs ( 0 . 2 mg/ml ) were incubated with 10 µM DPH ( Sigma ) , a hydrophobic probe for fluorescence polarization ( FP ) measurements [86] . DPH-labeled samples were equilibrated at each given temperature for 10 min and FP was measured in the POLARstar-Omega plate reader using 355 nm excitation and 430 nm emission filters . Based on experimental evidence ( discussed in Results ) , we concluded that the release of fluorophore-conjugated dextran per se from vesicles as well as its quenching by appropriate antibodies are rapid and non-rate-limiting processes that could thus be omitted from the model . To facilitate the kinetic modeling , we made some reasonable simplifying assumptions . First , we assumed that the rate of dextran release is proportional to the rate at which vesicles become permeabilized . In turn , this is proportional to the rate of pore formation ( see text ) . These assumptions imply that the vesicles exist only in two states: empty and full . Thus , for the purposes of modeling , the vesicles are analogous to biochemical entities , which are also binary: either reacted or unreacted . Importantly , this enabled us to use methods established for modeling biochemical kinetics . The simple reaction schemes that we examined at first ( Figure 4 , panels 1 , 2 ) clearly did not fit the observed kinetics of dextran release , which displayed a pronounced initial lag . However , we found that a somewhat more complex model involving two coupled reactions ( Figure 4A , panel 3 ) produced an essentially perfect fit to the experimental data . In this model , Reaction I entails the assembly of a multimeric complex . This complex serves as a catalyst for Reaction II , vesicle permeabilization ( which according to our assumptions is physically equivalent to the formation of a dextran-permeable membrane pore ) . Fortunately , we could derive an exact mathematical solution for the theoretical curves . Reaction II is a pseudo-first-order process whose rate is proportional to the concentration of the intact vesicles: ( 1 ) The pseudo-constant of this reaction k2∼ is proportional to the concentration of the catalytic complex C formed in Reaction I: ( 2 ) Next , we consider Reaction I , which consists of two steps , in the first of which a monomeric molecule M is activated to form M* . This relatively slow event is followed by a rapidly reversible second step , the multimerization of activated monomer M* to form the catalyst complex M*n . The activation step is a first-order reaction , and therefore the integral form of the rate law is the exponential function: ( 3 ) where k1 is the kinetic constant and [M]0 is the initial concentration of the inactive monomer M . The second step , multimerization of M* , is governed by the thermodynamic mass action law: ( 4 ) where Ka is the thermodynamic equilibrium constant and n is the degree of multimerization . Combining Equations 2–4 , we obtain the expression for k2∼: ( 5 ) Curve-fitting analysis of the experimental data for dextran release ( see below ) would produce estimates for the rate constant k1 and the coefficient , k2′ Ka [M]0n , but not for the individual components ( k2′ , Ka , or [M]0 ) . Thus , we must replace this coefficient with a combination constant , k2 , obtaining the following equation: ( 6 ) The equation describing dextran release is the standard solution for this kind of rate law: ( 7 ) where [Intact vesicles]0 is the initial concentration of vesicles . The fluorescence of the entrapped dextrans corresponds essentially to the fraction of intact vesicles , multiplied by the fluorescence intensity of the labeled dextrans . In practice , we also have to take into account the background fluorescence . The measured fluorescence , F ( t ) , at time t can thus be rewritten: ( 8 ) where F0 is the background fluorescence at time zero and Fmax is the change in fluorescence intensity after complete quenching ( i . e . , after addition of detergent ) . Finally , by substituting the formula for k2∼ from equation ( 6 ) and adding an additional term for a constant rate ( k3 ) of background leakage that we observed even in the control curves , we obtain the following equation , which was used to fit the experimental data: ( 9 ) In summary , this kinetic analysis identified k1 and k2 as parameters quantitatively describing the process of MOMP . They reflect , respectively , the rate of catalyst assembly and the rate of vesicle permeabilization . In a dextran release curve ( e . g . , Figure 4A ) , k1 determines the duration of the lag phase , while k2 is reflected in the maximal slope during the rapid phase . To determine the kinetics of MOMP in intact mitochondria , we measured Complex IV ( cytochrome c oxidase ) -dependent respiration in the presence of exogenous cytochrome c and a Complex III inhibitor , using a Clark-type electrode ( Hansatech ) . Added cytochrome c was continuously reduced via the NADH/cytochrome b5 reductase system , with NADH as the source of reducing equivalents [42] . Under these conditions , the rate of respiration depends on cytochrome c influx through the MOM and can therefore be used as a measure of MOMP . Specifically , isolated rat liver mitochondria ( 0 . 4 mg/ml ) were incubated in a KCl-based respiration buffer containing 125 mM KCl , 10 mM HEPES ( pH 7 . 4 ) , 0 . 5 mM KH2PO4 , 50 µM EGTA , 0 . 3 µM FCCP , and 10 mM succinate . Succinate-supported uncoupled respiration was measured for 3–4 min followed by addition of 2 µM myxothiazol , the Complex III inhibitor . Complex IV-dependent respiration was then stimulated by NADH ( 2 mM ) , cytochrome c ( 80 µM ) , and cBid/Bax at indicated concentrations . Respiration was measured until O2 exhaustion or termination of the reaction by addition of the Complex IV inhibitor , KCN ( 0 . 5 mM ) . 5–10 µl of the OMV suspension was applied onto a carbon-coated cooper 300 mesh grid ( Ted Pella , Inc . ) . The sample was negatively stained with 2 . 0% aqueous uranyl acetate for ∼30 s . Excess of staining solution was removed and the grids were air-dried . The vesicles were visualized in an electron microscope ( H-600A; Hitachi ) and images were acquired with a cooled 11 . 2-Megapixel CCD camera ( SIA-L9C; Scientific Instruments and Applications , Duluth , GA ) using Maxim DL software , v . 5 . 2 ( Diffraction limited , Ottawa , Canada ) . Samples of mitochondria , OMVs , mitoplasts , and ER ( 1 . 2–10 µg ) were run on NuPage 4%–12% Bis-Tris gels ( Invitrogen ) at 200 V for 45 min . Proteins were electrotransferred to nitrocellulose membrane ( BioRad ) at 30 V for 1 h . The membranes were stained with antibodies to cytochrome c oxidase subunit IV ( COX IV , 1∶1 , 000 dilution; Invitrogen ) , VDAC ( 1∶2 , 000; Calbiochem ) , and calnexin ( 1∶100; Chemicon ) . Other antibodies used were to Tom20 ( 1∶1 , 000; Santa Cruz Biotechnology ) , Bif-1 ( 1∶200; Imgenex ) , Bax-N20 ( 1∶1 , 000; Santa-Cruz ) , and Drp1 ( 1∶500; BD-Biosciences ) . The secondary antibodies were horseradish peroxidase-conjugated anti-mouse- or anti-rabbit-Ig antibodies ( 1∶2 , 000 dilution; Amersham ) . Protein bands were detected using either ECL reagent ( Amersham ) or SuperSignal West Femto reagent ( Thermo Scientific ) and X-ray film . Succinate dehydrogenase ( SDH ) activity was measured spectrophotometrically using coenzyme Q1 and 2 , 6-dichlorophenolindophenol ( DCPIP ) ( modified from [87] ) . Samples of mitochondria , mitoplasts and OMVs were permeabilized with 0 . 1% Triton X-100 . The reaction buffer contained 20 mM HEPES ( pH 7 . 5 ) , 10 mM succinate , 60 µM coenzyme Q1 , 80 µM DCPIP , and 1 mM KCN . Kinetics of succinate oxidation coupled with DCPIP reduction was measured as DCPIP absorbance decrease at 600 nm in the POLARstar-Omega plate reader . No changes in DCPIP absorbance were detected in the presence of 50 µM 2-thenoyltrifluoroacetone , a specific SDH inhibitor . Final concentrations of the samples were 0 . 01–0 . 02 mg/ml; assay volume was 100 µl . SDH activities ( the rates of DCPIP reduction ) were normalized per mg of protein . The reagents were from “Sigma . ” Monoaminooxidase ( MAO ) activity was measured fluorometrically using Amplex Red MAO assay kit ( Invitrogen ) and the manufacturer's protocol . The kinetics of the reaction were measured in the plate reader with 544 nm excitation and 590 nm emission filters . Rates obtained in the presence of specific MAO inhibitors ( provided in the kit ) were subtracted as background . MAO activities were normalized per mg of protein . To quantify the level of enrichment in corresponding markers , SDH and MAO activities in OMVs and mitoplasts were normalized to their activities in the whole mitochondria from which they were prepared . | Mitochondria are the key energy-producing structures inside cells , but are also crucial players in a common form of programmed cell death , apoptosis . A critical event in mitochondrion-driven apoptosis involves the formation of large pores in the mitochondrial outer membrane ( MOM ) . These pores cause long-term damage to mitochondria and also allow mitochondrial proteins to escape and accelerate cell death . Previous studies have revealed that the protein Bax when activated can form pores in protein-free membranes and that it , along with Bak , is involved in the formation of mitochondrial pores , but the process remains unclear . We now show , however , that in naturally derived MOMs , Bax is assisted by another resident MOM protein , which we term the “catalyst , ” and whose identity is still unknown . The mechanism involves two distinct stages . First , activated Bax activates the catalyst protein , causing multiple catalyst molecules to assemble into a larger structure ( a complex ) . In the second stage , this catalyst complex in turn facilitates Bax-driven pore formation . Our data also reveal some unexpected details of the pore formation process; in particular , it appears that catalyst activation involves a physical change in the molecular arrangement of the membrane . Furthermore , contrary to what was previously assumed , pore formation does not require Bax molecules themselves to assemble together into larger complexes . | [
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] | 2012 | Bax Activation Initiates the Assembly of a Multimeric Catalyst that Facilitates Bax Pore Formation in Mitochondrial Outer Membranes |
Human gamma herpesviruses , including Kaposi’s sarcoma-associated herpesvirus ( KSHV ) and Epstein-Barr virus ( EBV ) , are capable of inducing tumors , particularly in in immune-compromised individuals . Due to the stringent host tropism , rodents are resistant to infection by human gamma herpesviruses , creating a significant barrier for the in vivo study of viral genes that contribute to tumorigenesis . The closely-related murine gamma herpesvirus 68 ( γHV68 ) efficiently infects laboratory mouse strains and establishes robust persistent infection without causing apparent disease . Here , we report that a recombinant γHV68 carrying the KSHV G protein-coupled receptor ( kGPCR ) in place of its murine counterpart induces angiogenic tumors in infected mice . Although viral GPCRs are conserved in all gamma herpesviruses , kGPCR potently activated downstream signaling and induced tumor formation in nude mouse , whereas γHV68 GPCR failed to do so . Recombinant γHV68 carrying kGPCR demonstrated more robust lytic replication ex vivo than wild-type γHV68 , although both viruses underwent similar acute and latent infection in vivo . Infection of immunosuppressed mice with γHV68 carrying kGPCR , but not wild-type γHV68 , induced tumors in mice that exhibited angiogenic and inflammatory features shared with human Kaposi’s sarcoma . Immunohistochemistry staining identified abundant latently-infected cells and a small number of cells supporting lytic replication in tumor tissue . Thus , mouse infection with a recombinant γHV68 carrying kGPCR provides a useful small animal model for tumorigenesis induced by a human gamma herpesvirus gene in the setting of a natural course of infection .
Herpesviruses are ubiquitous pathogens in humans that have been implicated in an array of human diseases , including a wide range of cancers . The human gamma herpesviruses , including Epstein-Barr virus ( EBV ) and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , are capable of promoting tumor formation in immune-compromised individuals , e . g . , human immunodeficiency virus ( HIV ) -infected patients and organ transplant recipients [1–4] . KSHV is the etiological agent of human Kaposi’s sarcoma ( KS ) , primary effusion lymphoma and multicentric Castleman’s disease ( MCD ) [1–4] . KS is the most common neoplasm in AIDS patients that manifests in the skin , the surface of internal organs and the oral cavity . Particularly , the oral cavity is an important compartment for KSHV infection , replication and dissemination . Histologically , KS tumor lesions are composed of both latently-infected spindle cells and a small subset of cells supporting lytic replication of KSHV [5–7] . The prevailing postulate is that these two types of infection programs synergize in fostering the genesis of sarcoma or lympho-proliferative disorders [8] . In the presence of an active immune response , human EBV and KSHV have evolved to establish a lifelong persistent infection . To date , KSHV has been reported to infect only primates [9] , demonstrating a narrow host range for successful de novo infection in vivo [10 , 11] . Thus , most pathogenesis studies of human gamma herpesvirus genes involving small rodents rely heavily on transgenic or xenograft nude mouse models . For example , in transgenic models , the KSHV gene products LANA and vFLIP induce lymphoproliferative diseases that display some pathological features similar to human PEL or , to a less extent , MCD [12 , 13] . In contrast , mice with endothelium- or other tissue-specific expression of kGPCR , developed angiogenic lesions in the skin that resemble human KS [14–16] . These models provide important tools to investigate the interaction between viral transforming proteins and key cellular signaling pathways in instigating tumor formation; however , these studies take place outside of the context of viral infection of a natural host . Presumably , infection of a co-evolved host will provide additional layers of interaction between viral factors and host signaling components . Expression of multiple viral genes also triggers complex immune responses that likely influence the course and outcome of disease . Murine gamma herpesvirus 68 ( γHV68 ) is genetically related to human KSHV and EBV [17 , 18] . γHV68 infection results in a transient lytic replication in the lung or spleen that is followed by persistent infection , primarily in the form of latency [19] . γHV68 infection causes no apparent symptoms in immune-competent laboratory mouse strains , but lymphoproliferative disease and B cell lymphoma have been reported in β2-microglobulin-deficient mice that lack CD8 T cells [20] . Genome sequencing and functional studies have pointed to a number of viral factors that collectively contribute to the pathogenesis of KSHV . In the KSHV genome , genes encoding viral factors cluster into the K3-K7 locus and the latency locus ( including ORF74 ) are implicated in KSHV pathogenesis , notably tumorigenesis . One interesting example is the viral G protein-coupled receptor , kGPCR ( also known as ORF74 ) [21] . kGPCR is a homologue of the human IL-8 receptor and constitutively activates downstream signaling cascades without its cognate ligands . Compared to the IL-8R , kGPCR binds to a wide spectrum of chemokines that can alter kGPCR-mediated signal transduction [22–26] . In the absence of cognate ligand , kGPCR constitutively activates downstream signaling . All gamma herpesviruses encode a GPCR in their genomes , despite the functionality of these viral GPCRs may differ . The γHV68 mGPCR demonstrated no basal level of signaling activity that was only detected upon ligand stimulation , behaving like a cellular GPCR in signal transduction [27] . mGPCR-mediated signaling was proposed to promote viral lytic replication [27] . Human gamma herpesviruses demonstrate restricted host range and the lack of animal models hinders the in vivo study on these important human pathogens . We report here that introduction of kGPCR into the γHV68 genome rendered γHV68 the ability to induce tumor formation . Histologically , tumors derived from mice infected with recombinant γHV68 . kGPCR consisted of spindle-shaped cells and significant infiltrated immune cells . These signature components recapitulate key pathological features of human KS tumors . Importantly , cells with latent infection and lytic replication were detected in tumor tissues . Thus , we have engineered a recombinant γHV68 that is capable of inducing tumor formation in mice , providing a valuable tool to investigate tumorigenesis in the context of viral infection of a natural host .
The KSHV kGPCR ( ORF74 ) is a constitutively active signaling molecule , independent of ligand binding [22] . In contrast , the γHV68 mGPCR requires ligand binding to induce downstream signaling [27] . We thus compared the signaling capacity of kGPCR and mGPCR . Previous studies have shown that kGPCR activates signaling events that , ultimately , culminate in up-regulating gene expression driven by NF-κB , NFAT and AP-1 transcription factors [28–31] . We utilized luciferase reporter assays to probe the activation of NF-κB , NFAT and AP-1 by kGPCR and mGPCR . In transfected 293T cells , kGPCR expression highly elevated the expression of an NFAT-dependent reporter ( Fig 1A ) . By contrast , mGPCR expression had no detectable effect under the same conditions . kGPCR , but not mGPCR , modestly up-regulated gene expression driven by responsive elements of NF-κB and AP-1 transcription factors ( Fig 1B and 1C ) . Immunoblotting analysis showed that mGPCR and kGPCR were expressed at comparable levels in 293T cells ( Fig 1D ) . Similar results were obtained from murine NIH 3T3 fibroblasts that support robust lytic replication of γHV68 , i . e . , kGPCR activated NF-κB and NFAT-dependent gene expression , but mGPCR failed to do so ( Fig 1E and 1F ) . Collectively , these results show that kGPCR , but not mGPCR , activates NF-κB , NFAT and AP-1 transcription factors , signaling events underpinning kGPCR tumorigenesis . Cellular and viral GPCRs primarily localize to the cell surface where extracellular stimuli regulate intracellular signal transduction . kGPCR is unique in that it is predominantly retained in the trans-golgi network ( TGN ) , although its cell surface expression can be detected as well [30] . To compare the intracellular localization of kGPCR and mGPCR , we established mouse SVEC endothelial cells that stably express kGPCR and mGPCR with lentiviral infection . Confocal immunofluorescence microscopy analysis showed that kGPCR resided in the TGN , co-localizing with TGN46 ( Fig 2A ) . By contrast , mGPCR was dispersed in the cytoplasm , displaying an intracellular localization distinct from TGN46 staining . The subcellular pattern of mGPCR was reminiscent of the endoplasmic reticulum ( ER ) compartment ( Fig 2A ) . Indeed , mGPCR co-localized with protein disulfide isomerase , an ER resident protein , as analyzed by confocal microscopy ( Fig 2A ) . These results indicate that kGPCR and mGPCR reside in the TGN and ER compartment , respectively . However , we would like to point out that the cell surface expression of both viral GPCRs is expected . Signaling pathways that are constitutively activated by kGPCR , including the PI3K-AKT cascade that promotes cell proliferation and survival , underpin the tumorigenesis of kGPCR [14 , 32 , 33] . We thus compared the effect of kGPCR and mGPCR on AKT activation . Stable expression of kGPCR in SVEC cells elevated AKT phosphorylation at serine 437 under starvation condition , indicative of AKT activation ( Fig 2B ) . mGPCR expression in SVEC cells did not significantly impact the level of phosphorylated AKT under similar conditions . To compare the tumorigenic potential of kGPCR and mGPCR , we initially utilized a xenograft nude mouse model . In this model , SVEC cells stably expressing kGPCR or mGPCR were mixed with bystander regular SVEC cells to assess the autocrine and paracrine action of kGPCR in promoting tumor formation . At two weeks post-injection , tumors were detected in mice that were grafted with SVEC cells expressing kGPCR . No tumors were detected in mice that were grafted with control SVEC cells or SVEC cells expressing mGPCR , even at four weeks post-inoculation when mice grafted with kGPCR-expressing SVEC cells had to be euthanized ( Fig 2C ) . Tumors derived from SVEC cells expressing kGPCR were highly vascularized , which was visible in the skin , and tumor weight averaged ~850 mg ( Fig 2D ) . Collectively , these results indicate that kGPCR , but not mGPCR , potently activates signaling events and induces tumor formation derived from murine SVEC endothelial cells . The cell proliferative and tumorigenic potential of kGPCR , compared to mGPCR , prompted us to engineer a recombinant γHV68 that expresses kGPCR in place of mGPCR and under control of the endogenous mGPCR promoter . To facilitate detection of kGPCR , we also inserted an HA epitope immediately after the start codon of kGPCR ( Fig 3A ) . The GEMBO Bacmid , which incorporates the beta-lactamase ( Bla ) marker gene at the latent locus was selected as the parental backbone to introduce kGPCR into the mGPCR locus [34] . Viruses produced from the GEMBO BACmid were used to mark cells that are latently infected by γHV68 . Once recombinant γHV68 that carries kGPCR ( designated γHV68 . kGPCR ) was generated , we also introduced mGPCR back to the same locus to replace the HA . kGPCR , yielding γHV68 revertant ( γHV68 . rev ) . BAC DNA was purified from bacteria and digested with BamHI to analyze the overall digestion pattern . Due to two BamHI sites located at the 3’ end of kGPCR , a ~5 . 3 kb fragment disappeared in γHV68 . kGPCR BAC DNA , presumably BamHI digestion converted the ~5 . 3 kb fragment into a ~5 kb fragment that already existed ( S1A Fig ) . The kGPCR locus was further PCR amplified and sequenced , which validated the introduction of the HA-tagged kGPCR , without any additional mutations ( S1B Fig ) . Furthermore , we employed genome sequencing to compare the BAC DNA of γHV68 wild-type with that of γHV68 . kGPCR . This identified three point mutations within γHV68 . kGPCR , excluding the difference in kGPCR ( S1C Fig ) . Taken together , the sequencing data indicate the overall integrity of these recombinant γHV68 genomes . BAC DNA was then transfected into NIH 3T3 cells to produce recombinant γHV68 . Recombinant γHV68 viruses were passaged in NIH 3T3/Cre cells three times to remove the backbone of γHV68 BAC DNA and plaque assays were performed to determine the titer of recombinant γHV68 . Multi-step growth of recombinant γHV68 indicated that wild-type γHV68 and γHV68 . rev replicated indistinguishably in NIH 3T3 cells . Interestingly , γHV68 . kGPCR replicated with much faster kinetics and reached higher titer in NIH 3T3 cells , compared to wild-type γHV68 ( Fig 3B ) . This result indicates that kGPCR can enhance γHV68 replication when expressed from infected cells . Additionally , γHV68 . kGPCR consistently formed larger plaques in NIH 3T3 cells than wild-type γHV68 or γHV68 . rev ( Fig 3C ) . The larger plaque sizes did not correlate with more robust syncitia . Thus , the increased replication of γHV68 . kGPCR contributes to the larger plaques . Real-time PCR analysis with primers specific for viral lytic genes indicated that higher levels of expression of lytic genes in cells infected with γHV68 . kGPCR than those of cells infected with wild-type γHV68 ( Figs 3D and S1D ) . Surprisingly , kGPCR mRNA level was significantly lower than that of mGPCR at all time points post-infection ( Fig 3D ) . Immunoblot analysis further confirmed more robust viral RTA and TK expression in cells infected with γHV68 . kGPCR than those in cells infected with wild-type γHV68 ( Fig 3E ) . Taken together , these results indicate that kGPCR expression can promote γHV68 lytic replication . Considering that γHV68 . kGPCR replicated more robustly than wild-type γHV68 in vitro , we examined the lytic replication during acute infection of BALB/c mice , a wild-type inbred strain amenable to tumor development . When BALB/c mice were infected with high dose of virus ( 1 × 105 pfu ) via intra-peritoneal injection , we found that approximately 1 × 103 pfu per spleen was detected by plaque assay at 3 days post-infection ( dpi ) for both wild-type γHV68 and γHV68 . kGPCR , indicating significant lytic replication ( Fig 4A ) . At 5 and 7 dpi , γHV68 replication was about 1 × 102 pfu per spleen . At all three time points , similar levels of viral loads were detected in mice infected with wild-type γHV68 and γHV68 . kGPCR . None of the infected mice showed disease symptoms at the time of harvest . Regardless of the route and dose of infection , γHV68 efficiently establishes latent infection in splenocytes , including B lymphoid cells and other immune cells ( e . g . , macrophages ) [35] . We examined the latent infection in splenocytes of BALB/c infected with recombinant wild-type γHV68 and γHV68 . kGPCR . Limiting-dilution PCR analysis indicated that the frequency of splenocytes carrying γHV68 . kGPCR was ~2-fold higher than that of wild-type γHV68 , approximately one in 100 cells at 16 days post-infection ( Fig 4B ) . However , this was not due to increased frequencies of reactivation or preformed virus , as parallel samples of splenocytes from γHV68 . kGPCR-infected mice exhibited slightly lower levels of both compared to wild-type γHV68-infected mice ( Fig 4C ) . Furthermore , wild-type γHV68- and γHV68 . kGPCR-infected mice exhibited similarly lower frequencies of infection at 45 days post-infection ( Fig 4D ) , while reactivation and preformed virus were below the detection limit ( S2 Fig ) . These results show that wild-type γHV68 and γHV68 . kGPCR establish similar levels of latent infection in splenocytes in vivo . Having characterized lytic replication and latent infection of recombinant γHV68 . kGPCR in mice , we next assessed whether infection by γHV68 . kGPCR can induce tumors in BALB/c mice . In a pilot experiment , we set up two groups of mice that were infected with either wild-type γHV68 or γHV68 . kGPCR . At three weeks post-infection , mice were injected with cyclosporine A ( CsA ) to inhibit T cell response . Out of a total of 15 experimental mice that were infected with γHV68 . kGPCR and treated with CsA , five mice displayed malignant conditions of the liver , lung and subcutaneous compartment ( Fig 5A ) . Over the course of six months of the experiment , one out of five mice that were infected with γHV68 . kGPCR and treated with CsA died . Although the mouse had no apparent lesion on the skin , inspection of its internal organs identified highly vascular nodules in the liver ( Fig 5B ) . The reddish lesions of apparent size share similar characteristics of highly vascularized lesions with human nodular KS . Another mouse demonstrated localized fibrosis in the lung ( Fig 5C ) , which displayed none of the angiogenic characteristics of human Kaposi’s sarcoma . In addition to the hepatic KS-like lesion observed in one mouse , 4 mice developed large subcutaneous tumors in a hind leg at 5 months post-infection with γHV68 . kGPCR and treated with CsA , although no apparent skin lesion was observed ( Fig 5D ) . The tumor was large and highly vascular , consistent with the notion that kGPCR induces angiogenesis of endothelial cells . H&E staining showed that the tumor cells were spindle-shaped and packed into slit-like structure , infiltrated with large numbers of erythrocytes ( Fig 6A ) . Large numbers of immune cells were also observed within or proximal to the highly packed tumor region . IHC staining revealed that kGPCR was expressed in a small subset of cells scattered in the tumor lesion , which is consistent with the paracrine action of kGPCR in human KS tumors ( Figs 6B and S3A ) . Given the suspected origin of endothelial cells of KS , we examined the tumor tissue with antibodies against an endothelial marker CD31 and found that a significant portion of tumor cells expressed high levels of CD31 , most of which were elongated and spindle-shaped cells ( Figs 6C and S3B ) . These cells appeared to connect with each other , implying that they were undergoing proliferation in these tumors . Indeed , a significant fraction of tumor cells were stained positive for Ki67 ( Figs 6D and S3C ) , a marker of proliferating cells . Human KS lesions demonstrate hallmarks of excessive inflammatory response , with infiltration of a wide range of immune cells . Thus , we determined whether immune cells are present in the angiogenic lesions that developed in mice infected with γHV68 . kGPCR . To this end , we performed IHC to probe T cells , macrophages and dendritic cells . Antibody against human CD3 identified scarce T cells in the tumor lesion ( Fig 6E ) . The low level of T cells is likely due to the immune suppressive effect of cyclosporine A . To determine whether this is due to lack of antigen education , we further probed antigen-presenting cells with antibody against CD80 ( also known as B7-1 ) that provides co-stimulatory signal for T cell activation . CD80 is generally expressed on the cell surface of activated B cells and monocytes [36] , classic antigen-presenting cells . Interestingly , a small number of cells were positively stained with anti-CD80 antibodies and these cells had a small cytoplasm , morphological characteristics of B cells and monocytes ( Fig 6F ) . Finally , we examined the relative frequency of macrophages and dendritic cells in the tumor lesions . These innate immune cells have been implicated in antigen presentation and cytokine production , thus influencing the course of tumorigenesis . When tumor tissues were stained with antibodies against IBA-1 and CD11c , markers for macrophages and dendritic cells . We found that large number of cells were positive for IBA-1 and well distributed in the tumor lesion ( Figs 6G and S3D ) . By contrast , cells stained for CD11c were much less and localized ( Fig 6H ) . These cells demonstrated elongated and spindle-shaped cytoplasm , forming the slit-like structure within tumors . Overall , these H&E staining and IHC analyses demonstrate that the tumor derived from γHV68 . kGPCR virus displayed significant number of immune infiltrates , signature inflammatory components of human KS . An important characteristic of human KS is the presence of predominant KSHV latently-infected cells and low percentage of cells supporting lytic replication of KSHV . It was postulated that a synergy between cells of latent and lytic KSHV programs fuels the sarcomagenesis . We first examined the latently infected cells with an antibody against the latency-associated nuclear antigen ( LANA ) , a hallmark antigen for latent γHV68 infection . Using mock- and γHV68-infected NIH 3T3 cells , we showed that the purified rabbit serum was highly specific to LANA antigen by immunoblotting and immunofluorescence staining ( S4A and S4B Fig ) . When tumor tissues were analyzed by IHC staining , we observed that approximately 20–30% of tumor cells were positive for LANA expression in the nucleus ( Fig 6I ) . LANA positive cells were more likely present in the highly packed slit-like structure . To detect cells supporting γHV68 lytic replication , we analyzed tumor tissues by IHC with an antibody against vGAT ( ORF75c ) , a tegument protein of virion particle . Immunoblotting and immunofluorescence staining demonstrated that the purified anti-vGAT antibody was highly specific for vGAT in γHV68-infected NIH 3T3 cells ( S4C and S4D Fig ) . As shown in Fig 6J , vGAT-positive cells were apparent in the tumor section and accounted for about 2–3% of tumor cells . The frequency of vGAT-positive cells is similar to that of kGPCR-expressing cells ( Fig 6B ) . Thus , similar to KS lesions , cells of latency and lytic replication programs of γHV68 are present in tumor tissue derived from γHV68 . kGPCR-infected mice . We used cyclosporine A to suppress host T cell immunity , which facilitated tumor formation in mice infected with γHV68 . kGPCR . Cyclosporine A is a potent inhibitor of NFAT activation and kGPCR activates NFAT . Thus , we examined the effect of cyclosporine A on the lytic replication of γHV68 . kGPCR and wild-type γHV68 . The latter serves as a control for γHV68 . kGPCR . However , cyclosporine A had no significant effect on viral replication in NIH 3T3 cells as determined by plaque assay ( S5A–S5D Fig ) . Thus , cyclosporine does not intrinsically promote the lytic replication of γHV68 . kGPCR in cultured cells . To compare the tumors derived from mice infected with γHV68 . kGPCR to human KS , we examined the inflammatory cells present in human KS samples that have been previously reported [37] . H&E staining revealed that the tumor tissue was filled with a large number of erythrocytes , which were widely distributed within the tumor lesion ( Fig 7A ) . Tumor cells of irregular nuclei were abundant and packed into slit-like structure constituting of spindle-shaped cells . IHC with antibody against the latency-associated nuclear antigen ( LANA ) , an obligate molecule for KSHV persistent infection , showed that KSHV latently-infected cells were abundant , many of which were the signature spindle-shaped cells with elongated nuclei ( Figs 7B and S6A ) . To identify endothelial cells , we stained the tumor section with anti-CD31 antibody and found nearly 50% of cells were positive for CD31 expression , indicating their endothelial origin ( Fig 7C and S6B ) . IHC staining with antibody against the Ki-67 proliferation marker identified a high percentage of cells , indicative of their active proliferation ( Figs 7D and S6C ) . These results confirmed that KS tumors are neoplasms of latent KSHV infected cells . To define the inflammatory nature of the KS tumors , we probed a number of immune components , including T cells , macrophages and dendritic cells . Their presence in KS tumors has been implicated in the prognosis and severity of KS [37] . Antibody against the common CD3 antigen reacted with cells scattered in sectioned tumor ( Fig 7E ) , with regions were highly enriched for CD3-positive cells ( S6D Fig ) . This is likely due to lytic replication of KSHV in a group of highly localized tumor cells . To further probe antigen-presenting cells ( APCs ) in the tumor , we choose antibody against CD80 , a marker for matured APCs , for IHC analysis . This identified some densely packed tumor cells that detected low level of CD80 expression ( Fig 7F ) . Using antibody against a well-defined macrophage marker , IBA-1 [38] , we discovered that a significant number of macrophages were present in the highly dense tumor region ( Figs 7G and S6E ) . By contrast , dendritic cells stained with anti-CD11c were less abundant ( Fig 7H ) . Taken together , human KS tumors are extensively infiltrated with immune cells , critical components of organ inflammatory responses .
In this study , we report that introduction of kGPCR into murine γHV68 enables the virus to induce angiogenic lesions in infected mice . Despite that γHV68 encodes its own GPCR homologue , wild-type γHV68 is not sufficient to induce KS-like lesion or malignancies in infected laboratory mouse . In fact , mGPCR failed to activate multiple signaling cascades that are potently up-regulated by kGPCR . Additionally , kGPCR , but not mGPCR , induced tumor formation in a xenograft nude mouse model . One notable difference between these two viral GPCRs is their distinct intracellular localization . While kGPCR primarily resides in TGN , mGPCR appears to localize in the ER compartment , a phenomenon that may underpin the differential signaling capacity . Regulation by cognate ligands at the plasma membrane appears to be shared by both kGPCR and mGPCR [25 , 27] . It is possible that the post-translational modification indigenous to TGN is a limiting factor for viral GPCR signaling . Events such as glycan modification/editing and tyrosine sulfation occur in the TGN and are relevant to the trafficking and signaling of GPCRs . We previously reported that the N-terminal tyrosine-containing motif of kGPCR was sulfated within the TGN compartment and that sulfation promoted kGPCR signaling and tumorigenesis [39] . Further experiments will be needed to examine these possibilities . Nevertheless , these results demonstrate the fundamental difference between the two closely-related viral GPCR homologues in signaling and tumorigenesis , prompting us to develop a rodent tumor model utilizing a recombinant γHV68 carrying kGPCR . Multiple transgenic mouse models have been developed in which kGPCR , when expressed in a ubiquitous or endothelium-specific manner , is sufficient to induce tumor lesions [14 , 16 , 33] . These mouse tumors recapitulate key pathological features of human Kaposi’s sarcoma , including the spindle-shaped tumor cells and angiogenic vasculature . Though these models excel in tumorigenesis induced by single gene product , they lack the viral infection component . With a recombinant γHV68 carrying kGPCR , we now show that infection in mice triggered angiogenic tumors when T cells were muted with cyclosporine A . Interestingly , recombinant γHV68 . kGPCR also displayed faster replication kinetics in cultured cells and formed larger plaques , indicating that kGPCR enhances viral lytic replication in vitro . This observation is consistent with previous reports that kGPCR-mediated signaling enhances the lytic replication of KSHV likely via RTA [40 , 41] , although the molecular detail by which kGPCR impinges on viral lytic replication remains to be further explored . Despite that γHV68 . kGPCR undergoes more robust lytic replication in vitro than wild-type γHV68 , both viruses established similar levels of latency as judged by viral genome frequency and reactivation capacity of splenocytes . This suggests that host immune response dominates viral infection and facilitates a latent infection , agreeing with the observation that γHV68 established similar levels of long-term latency independent of doses and routes of infection [35] . Consistent with this , we observed that the frequency of splenocytes carrying γHV68 . kGPCR genome was only slightly increased compared to wild-type γHV68 . Conceivably though , this subtle increase in viral persistence in vivo may amplify under conditions of immune-suppression . As such , when T cells were inhibited with cyclosporine A , mice infected with γHV68 . kGPCR developed hepatic and subcutaneous tumor lesions , while those infected with wild-type γHV68 displayed no malignancies when experiment was terminated . To date , we had five , out of 15 mice , developed angiogenic tumors within the time frame of one year . We will continue to optimize the recombinant γHV68-infection tumor model . One possibility is to introduce additional KSHV-specific pathogenic factors , which are devoid in the γHV68 genome [18] , into the recombinant γHV68 . kGPCR . While KS lesions develop predominantly in the skin , it is not rare to find on the surface of internal organs , including the liver . Hepatic KS has been diagnosed with ultrasound or x-ray imaging , but biopsy has not been available [42–44] . A retrospective autopsy analysis on AIDS patients discovered that the most frequent benign neoplasm was hepatic hemangiomas [45] . Thus , a rodent model of hepatic KS may provide insight into the pathological features and fundamental sarcomagenesis of human hepatic KS , despite that the frequency of hepatic KS is relatively low in our small cohort of experimented mice . In addition to nodule KS-like lesion in the liver , mice infected with γHV68 . kGPCR also developed subcutaneous tumor that was infiltrated with various immune cells , critical components in human KS lesion . These cells include erythrocytes , T cells , macrophages and dendritic cells . Among them , macrophage was abundant in tumors compared to the other immune cells , it would be interesting to determine their roles in sarcomagenesis , if any . A previous report also identified macrophages in KS-like tumors derived from Tva mice expressing kGPCR [14] . It is the M2 type macrophage that is implicated in promoting tumor development , while M1 type macrophage inhibits [46] . When mouse lesions were compared to human KS tumors , H&E and IHC analyses revealed the common histopathological features shared by both neoplasms , including slit-like structure , infiltration of diverse immune cells and the abundant CD31+ endothelial cells in the tumor . However , these two types of tumors also differ in the status of T cells , i . e . , with scarce T cells present in mouse angiogenic lesion . This likely stems from the application of cyclosporine A to nullify T cells in the mouse infection model . Alternatively , the distinct anatomical locations of these two types of tumors may underpin the difference in pathology . Nevertheless , these results support the conclusion that tumors induced by γHV68 . kGPCR infection are angiogenic and pathobiologically relevant to KSHV malignancies , providing a useful model to investigate tumor induction by KSHV genes in the context of a natural course of viral infection .
If not specified otherwise , constructs were derived from pcDNA5/FRT/TO ( Invitrogen ) . kGPCR and mGPCR were amplified by PCR and cloned into pcDNA5/FRT/TO between BamHI and XhoI . The HA epitope was inserted upstream of kGPCR and mGPCR coding sequences . HEK293T ( ATCC ) , SV40-immortalized mouse endothelial cells SVEC ( kindly provided by Dr . Philip Sharpe , UT Southwestern ) , NIH 3T12 cells ( ATCC ) were cultured in DMEM supplemented with 10% ( vol/vol ) FBS and 100 U penicillin/streptomycin . NIH 3T3 cells ( ATCC ) were maintained in DMEM supplemented with 10% ( vol/vol ) newborn calf serum and 100 U penicillin/streptomycin . Transfection was performed with DNA-calcium phosphate precipitation . Stable cell lines were established using lentivirus transduction and selected with puromycin ( 1 μg/mL ) . HEK293T cells were plated one day before transient transfection with 50 ng of the plasmid expressing firefly luciferase ( under the control of response elements of NF-κB , NFAT , and AP-1 transcription factors ) and 100 ng of the plasmid expressing β–galactosidase ( driven by a house keeping glucophosphokinase promoter ) . Each transfection was balanced with an empty vector pcDNA5/FRT/TO . Thirty hours after transfection , cells were harvested and lysed in passive lysis buffer ( Promega ) on ice . After centrifugation , supernatant was used to measure luciferase and β–galactosidase activity according to the manufacturer’s instruction . Cells were collected , rinsed with ice-cold PBS and lysed in NP-40 buffer ( 50 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 1% NP-40 , 5 mM EDTA ) supplemented with a protease inhibitor cocktail ( Roche ) , and sonicated for 3 times on ice . Whole cell lysates were denatured and resolved by SDS-PAGE . Proteins were transferred to nitrocellulose membrane and blocked in PBS-T containing 3% non-fat milk for one hour at room temperature . Membrane was incubated with primary antibodies overnight at 4°C . Blots were washed extensively and incubated with corresponding IRDye800 conjugated secondary antibodies ( Licor ) at 1:5000 for one hour at room temperature . Proteins were visualized using Odyssey infrared imaging system ( Licor ) . To generate rabbit antibody against thymidine kinase ( TK or ORF21 ) , the N-terminal region ( amino acid 1–300 ) was cloned into pGEX-4T-1 . Expression and purification of GST fusion proteins were carried out as previously described [47 , 48] Purified proteins were used to immunize rabbits and whole serum was used to probe TK expression from γHV68-infected cells , along with pre-immune serum as a control . Anti-RTA antibody was described previously [49] . Rabbit sera against vGAT ( ORF75c ) and mLANA ( ORF73 ) were described previously [50 , 51] . The antibodies were affinity purified using GST fusion antigens . Immunofluorescence and immunohistochemistry experiments were carried out as previously described [30 , 52 , 53] . Cells were fixed with 4% ( vol/vol ) paraformaldehyde for 20 min and permeabilized with 0 . 4% ( vol/vol ) Triton X-100 for 20 min . For cell surface staining , Triton X-100 permeabilization was omitted from the procedures . Goat serum ( 10% ) was used to block for one hour at room temperature . Cells were incubated with monoclonal anti-HA ( Covance ) , anti-PDI ( Stressgene ) , anti-TGN46 ( Abcam ) , anti-vGAT , anti-mLANA antibodies overnight at 4°C . After extensive washing , cells were incubated with corresponding secondary antibodies for one hour at room temperature . DAPI was stained before mounted onto microscope slides . Cells were analyzed with a Nikon E800M microscope . Mouse or human tissues were fixed in 10% neutral buffered formalin ( Sigma ) overnight at room temperature . Tissues were then dehydrated , embedded in paraffin , and cut into 3-μm sections . After antigen retrieval , tissue sections were subject to H&E staining and immunohistochemical staining with antibodies against HA epitope ( OriGene ) , CD31 ( Abcam ) , CD3 ( Abcam ) , CD11c ( Abcam ) , Ki67 ( Abcam ) , CD80 ( Abcam ) , IBA-1 ( Abcam ) , rabbit or mouse ABC staining system ( Santa Cruz ) , and DAB substrate kit ( Vector laboratories ) . The γHV68 . kGPCR virus was generated from the parental wild-type γHV68 . ORF73bla marker virus bacterial artificial chromosome ( BACmid ) backbone [34] using allelic exchange , as previously described [54] . Briefly , full-length kGPCR was cloned from KSHV genomic DNA and ligated in place of full-length mGPCR in a pGS284 allelic exchange vector [54] carrying wild-type mGPCR and flanking homology arms . Allelic exchange was performed using pGS284 . kGPCR in S17λpir and γHV68 . ORF73bla BACmid GS500 Escherichia coli , as previously described [54] . Following positive and negative selection , diagnostic restriction digests were performed on multiple clones to determine the integrity of the viral genome , and the region of interest was directly sequenced to confirm correct insertion . A single validated BACmid clone was transfected into NIH 3T3 murine fibroblasts to generate high-titer viral stocks , and resulting virus stock was serially passaged in NIH 3T3 cells stably expressing Cre recombinase , resulting in the removal of the loxP-flanked BAC sequence and the generation of the γHV68 . kGPCR virus . γHV68 . rev was generated by recombination of wild-type mGPCR sequence into the identical γHV68 . kGPCR BACmid clone . The frequency of splenocytes harboring γHV68 genome was measured by LD-PCR as previously described [35] . Briefly , mouse spleens were homogenized , re-suspended in isotonic buffer and subjected to 3-fold serial dilutions ( from 104 to 41 cells/well ) in a background of uninfected RAW 264 . 7 cells , with a total of 104 cells per well . Twelve replicates were plated for each cell dilution . After being plated , cells were subjected to lysis by proteinase K at 56°C for 8 hours . Following inactivating the enzyme for 30 minutes at 85°C , samples were subjected to nested PCR using primers specific for γHV68 ORF72 . Reaction products were separated using 2 . 5% UltraPure agarose ( Invitrogen ) gels and visualized by ethidium bromide staining . To measure ex vivo viral growth kinetics , NIH 3T3 cells were infected with γHV68 . WT , γHV68 . kGPCR or γHV68 . Rev at a multiplicity of infection ( MOI ) of 0 . 05 . Cells and supernatant at different time points were collected and viral titer was determined by plaque assay . To do that , samples were serially diluted and plated onto NIH 3T3 in 24 wells in replicate . Infection was carried out at 37°C for two hours with frequent rocking every 15 min . Cells were then overlaid with DMEM containing 2% NCS and 2% methylcellulose . Cells were incubated for 5 days at 37°C and eventually stained with 0 . 33% neutral red . Plaques were visualized under microscope and all titers were determined in parallel . All animal experiments were carried out according to the National Institutes of Health principles of laboratory animal care and approved by the University of Southern California Institutional Animal Care and Use Committee ( IACUC ) with permit number A0372 . The xenograft experiments were performed as described previously [30 , 39 , 53] . Briefly , 1×105 stable SVEC cells expressing mGPCR or kGPCR , along with 1×105 bystander SVEC cells were subcutaneously injected into the flanks of 6- to 8-week-old athymic ( nu/nu ) nude mice ( Jackson Laboratory ) . Tumor formation was monitored twice every week and tumors were weighed when mice were euthanized . For infection with recombinant γHV68 , 6- to 8-week-old Balb/c mice were intraperitoneally injected with 105 PFU γHV68 . WT or γHV68 . kGPCR . A week after inoculation , mice were treated with 10 mg/kg cyclosporine A via intraperitoneal injection twice per week . Body weight was monitored every month . All animal experiments were performed according to the National Institutes of Health principles of laboratory animal care and approved by the University of Southern California Institutional Animal Care and Use Committee ( IACUC ) . Statistical analyses were performed using an unpaired , two-tailed Student’s t-test . P values of less than 0 . 05 were considered to be statistically significant . Mice experiment data were analyzed using GraphPad Prism software ( GraphPad Software . Statistically significant p values are indicated by asterisks: *p < 0 . 05; **p < 0 . 01; ***p < 0 . 001 . | Human gamma herpesviruses , including Epstein-Barr virus ( EBV ) and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) , are causatively linked to a spectrum of human oncogenic malignancies . Due to the stringent host restriction , rodents are generally not amenable to infection by EBV and KSHV . Murine gamma herpesvirus 68 ( γHV68 ) is closely related to KSHV and EBV , although infection in mouse does not manifest apparent diseases . Here we developed a recombinant γHV68 that carries the KSHV G protein-coupled receptor , an important signaling molecule implicated in KSHV pathogenesis . Intriguingly , laboratory mice infected with this recombinant γHV68 developed angiogenic lesions that resembled human Kaposi’s sarcoma . This mouse infection with recombinant γHV68 carrying KSHV GPCR represents a useful model to investigate viral oncogenesis induced by human gamma herpesvirus in the context of viral infection . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [] | 2015 | Recombinant Murine Gamma Herpesvirus 68 Carrying KSHV G Protein-Coupled Receptor Induces Angiogenic Lesions in Mice |
The early events that shape the innate immune response to restrain pathogens during skin infections remain elusive . Methicillin-resistant Staphylococcus aureus ( MRSA ) infection engages phagocyte chemotaxis , abscess formation , and microbial clearance . Upon infection , neutrophils and monocytes find a gradient of chemoattractants that influence both phagocyte direction and microbial clearance . The bioactive lipid leukotriene B4 ( LTB4 ) is quickly ( seconds to minutes ) produced by 5-lipoxygenase ( 5-LO ) and signals through the G protein-coupled receptors LTB4R1 ( BLT1 ) or BLT2 in phagocytes and structural cells . Although it is known that LTB4 enhances antimicrobial effector functions in vitro , whether prompt LTB4 production is required for bacterial clearance and development of an inflammatory milieu necessary for abscess formation to restrain pathogen dissemination is unknown . We found that LTB4 is produced in areas near the abscess and BLT1 deficient mice are unable to form an abscess , elicit neutrophil chemotaxis , generation of neutrophil and monocyte chemokines , as well as reactive oxygen species-dependent bacterial clearance . We also found that an ointment containing LTB4 synergizes with antibiotics to eliminate MRSA potently . Here , we uncovered a heretofore unknown role of macrophage-derived LTB4 in orchestrating the chemoattractant gradient required for abscess formation , while amplifying antimicrobial effector functions .
Staphylococcus aureus is a significant cause of severe skin and soft tissue infections that can progress to life-threatening infections , such as osteomyelitis or sepsis , if left untreated [1–3] . The inflammatory response to S . aureus infection is orchestrated by the interaction among structural cells of the skin and both resident and recruited phagocytes [4] . Upon skin infection , keratinocytes and resident immune cells produce antimicrobials to clear the pathogens and generate cytokines , chemokines , and lipid mediators to further activate dermal macrophages and promote neutrophil recruitment to the site of infection [4] . Most studies have focused on the role of cytokines and chemokines as central regulators of skin host defense . Although IL-1β and CXCL2 have been implicated as regulators of neutrophil chemotaxis [5] and IFN-γ and TNFα as potent inducers of phagocyte antimicrobial effector functions [6 , 7] , the early events that lead to the production of these molecules upon bacterial recognition remain elusive . We and others have shown that the lipid mediator leukotriene B4 ( LTB4 ) directly enhances neutrophil migration to inflammatory sites and also amplifies in vitro pathogen recognition and antimicrobial effector functions [8] . LTB4 is produced within seconds to minutes upon phagocyte activation from the multistep metabolism of arachidonic acid ( AA ) by 5-lipoxygenase ( 5-LO ) to form leukotriene A4 ( LTA4 ) , which is then hydrolyzed to LTB4 by LTA4 hydrolase [8] . The effects of LTB4 are mainly mediated by its high-affinity receptor BLT1 [8] , but the low-affinity receptor BLT2 also exerts essential functions in skin homeostasis , cancer , and recruitment of T cells [8 , 9] . In vitro studies show that LTB4/BLT1 also allows TLR activation by increasing MyD88 expression and activating downstream effectors , such as IKK-α and -β , p38 MAPK , IRAK4 , and transcription factors such as NFκB , AP-1 , and PU . 1 [10–13] . We have also shown that aerosolized LTB4 increases clearance of lung Streptococcus pneumoniae , demonstrating its safety and potential therapeutic application [14] . Furthermore , Yamamoto et al . [15] have shown that treatment of mice with LTB4 increased the clearance of MRSA peritoneal infection , but the cellular players and molecular mechanisms involved in in vivo host defense are unknown . A significant component of the host defense during S . aureus infection is the formation of an abscess . The abscess is composed of bacteria and dead and live neutrophils encapsulated in collagen , fibrin , and other fibrous materials that contain bacteria at the site of infection and prevent dissemination to the bloodstream and other organs [16] . While bacterial products are essential components of a compact and well-organized abscess , the host-derived cellular sources and molecular programs t that promote abscess formation are not well understood [17 , 18] . Therefore , therapeutic strategies to improve abscess formation along with enhancing antimicrobial effector functions are highly desirable . Here , we employed state of the art techniques , along with epistatic and gain of function experimental procedures , to identify critical innate immune components crucial in the abscess formation and control of MRSA skin infection . We showed that while 5-LO-/- and BLT1-/- deficient mice were unable to clear the bacteria and form a well-defined abscess that renders animal more susceptible to skin infection , topical treatment of 5-LO-/- mice with LTB4 restored abscess formation and the skin host defenses . We also identified skin macrophages as key players involved in the LTB4 production , which were necessary for orchestrating abscess architecture , bacterial clearance , and NADPH oxidase-dependent ROS production , all of which were necessary for abscess formation and MRSA clearance . These findings confirm the concept that endogenously produced LTB4 was required for optimal control of MRSA skin infection , an ointment containing LTB4 shows synergistic effects with the antibiotic mupirocin to improve skin host defense , abscess formation , while eliminates skin MRSA . These results suggest that LTB4 could be used locally as an immunotherapeutic agent to enhance/restore innate immune host activation during antibiotic-resistant bacterial skin infections .
We first measured the mRNA transcript levels for Alox5 ( the gene that encodes 5-LO ) , Ltb4r1 ( the gene that encodes BLT1 ) , and Ltb4r2 ( BLT2 ) during MRSA skin infection . At 1 day post-MRSA skin infection , the expressions of Alox5 and Ltb4r1 , but not Ltb4r2 , were significantly enhanced in the skin of infected mice compared to naïve mice skin ( Fig 1A ) . The increased transcripts correlated with higher LTB4 production in the skin both early ( day 1 ) and late ( day 11 ) in our study ( Fig 1B ) . Next , we aim to determine the spatial location by which LTB4 might influence ski host defense . Using imaging mass spectrometry ( IMS ) , we detected AA mainly on the epidermis in naïve mice . After infection , we identified a robust production of AA in areas near the edge of the abscess ( red fluorescence ) , indicating that LTB4 could be produced near the abscess and influence neutrophil migration to the site of infection ( Fig 1C ) . Next , we measured 5-LO expression in the infected skin . Our data clearly showed that 5-LO expression was also localized to areas surrounding the abscess , predominantly in macrophages , and less extensively in neutrophils . We also detected , although in lower amounts , 5-LO staining inside the abscess ( Fig 1D ) . We further confirmed these findings by performing immunofluorescence staining of the infected skin with 5-LO ( red ) and the neutrophil marker Ly6G ( green ) . We observed that neutrophils express lower abundance of 5-LO than cells with macrophage morphology located surrounding the abscess ( Fig 1E ) . Together these data suggested that endogenously produced LTB4 is detected in recruited and activated phagocytes in areas near the abscesses , providing an initial chemoattractant gradient required to mount an efficient host defense during MRSA skin infection . Although the role of LTB4 in host defense has been explored in vitro , most studies did not show the relevance of this bioactive lipid in vivo . To that end , mice deficient in LT production ( 5-LO-/- ) , LTB4 responsiveness ( BLT1-/- ) and wild-type ( WT ) mice were infected with MRSA , and the lesion size and bacterial load studied . As a confirmatory approach , WT mice were treated daily with an ointment containing the BLT1 antagonist , U75302 . Genetic or pharmacologic BLT1 deficiency led to increased lesion size and bacterial load at the site of infection ( Fig 2A and 2B ) . Infected 5-LO-/- mice also showed larger infection areas and bacterial burden ( Fig 2C and 2D ) and adding LTB4 back to the 5-LO-/- mice restored the hosts’ skin defenses . Following treatment once daily for 9 days with the LTB4 ointment , the 5-LO-/- mice showed reduced infection sizes and the bacterial burdens were comparable to that of the WT mice levels ( Fig 2C and 2D ) . Next , using Gram staining , we observed intracellular bacteria in the WT mice and increased extracellular bacteria in the skin of BLT1-/- and 5-LO-/- mice ( Fig 2E ) . Importantly , treatment of 5-LO-/- mice with LTB4 restored engulfment of bacteria ( Fig 2E ) . These data demonstrated that LTB4/BLT1 signaling is critical for controlling MRSA skin infections in vivo and further showed its therapeutic potential for controlling MRSA skin infections in the absence of LTB4 . Next , we examined the leukocyte localization and histological changes in the skin of WT , BLT1-/- , and 5-LO-/- mice with and without LTB4 ointment treatment . Bacterial infections in WT mice resulted in neutrophil and monocyte/macrophage recruitment and abscess formation , as defined by collagen-enriched capsules at the sites of infection ( Fig 2F and S1 and S2 Figs ) . Surprisingly , infections in both BLT1-/- and 5-LO-/- mice led to abundant immune cell recruitment throughout the infected sites , indicating that leukotriene signaling was not required for leukocyte migration to the site of MRSA skin infection ( Fig 2F ) . However , while migrating cells from WT mice developed an organized abscess structure with a fibrous capsule , BLT1-/- and 5-LO-/- infected mice lacked an abscess structure and had poor capsule formation ( Fig 2F and S1 and S2 Figs ) . To further confirm a role for LTB4 in abscess formation , 5-LO-/- mice were infected and treated with a topical LTB4 ointment . The abscess structures were considerably restored in these mice , suggesting that LTB4 was required for normal abscess architecture during MRSA skin infection ( Fig 2F and S1 and S2 Figs ) . Furthermore , we measured the thickness of the capsule , and our data show that while 5-LO-/- mice showed less fibrous capsule surrounding the abscess than infected WT mice , LTB4 ointment significantly increased the thickness of the abscess capsule in both WT and 5-LO-/- mice ( Fig 2G and S1 and S2 Figs ) . Because LTB4 is quickly produced , we studied whether blocking LTB4 signaling as early as 3 hours after infection will differently influence bacterial clearance and inflammatory response . We observed increased LTB4 in the site of infection . When mice were treated with the BLT1 antagonist for 3 hours , we detected higher bacterial load; lower cell infiltrates in BLT1 antagonist-treated mice than mice treated with the vehicle control . Importantly , no changes in CXCL2 and TNF-α between the two experimental groups were identified ( S3A–S3E Fig ) . To study if lack of abscess is due to uncontrolled bacterial growth in LT-deficient mice , we infected LT-deficient mice with a lower inoculum ( 5x105 CFU/mL ) and our data showed increased bacterial load in 5-LO deficient mice , but did not detect any differences in the production of the inflammatory cytokine TNF-α and , the chemokine CXCL2 when compared to WT control . Interestingly , H&E staining showed that infection of WT mice lead to small swarm formed mainly by polymorphonuclear cells , while the lower number and disorganized phagocytes migration were found in the site of infection in LT-deficient mice ( S4A–S4C Fig ) . These data show that LTB4 production is primarily required for bacterial clearance neutrophil migration and organization in the site of infection which leads to control of bacterial load in the skin . Next , we determined whether topical LTB4 treatment influenced the generation of inflammatory mediators in the skin of infected mice . LTB4 treatment increased the production of chemokines ( CXCL2 , CCL4 ) and metalloproteinases ( MMP8 ) involved in neutrophil and monocyte recruitment , and inflammatory cytokines ( IL-33 and IFN-γ ) that are known to enhance macrophage antimicrobial effector functions ( S5A–S5D Fig ) . These data unveiled that LTB4 restrains MRSA infection by both controlling phagocyte-mediated bacterial clearance and setting the tone of the inflammatory response that leads to a self-contained abscess formation . We then determined whether LTB4 synergizes with antibiotics to further improve clearance of MRSA infection . We then employed antibiotic mupirocin , that is effective as a topical ointment . When mice were treated with LTB4 plus 0 . 01% mupirocin , the combination of both agents greatly increased bacterial clearance and decreased lesion size ( Fig 3A and 3B ) . We then determined the time points at which LTB4 plus mupirocin were more effective in killing the bacteria in vivo . Using a bioluminescent MRSA and monitored bacterial burden over time on the same animal , we observed a highly synergistic effect when mice were treated with both agents ( Fig 3C ) . Furthermore , when we studied the abscess of mice treated as in the Fig 3A , both LTB4 and mupirocin alone increased the abscess structure and formed a highly defined abscess than mice treated with the vehicle control ( Fig 3D and S1 Fig ) . The combination therapy further decreased the abscess size and increased the size of the capsule , which correlated with improved bacterial clearance ( Fig 3B–3E and S1 Fig ) . These data show that LTB4 is a potent immunotherapeutic agent that influences both early and late events in MRSA infection and synergizes with antibiotic treatment to increase bacterial clearance during MRSA skin infection . To determine the step ( s ) of LTB4 contribution to the increased host defense during skin infection , we assessed the production of inflammatory cytokines , chemokines , and molecules involved in tissue destruction at early ( day 1 ) and late ( day 9 ) time points after infection in both WT and BLT1-/- mice . Our data ( Fig 4A ) clearly showed that at day 1 after infection , we detected high levels of chemokines involved in both neutrophils and monocyte recruitment to the site of infection ( CXCL2 , CXCL1 , CCL8 , CCL4 , CCL2 , and CXCL1 ) , along with cytokines known to increase macrophage and neutrophil antimicrobial effector functions , such as IFN-γ and IL-12p70 in WT mice ( Fig 4A ) . In contrast , these inflammatory mediators were decreased in BLT1-/- mice at day 1 after infection . However , BLT1-/- mice had increased levels of the major neutrophil chemoattractant CXCL2 , possibly explaining the increased neutrophil numbers in these animals . When the infection was mostly cleared in WT mice ( day 9 ) , we observed a decrease in most chemokines , but not in IL-1α and IL-1β , CCL7 , and CCL4 . In BLT1-/- mice , the inflammatory process was still intense as characterized by higher levels of RAGE , TIM , CXCL2 , IFN-γ , MMP12 , and CCL8 . At this time point , we also detected decreased VEGF , P-Selectin , CCL2 , CCL7 , CCL4 , IL-1α , IL-1β , and IL-33 in BLT1-/- when compared to that in the infected WT mice ( Fig 4A ) . These data suggested that BLT1 was essential in controlling early events involved in the recruitment of effector cells during infection , and lack of BLT1 led to chronic inflammatory responses , as characterized by increased inflammatory cytokines chemokines and danger-associated molecular pattern , such as RAGE ( Fig 4A ) . Next , we stained the infected skin of WT , BLT1-/- , and 5-LO-/- mice using anti-Ly6G/C antibodies to further study whether LTB4 controls neutrophil localization . Our data showed that in WT mice , neutrophils were located mainly in the abscess and surrounding the abscess area . However , neutrophils from BLT1-/- and 5-LO-/- mice were not confined to an abscess structure , with the cells found in all layers of the dermis ( Fig 4B ) . Importantly , treatment of 5-LO-/- mice with topical LTB4 ointment restored abscess formation and neutrophil location inside the abscess ( Fig 4B ) . We also confirmed that blocking BLT1 decreases neutrophil migration to the site of infection ( Fig 4C ) . Next , we sought to investigate how LTB4 affected neutrophil directed chemotaxis in the infected skin using intravital microscopy imaging . GFP-expressing myeloid cells ( LysGFP ) mice , were infected with MRSA and treated with the LTB4 or BLT1 antagonist ointment . While neutrophils ( brighter GFP signals than other myeloid cells [19] in the skin of mice treated with vehicle ointment formed swarm-like clusters ( Fig 4C and 4D ) , mice treated with topical LTB4 formed more neutrophil swarm-like clusters than was shown in the vehicle-treated mice ( Fig 4C and 4D ) . However , BLT1 antagonist treatment did not decrease neutrophil accumulation after 24 h of infection . Additionally , although BLT1 antagonism did not affect cellular velocity , the neutrophil median velocity was increased when mice were treated with LTB4 ( Fig 4E and 4F ) . The displacement values were similar between the LTB4-treated and untreated control mice . However , BLT1 antagonism significantly reduced the movement of neutrophils in the infected mice ( Fig 4C ) . The ratio of displacement ( vector of distance from point A to point B ) to duration was used to estimate the cellular directionality and our data showed that the ratio of displacement/duration was increased with LTB4 treatment , and decreased with BLT1 antagonist treatment ( Fig 4F ) . Here we demonstrated that early LTB4 production and BLT1 activation dictates skin host defense by controlling both neutrophil chemotaxis and the generation of antimicrobial effector during MRSA skin infection . Next , we aimed to identify whether macrophage localization and actions in the abscess could influence the production of inflammatory mediators and abscess formation during skin infection . In WT mice , macrophages were found along the periphery of the abscess and within the fibrous capsule ( Fig 5A ) . However , macrophages were randomly distributed throughout the areas of the dermis and in the in the central areas of the infectious foci in the BLT1-/- and 5-LO-/- mice ( Fig 5A ) . We then determined whether LTB4 restored macrophage localization to areas near the abscess seen in WT mice . Our data clearly show that topical LTB4 restored macrophage localization to the periphery of the abscess in 5-LO-/- mice ( Fig 5A ) . Furthermore , these data led us to speculate that macrophage LTB4 production near the abscess is necessary for neutrophil recruitment to the abscess . We then determined if depletion of skin macrophages influenced the in vivo LTB4 production , neutrophil recruitment , and abscess formation during MRSA skin infection . Confirming the depletion studies , the diphtheria toxin ( DT ) treated mice ( S5A Fig ) exhibited lower mCherry fluorescence as detected using IVIS SpectrumCT in vivo imaging and FACS before infection and at day 1 post-infection ( S6B Fig and Fig 6B ) . Depletion of macrophages led to both decreased neutrophil migration and LTB4 production at 6 and 24 hours after skin infection ( Fig 5B and 5C ) . In addition , macrophage depletion led to reduced production of CXCL1 , and CCL4 24 hours after infection ( Fig 5E ) . Next , we hypothesized that macrophage depletion could compromise abscess structure , as observed in the 5-LO-/- or BLT1-/- mice . Confocal microscopy imaging of infected MMDTR mice showed an organized abscess structure at day 1 post-infection that was composed predominantly of neutrophils , as indicated by Ly6G+ staining ( green ) , and was surrounded by mCherry+ macrophages ( red ) ( Fig 5F ) , supporting our observations using immunohistochemistry ( IHC ) staining as shown in Fig 5A . DT treatment of MMDTR mice led to a reduction in neutrophils recruited to the skin and abscess formation ( Fig 5H ) , which correlated with worse infection areas and higher bacterial burdens ( Fig 5G and 5H ) when compared to that of the WT mice treated with DT . These results suggested that macrophages played a central role in LTB4 production during MRSA skin infections , which was critical for abscess formation and bacterial clearance . To confirm that LTB4 was involved in poor host defenses in macrophage-depleted cells , DT-treated MMDTR were subjected to the LTB4 ointment for 24 hours . Our results showed that topical LTB4 decreased lesion size and bacterial clearance in macrophage-depleted mice ( Fig 5G and 5H ) , restored production of the chemokines , CCL2 and CXCL4 , but not CXCL2 ( S7A–S7C Fig ) , VEGF , IL-33 , and IL-1β ( S7D–S7F Fig ) and neutrophil migration to the site of infection ( S7G Fig ) in DT-treated MMDTR mice . Together , these findings identified a novel role for skin resident macrophages in regulating inflammatory response and abscess formation during MRSA skin infection . The generation of ROS is essential in the control of S . aureus infection [16 , 20] . LTB4 utilizes NADPH oxidase to increase in vitro Klebsiella pneumoniae killing . The role of LTB4-mediated NADPH oxidase activity in killing MRSA in vivo was determined employing both pharmacological and genetic approaches . Initially , we determined whether LTB4 is required for MRSA-induced bacterial killing in both macrophages and neutrophils . Our data showed that blocking BLT1 signaling decreases bacterial killing in both cells ( Fig 6A ) , which correlated with decreased ROS production in MRSA-challenged phagocytes pretreated with the BLT1 antagonist ( Fig 6B and 6C ) . Next , we tested whether LTB4 enhances bacterial clearance in vivo . WT mice were treated with topical ointments containing LTB4 , an NADPH oxidase inhibitor , apocynin [21] , or a combination of both agents after the MRSA infection . Furthermore , The WT mice and the gp91phox-/- mice were treated daily with the LTB4 ointment . Here , we observed higher lesion size and bacterial loads when NADPH oxidase was deficient , and while LTB4 alone increased bacterial clearance in WT mice , LTB4 treatment did not reduce the infection areas or bacterial burdens , and both the apocynin co-treatment or gp91phox-/- mice showed similar results , further confirming that LTB4 increased bacterial killing by activating NADPH oxidase in vivo ( Fig 6D and 6E ) . We then determined whether ROS influenced abscess formation and if LTB4-mediated abscess formation was dependent on ROS production . The WT mice treated with apocynin alone showed poor abscess structure , and LTB4 co-treatment with apocynin failed to restore abscess morphology , correlating with higher bacterial burdens ( Fig 6F and S1 Fig ) . These results demonstrated that formation of the abscess and bacterial clearance were dependent upon LTB4-dependent NADPH oxidase activities .
MRSA , previously a nosocomial pathogen , has reached epidemic proportions and now is commonly found in both community and hospital settings [22 , 23] . Infections with antibiotic-resistant pathogens significantly limit treatment options and could lead to irreversible tissue damage and co-morbidities associated with chronic infections [24] . The development of host-derived immunotherapeutics that boost innate immune response and limit antibiotic resistance to avoid alterations in microbiome populations is much needed . Therefore , combination therapies using endogenous molecules along with the use of antibiotics represent a new frontier in the control of antibiotic-resistant pathogens [25] . Among the major cellular players involved in MRSA control , keratinocytes , macrophages and neutrophils are essential in recognizing and eliciting bacterial clearance by producing inflammatory mediators that create chemoattractant gradients required for the recruitment of immune cells to the site of infection , acting in an autocrine fashion to increase leukocyte action , and enhance the generation of microbicidal molecules [4] . Consequently , pleiotropic endogenous molecules that act on amplifying both inflammatory response and antimicrobial effector functions are , in theory , optimal immunotherapeutics to treat antibiotic-resistant infections . Furthermore , the earlier events that shape innate immune response during skin infection is not well appreciated . Here , we focused our efforts in understanding the role of a lipid mediator ( LTB4 ) that is produced within seconds to minutes after microbial challenge and could dictate the outcome of infection . LTB4 has been shown to improve phagocyte effector functions in vitro [14 , 26–32] . Here , by employing a variety of epistatic and gain of function experiments , we unveiled that LTB4 is required to control different steps of the inflammatory response that culminates in a well-capsuled abscess that retains and control MRSA skin infection and that topical LTB4 treatment synergizes with the over-the-counter antibiotic mupirocin that greatly improves abscess formation and accelerates bacterial clearance ( Fig 7 ) . In summary , we found that: 1 ) LTB4 production in areas near the abscess sets the stage for the neutrophil and macrophage chemotaxis during MRSA skin infection . 2 ) Endogenously LTB4 production is required for host control during MRSA skin host by chemotaxis in the tissue , bacterial killing ( ROS production ) and inflammatory response ( chemokines and cytokines ) that directly regulates abscess architecture and bacterial control . 3 ) Skin macrophages govern neutrophil migration , abscess formation , and bacterial clearance . Although the role of proteins and lipids in inflammatory processes are studied using strategies to either inhibit or overexpress a given molecule , the exact location of inflammatory mediators in the site of infection is poorly understood . Here we show that LTB4 production in areas near the edge of the abscess ( as evidenced by enriched 5-LO staining in macrophages and less abundant in neutrophils and AA detection determined by MALDI-IMS ) might directly influence leukocyte behavior and increase antimicrobial effector functions . These findings unlock new biology , since identifying the location of inflammatory mediators during infections could uncover regulatory events necessary for optimal host defense in vivo . LTB4 production in areas near the abscess could favor directed chemotaxis of neutrophils to the site of infection and shape production of inflammatory mediators to elicit phagocyte antimicrobial effector functions . S . aureus abscess is molded by several layers of neutrophils , dead cells and macrophages , surrounded by a capsule of fibrin/collagen that contains the pathogen [16 , 17] . Although a number of the pathogen-derived virulence factors and cell wall components required for abscess formation have been studied [17 , 33] , less is known about host-derived products produced during S . aureus infection and how endogenous inflammatory mediators influence the pathogenesis and abscess formation during staphylococcal infections . Here , genetic and pharmacologic approaches unveiled that LTB4 deficiency did not impact neutrophil migration to the site of infection . While MRSA infection led to the formation of a neutrophilic self-confined abscess in WT mice , neutrophils in 5-LO-/- and BLT1-/- infected mice were found within all layers of the dermis and near the epidermis . Importantly , topical LTB4 treatment in 5-LO-/- mice restored abscess formation , which indicates that LTB4 provides neutrophils cues to locate the abscess ( see below ) . Importantly , poor abscess formation could also be due to an excessive bacterial growth that could delay abscess formation in the abscess of LTB4 , but when we infected WT and 5-LO-/- mice with 100 times less bacteria than we used in this study , we detected a small neutrophilic swarm in WT but not in LT-deficient mice . The fact that LTB4 restores abscess and bacterial clearance in 5-LO-/- is of importance , since acquired states of LT deficiency , such as bone marrow transplant , protein-calorie malnutrition , HIV infection that are highly susceptible to S . aureus infection are also characterized by low 5-LO expression and LTB4 production [8] . Therefore , using LTB4 to boost host defense in these groups of patients can be an attractive immunotherapeutic strategy to control infection . We then determined whether LTB4 could play a differential role in different steps of the S . aureus abscess formation and clearance . Initially , we studied whether blocking LTB4 early in the infection could influence bacterial clearance , neutrophil migration and production of inflammatory molecules . Our data show that LTB4 is produced after 3 hours of infection , and BLT1 blockage increased bacterial loads and leukocyte migration but did not influence cytokine and chemokine production . These data suggest an essential role for LTB4 in the establishment of the skin infection . Next , we determined whether LTB4 would alter the synthesis of chemokines and cytokines involved in neutrophil and monocyte chemotaxis during S . aureus skin infection in WT and BLT1-/- at the beginning of the infection ( day 1 ) and when the lesion is cleared in WT mice ( day 9 ) . LTB4/BLT1 axis is required for the synthesis of chemokines that recruits neutrophils ( CXCL1 and CXCL2 ) , monocytes ( CCL2 , CCL8 , and CCL7 ) and T cells ( CCL4 ) at both days 1 and 9 after infection . However , CXCL2 levels were substantially increased in infected BLT1-/- , which could explain why initial neutrophil migration is not affected in these mice . Given the differential regulation of chemokines in BLT1-/- , we speculate that increased CXCL2 is not a compensatory mechanism . Considering that infected BLT1-/- still shows enhanced metalloprotease MMP12 , the alarmin RAGE , IFN-γ and decreased VEGF suggest an active inflammatory process , due to inefficient bacterial clearance . Although , BLT1-/- neutrophils were capable of migrating to the site of injury , indicates that LTB4/BLT1 does not influence the steps involved in neutrophil diapedesis , such as rolling , adherence and transmigration [34] . LTB4 has been shown to act as a signal-relay signal promoting enhanced neutrophil migration towards other chemotactic molecules such as Fmlp [35] . However , the ability of BLT1-/- to be directed to a focal point in the skin was impaired as shown in both our MRSA infection model as well as in a sterile injury model [36] , suggesting LTB4 functions to provide direction towards a chemoattractant , which is necessary to eliminate infectious products or dead tissue . This phenomenon was recently termed neutrophil swarming [36] . It remains to be determined what are the intracellular events triggered by LTB4/BLT1 that provides neutrophil swarming . Here , our data suggest that LTB4 production is required during different phases of the infection , namely the initial recruitment of phagocytes to control infection and when the infection is established by locally amplifying antimicrobial effector functions , as well as the inflammatory milieu . Furthermore , we are providing new evidence that topical ointment containing LTB4 indeed increases neutrophil swarming during MRSA infection , in addition to increased velocity and neutrophil displacement . During the initial hours after infection , it has been suggested that skin-resident cells are responsible for neutrophil recruitment to the site of infection [37–39] . However , the specific role of macrophages in neutrophil migration during skin infection remains to be fully determined . Here , using an animal model that specifically depletes macrophages and monocytes , we unveiled that skin macrophages are required for critical events in MRSA skin infection , namely LTB4 production and neutrophil migration to the site of infection , abscess formation , and bacterial clearance . Although we depleted cells before infection , we cannot rule out whether recruited monocytes are also crucial for initial LTB4 production and neutrophil recruitment . Since we detected these differences as early as 6 hours after infection , we anticipate that skin resident macrophages are dictating early immune response during MRSA skin infection . Also , Feuerstein et al . suggested that MyD88 expression in macrophages is also required for abscess formation . However , the authors used liposomal clodronate that depletes several phagocytes [40] . Interestingly , we have shown that IL-1β/MyD88 actions in neutrophils are necessary for S . aureus abscess formation [41] . Furthermore , we have previously demonstrated LTB4 increases MyD88 expression in macrophages from different organs [10–12] . Whether LTB4/MyD88 axis regulates abscess formation during MRSA skin infection remains to be investigated . Given the fact that LTB4 exhibits pleiotropic effects during MRSA skin infection , a topical ointment containing LTB4 is a promising therapeutic strategy for treating MRSA skin infections . Interestingly , LTB4 enhances the killing of different pathogens by inducing ROS , RNI and antimicrobial peptides in vitro [14 , 28 , 42] . We also have shown that treatment of WT mice with bestatin ( a nonspecific LTA4 hydrolase and aminopeptidase inhibitor [43] ) decreases neutrophil migration and increases bacterial load in after days 1 and 3 after infection [44] . However , the use of bestatin , which also inhibits di-peptidases with inflammatory actions , could potentially influence skin host defense in a manner independent of LTB4 production [43 , 45] . Yamamoto et al . have shown that LTB4 injection increases MRSA clearance and animal survival in a model of peritonitis [15] . Here , we are showing that LTB4 activation of NADPH oxidase is required for microbial clearance and improve lesion during MRSA infection . Furthermore , we observed that topical treatment with an ointment containing LTB4 and the topical antibiotic mupirocin quickly reduced bacterial burden and infection size . We used mupirocin because it is a topical antibiotic used to treat skin infections and only functions locally . There are many potential advantages to using LTB4 in immunotherapeutic protocols as follows: ( 1 ) LTB4 synthesis is easily produced with a high degree of purity; ( 2 ) LTB4 has a short half-life , which allows precision in controlling undesired inflammatory response; ( 3 ) LTB4 is safe to humans and nonhumane primates [42 , 46 , 47]; and ( 4 ) LTB4 is a pleiotropic molecule that amplifies initial anti-MRSA responses by enhancing bacterial recognition and phagocytosis [12 , 48 , 49] , release of ROS [49] , IL-1β levels , and pro-inflammatory responses through MyD88 expression and NFκB activation [49] that culminates in efficient abscess formation . In our preclinical studies , we have identified a previously unknown role of tissue-resident macrophages to promote LTB4 production , which is necessary to enhance structured abscess formation and bacterial clearance . These studies have evident translational importance given the prevalence of skin infections in patients with immunosuppressive diseases that exhibit poor LTB4 production , such as cancer and malnutrition [8] . Therefore , a therapeutic intervention of these groups of patients with an ointment containing LTB4 with concurrent antibiotic therapy is a promising strategy to treat MRSA infections .
For all experiments , the minimum sample size was determined to detect a difference between group means of two times the observed standard error ( SE ) , with a power of 0 . 8 and a significance level of 0 . 05 , using the power and sample size calculator ( http://www . statisticssolutions . com/ ) . On the basis of this , the calculated minimum sample sizes ranged from three to four depending on the experiment . The average sample size for mouse studies was five per group . All samples were randomized but not blinded . Eight—ten-week-old female or male 5-LO-/- ( B6 . 129-Alox5tm1Fun; [50] ) , BLT1-/- ( B6 . 129S4-Ltb4r1tm1Adl/J;[51] , Csf1r-HBEGF/mCherry ) 1Mnz/J ( JAX stock #024046 ) [52] , LysMcre , MMDTR , and strain-matched WT C57BL/6 mice were purchased from Jackson Labs ( Bar Harbor , ME USA ) . MMDTR mice were generated by breeding the Csf1r-HBEGF/mCherry ) 1Mnz/J plus LysMcre mice as previously reported [52] . EGFP-LysM was donated by Dr . Nadia Carlesso ( City of Hope , Duarte , CA , USA ) , and the pIL1DsRed ( donated by Dr . Akiko Takashima , University of Toledo , Toledo , OH , USA , [53] ) . Mice were maintained according to National Institutes of Health guidelines for the use of experimental animals with the approval of the Indiana University ( protocol #10500 ) and Vanderbilt University Medical Center ( protocol #M1600154 ) Committees for the Use and Care of Animals . Experiments were performed following the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals and the US Animal Welfare Act . Mice were infected with MRSA USA300 LAC strain ( ~3 ×106 colony forming units [CFU] ) s . c . in 50 μL phosphate-buffered saline ( PBS ) as we have previously shown[54] . Lesion and abscess sizes were monitored daily and determined by affected areas calculated using a standard formula for the area: ( A = [π/2] × l × w ) [55] . The final concentrations of the ointments were as follows: LTB4 ( 33 . 7 ng– 3 . 37 × 10−6% ) , U75302 ( 0 . 001% ) , apocynin ( 16 . 6% ) , and mupirocin ( 0 . 1% ) , all in 1 g of petroleum jelly ( vehicle control ) . The treatments were applied to the infected area with a clean cotton swab . Mice were treated once a day throughout infection ( ranging from 6 hours to 9 days ) . Punch biopsies ( 8 mm ) from noninfected or infected skin were harvested at different time points and used for determining bacterial counts , cytokine production , RNA extraction , cell isolation , histological analyses , and IHC staining [56] . For bacterial load , skin biopsies were collected at days 1 and 9 post-infection , processed and homogenized in TSB medium , and serial dilutions were plated on TSB agar plates and colonies were counted after 18 h at 37°C . Resident peritoneal macrophages were isolated using ice-cold PBS as previously described [11 , 57] . To isolate bone marrow neutrophils , bone marrow from both tibias and femurs were flushed with PBS with a 26G needle and a 20-mL syringe . Neutrophils were negatively isolated using a MACSxpress Neutrophil Isolation Kit as suggested by the manufacturer ( Miltenyi Biotec , Sunnyvale , CA , USA ) . Resident peritoneal macrophages and bone marrow-derived neutrophils ( 2 × 105/well ) were plated into two 96-well plates with opaque walls and clear bottoms . Cells were cultured in DMEM and pretreated with 10 μM U75302 ( Cayman Chemicals , Ann Arbor , MI ) for 30 minutes or 10 nM LTB4 for 5 minutes before the addition of MRSA-GFP at a multiplicity of infection of 50:1 , as we have previously described [49] . Infected cells were incubated 1 hour to allow phagocytosis , and both plates were washed with warm PBS . The PBS and treated samples were added to the killing plate and incubated for another 2 hours for killing assays . To measure the intensity of GFP fluorescence , extracellular fluorescence was quenched with 50 μL of 500 μg/mL trypan blue , and the GFP fluorescence was quantified using a fluorimeter plate reader . Trypan blue served as blank . A reduction in GFP fluorescence in the killing plate relative to the phagocytosis plate indicated bacterial killing . To deplete monocytes and macrophages , MMDTR mice were treated with 100 ng of DT once a day for 3 consecutive days before MRSA skin infection [52] . The IVIS SpectrumCT ( PerkinElmer , Waltham , MA , USA ) instrument was used to image bioluminescent MRSA and macrophages in the skin of the animal . For bioluminescence imaging , the mice were scanned without emission filtration for 1–4 min/image . Mice were scanned longitudinally once a day for 10 days to monitor the course of MRSA skin infection . For analysis , regions of interest were drawn around each site of infection and the total photon flux ( photons/second ) was measured . To account for background signal resultant from camera thermal noise and emission scatter , a background region was drawn outside the mouse to determine the background signal of each scan . To establish the relationship between bacterial CFU and background-free total photon flux , bacterial standard curves were prepared and imaged by spotting known bacterial CFU onto TSA plates ( for in vitro studies ) or from subcutaneously infected mice ( for in vivo studies ) . By establishing the luminescence and CFU relationship , and fitting these with linear regression , it was possible to quantify the bacterial burden in the skin or each mouse . For DsRed and mCherry fluorescence imaging , in the presence of bioluminescent MRSA , groups of 4 mice were imaged at 6–8 distinct emission wavelengths over the ( excitation: 535 , 570 , 605 ) 560-680nm and ( excitation: 500 , 535 , 570 ) 580-720nm bandwidths , respectively , with an exposure range of 1–5 sec/group . To provide negative controls , WT mice that were DsRed negative ( or mCherry negative ) with and without bioluminescent MRSA were imaged as above , and served as bioluminescent MRSA and auto-fluorescence controls , respectively . Because the total emission in each mouse is a linear combination of DsRed ( or mCherry ) fluorescence combined with MRSA bioluminescence , DsRed ( or mCherry ) the fluorescence emission must be spectrally deconvolved . Individual basis functions for each spectral series were constructed as follows: EAuto ( λ , i ) =EWT ( λ ) ( 1 ) EBL ( λ , i ) =EMRSA ( λ , i ) −EAuto ( λ , i ) ( 2 ) EFL ( λ , i ) =ETotal ( λ ) −EBL ( λ , i ) ( 3 ) Where λ , i , EAuto , EWT , EBL , EMRSA , ETotal , and EFL are the wavelength , subject , autofluorescence emission , wild-type emission ( i . e . no DsRed or mCherry ) , bioluminescence emission , MRSA emission ( i . e . bioluminescence + autofluorescence ) , total emission ( i . e . bioluminescence + autofluorescence + mCherry ) , and mCherry emission only . Wavelength-dependent spectral basis functions were manually constructed and loaded into LivingImage ( PerkinElmer , Waltham , MA , USA ) , where mCherry spatially dependent signals for each pixel were spectrally deconvolved using a multi-linear least squares approach . Once the images were spectrally decomposed , a region of interest was drawn around the mCherry signal and was the average pixel intensity was reported in total radiant efficiency ( [photons/second]/[μW/cm2] ) . Mice were anesthetized with a solution of ketamine/xylazine . A skin flap was created surrounding the infection area , as adapted from [58] . The skin flap was placed in a coverslip-bottomed cell culture dish for imaging and moistened with PBS . The temperature of the mouse was maintained at 36°C with two ReptiTherm pads . Mice were imaged for up to 1 hour . Imaging was performed using an Olympus FV1000-MPE confocal/multiphoton microscope ( Olympus , Tokyo , Japan ) . Analyses were performed using Amaris or FIJI ( Image J ) tracking software . Total RNA was isolated from skin biopsies using lysis buffer ( RLT; Qiagen , Redwood City , CA , USA ) and the cDNA and real-time PCR were performed as previously published [10] using primers indicated in the instructions included with the CFX96 Real-Time PCR Detection System ( Bio-Rad , Hercules , CA , USA ) . Gene-specific primers were purchased from Integrated DNA Technologies ( Redwood City , CA , USA ) . Relative expression was calculated as previously described [10] . Skin biopsy sections were homogenized with a pestle in TNE cell lysis buffer containing 1× protease inhibitor ( Sigma-Aldrich , St . Louis , MO , USA ) and centrifuged to pellet the cellular debris . Multiplex bead array analysis was performed as suggested by the manufacturer and analyzed using a Bio-Rad Bio-Plex MAGPIX multiplex reader . The Web-based tool Morpheus ( https://software . broadinstitute . org/morpheus/ ) was used to generate heat maps . LTB4 was measured using an EIA kit ( Cayman Chemicals , Ann Arbor , MI , USA ) following the manufacturer’s protocols . In all the experiments , protein and lipid concentrations were normalized to the total protein concentration in the tissue , as determined by the Bradford method . Skin biopsies were digested with collagenase and processed to obtain a single-cell suspension as previously described [54] . Single cells were stained with the fluorescent antibodies or CellRox for flow cytometry analyses on the BD LSR II flow cytometer ( BD Biosciences , San Jose , CA , USA ) . Analyses were completed using Flow Jo software . For histological analysis , 8 μm skin sections were stained with Hematoxylin and eosin or Masson’s trichrome blue stain for capsule visualization [16] . Images of tissue sections were visualized and acquired using the Nikon Eclipse Ci and Nikon Ds-Qi2 ( Nikon , Tokyo , Japan ) . For histological analyses , the slides from 8 μm paraffin-embedded skin sections were treated with 10% hydrogen peroxide in distilled water to block endogenous peroxidase activity . Slides were blocked with PBS containing 8% serum . Sections were then incubated with anti-Ly6G/C antibody , anti-F4/80 , followed by a peroxidase-conjugated secondary antibody , color development , and hematoxylin counterstaining . The 5-LO staining was performed using the Vectastain ABC kit ( Vector Labs , Burlingame , CA , USA ) as suggested by the manufacturer , and the 5-LO antibody ( 1:100; Cell Signaling Technology , Danvers , MA , USA ) was incubated for 18 hours at room temperature . Images were analyzed using FIJI software . Negative staining controls were generated by omitting the primary antibody . Slides were visualized , and images were acquired using the Nikon Eclipse Ci and Nikon Ds-Qi2 ( Nikon , Tokyo , Japan ) . The slides from 8 μm sections were incubated in 3% PBS / BSA /Triton X-100 for one hour and incubated with a rabbit anti-5-LO ( 1:100; Cell Signaling Technology ) antibody and goat anti-Ly6G-AlexaFluor488 conjugated ( 1:100; BD Biosciences ) for 1 h followed by sequential washes and incubation with an Alexa-Fluor568 goat anti-rabbit secondary antibody . To image mCherry+ cells in MMDTR mice , biopsy sections were flash frozen , and the slides were prepared on a microtome for immunofluorescence staining . In all circumstances , tissues were stained with 4' , 6-diamidino-2-phenylindole ( DAPI ) as a nuclear counterstain . Slides were imaged on a Leica ( Wetzlar , Germany ) confocal microscope for mCherry , Ly6G , 5-LO and DAPI fluorescence . Slides were visualized and images acquired on the Nikon Eclipse Ci with filters for DAPI , GFP , and Texas Red , using the Nikon Ds-Qi2 camera . The results are shown as the mean ± SEM and were analyzed using the Prism 5 . 0 software ( GraphPad Software , San Diego , CA , USA ) . For comparisons between two experimental groups Student’s t-test was used , and for comparisons among three or more experimental groups , we used one-way analysis of variance followed by Bonferroni multiple comparison tests . The open software Morpheus heatmap program ( https://software . broadinstitute . org/morpheus/ ) was used to generate heat maps . Values of p < 0 . 05 were considered significant . | Staphylococcus aureus skin infection is orchestrated by resident and recruited immune cells . The signals that required for the control of S . aureus infection have been studied . However , most studies have focused on the actions of inflammatory mediators produced after hours post infection and the early events ( seconds to minutes ) that shape innate immune response is poorly understood . Here , we employed novel techniques , along with epistatic and gain of function experimental procedures , to identify critical components involved in the control of MRSA skin infection . Here , we unveiled the role of skin macrophages in the generation of the lipid mediator leukotriene B4 ( LTB4 ) that culminates in optimal chemokine/cytokine production , abscess formation , and bacterial clearance . An ointment containing LTB4 and antibiotics improved skin host defense and abscess formation and also enhanced bacterial clearance . These results suggest that LTB4 could be used locally as an immunotherapeutic agent to strengthen/restore innate immune host activation during MRSA skin infections . | [
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"medical",... | 2018 | Macrophage-derived LTB4 promotes abscess formation and clearance of Staphylococcus aureus skin infection in mice |
Vibrio cholerae O1 is a natural inhabitant of aquatic environments and causes the diarrheal disease , cholera . Two of its primary virulence regulators , TcpP and ToxR , are localized in the inner membrane . TcpP is encoded on the Vibrio Pathogenicity Island ( VPI ) , a horizontally acquired mobile genetic element , and functions primarily in virulence gene regulation . TcpP has been shown to undergo regulated intramembrane proteolysis ( RIP ) in response to environmental conditions that are unfavorable for virulence gene expression . ToxR is encoded in the ancestral genome and is present in non-pathogenic strains of V . cholerae , indicating it has roles outside of the human host . In this study , we show that ToxR undergoes RIP in V . cholerae in response to nutrient limitation at alkaline pH , a condition that occurs during the stationary phase of growth . This process involves the site-2 protease RseP ( YaeL ) , and is dependent upon the RpoE-mediated periplasmic stress response , as deletion mutants for the genes encoding these two proteins cannot proteolyze ToxR under nutrient limitation at alkaline pH . We determined that the loss of ToxR , genetically or by proteolysis , is associated with entry of V . cholerae into a dormant state in which the bacterium is normally found in the aquatic environment called viable but nonculturable ( VBNC ) . Strains that can proteolyze ToxR , or do not encode it , lose culturability , experience a change in morphology associated with cells in VBNC , yet remain viable under nutrient limitation at alkaline pH . On the other hand , mutant strains that cannot proteolyze ToxR remain culturable and maintain the morphology of cells in an active state of growth . Overall , our findings provide a link between the proteolysis of a virulence regulator and the entry of a pathogen into an environmentally persistent state .
The ability of microorganisms to alter their gene expression profiles when transitioning between environments is fundamental for their survival . Vibrio cholerae O1 is a non-obligate pathogen that switches between the human host , where it colonizes the intestinal epithelium , and the aquatic environment where it is found as free-living or attached to biotic and abiotic surfaces [1–4] . Upon entry of V . cholerae into the human host , the expression of its two major virulence factors is induced: the toxin co-regulated pilus ( TCP ) , an essential intestinal colonization factor [5] , and cholera toxin ( CT ) , responsible for the diarrhea associated with the disease [5 , 6] . The expression of these factors is coordinately regulated at the transcriptional level by a virulence cascade involving a number of regulatory proteins [7] . Central to this cascade is the cooperation between two pairs of membrane-localized transcriptional regulators , TcpPH , encoded on the Vibrio Pathogenicity Island ( VPI ) [8] , and ToxRS , encoded in the ancestral genome and also present in non-pathogenic isolates of V . cholerae . These findings indicate that ToxR has roles outside of the human host . Both TcpPH and ToxRS are required to activate the transcription of the master virulence regulator , ToxT [9–12] . ToxT , also encoded on the VPI , directly activates the expression of TCP and CT , as well as other genes [7 , 13 , 14] . Once intestinal colonization and proliferation have taken place , V . cholerae downregulates the expression of the virulence cascade [8 , 15 , 16] . In the classical biotype of V . cholerae , termination of virulence is mediated by proteolysis of the major virulence activator ToxT [9–12 , 17] as well as by the regulated intramembrane proteolysis ( RIP ) of TcpP [18] . RIP is a widely distributed mechanism in both prokaryotes and eukaroytes for responding to extracellular signals and stresses [19–21] . The initial proteolytic event in the RIP of TcpP is catalyzed by a currently unidentified site-1 protease , resulting in a TcpP species that is further degraded by the site-2 metalloprotease RseP ( YaeL ) [18] . The most widely studied example of RIP activates the σE-dependent envelope stress response [22 , 23] . This process involves RseA , a membrane localized anti-σ factor that arrests RpoE . In response to envelope stress , RpoE is released for interaction with RNA polymerase by a RIP event in which RseA is sequentially cleaved by the serine protease DegS at a periplasmic site , and then by RseP at an intramembrane site [24–26] . The downregulation of virulence gene expression late in the infection process coincides with the upregulation of genes that promote detachment of bacteria from the mucosal surface of the intestine and that enhance the survival of V . cholerae when it returns to the environment [27–29] . Regulatory systems involved in controlling the expression of these genes include the quorum sensing regulator HapR [27] , the stationary phase alternative sigma factor RpoS [28] , and the VarS/VarA two component system [29] . Once in the environment , biofilm formation and entry into a dormant state known as viable but nonculturable ( VBNC ) or conditionally viable environmental cells ( CVEC ) , play crucial roles in the survival of V . cholerae by facilitating its environmental persistence within aquatic habitats during periods between epidemics [4 , 30–33] . CVEC are clumps of dormant cells embedded in a biofilm matrix that can be recovered using enriched culturing techniques [31] . Quorum sensing has been implicated in the regulation of CVEC [31 , 34] . Nonetheless , the molecular mechanisms governing entry into VBNC remain to be elucidated . Microarray analysis has revealed that ToxR influences the expression of more than 150 genes in V . cholerae [35] . Besides virulence , regulated genes include those involved in cellular transport , energy metabolism , motility , and iron uptake . In addition , ToxR reciprocally regulates the expression of two outer membrane porins , OmpU and OmpT , in response to the nutritional status of the cell [36–38] . Furthermore , unlike tcpP , toxR expression is not altered under conditions that favor the maximal expression of virulence genes in the laboratory [39 , 40] . In this study , we show that under nutrient limitation at alkaline pH , ToxR levels decrease by a RIP mediated event . This process is dependent upon the metalloprotease RseP , which appears to function as a site-2 protease , and the σE-mediated periplasmic stress response , which likely provides a site-1 protease that contributes to the cleavage of ToxR prior to RseP . We determined that the loss of ToxR , either by proteolysis or genetically , is associated with the entry of V . cholerae into a dormant , nonculturable state that is similar to the VBNC state observed in the natural environment .
The expression of ompU is activated by ToxR in nutrient abundant environments , such as in rich medium [36] or in the host [41] , whereas the expression of ompT is derepressed in nutrient limited conditions , such as in minimal medium [38] or during the late stationary phase of growth [37] . Since the levels of ToxR increase in the presence of nutrients to facilitate ompU expression [38] , we hypothesized that the levels of ToxR would decrease during the growth of V . cholerae in late stationary phase to facilitate ompT expression . To test this , wild-type classical biotype strain O395 was grown in LB medium ( starting pH 7 . 0 , unbuffered ) for 12 and 48 hours . As shown in Fig 1A , the levels of ToxR after 48 hours were significantly reduced compared to the 12 hour culture . The final pH of the 48 hour culture was determined to be 9 . 2–9 . 3 , indicating that the medium became alkaline during growth . To determine whether alkaline pH contributed to the loss of ToxR after 48 hours , V . cholerae was grown for 12 and 48 hours in LB medium with a starting pH of 9 . 3 ( unbuffered ) and also in LB medium buffered at pH 7 . 0 with 100 mM HEPES . As shown in Fig 1A , ToxR was undetectable in cultures grown for 48 hours in LB at pH 9 . 3 , whereas its levels remained high at this pH when grown for only 12 hours . This indicates that , in LB , ToxR appears to start being proteolyzed once the cultures reach both alkaline pH and nutrient limitation . When the medium was buffered at pH 7 . 0 , ToxR levels remained high even after 48 hours ( Fig 1A ) . Thus , in late stationary phase , ToxR levels significantly decrease after 48 hours due to an increase in the pH . As a control we examined the stability of a ToxR-unrelated protein , GbpA , and found that these conditions did not affect the stability of GbpA ( S1A Fig ) [42] . We then determined the dynamics of ToxR proteolysis by measuring ToxR protein levels between 12 hours and 48 hours post-inoculation in LB pH 9 . 3 at 6 hour intervals ( Fig 1B ) . We found that ToxR proteolysis begins around 24 hours of growth at LB pH 9 . 3 ( Fig 1B ) . ToxR becomes undetectable between 42 and 48 hours of culture ( Fig 1B ) . The observation that ToxR levels start decreasing after 24 hours at pH 9 . 3 ( Fig 1B ) suggests that nutrient limitation associated with late stationary phase might influence the levels of ToxR in stationary phase . To assess this , overnight cultures of O395 grown in LB at 37°C were pelleted and resuspended in phosphate buffered saline ( PBS ) at pH 7 . 0 or pH 9 . 3 for 12 hours . As shown in Fig 1C , ToxR levels decreased after only 3 hours in response to nutrient limitation at pH 9 . 3 , whereas they did not significantly decrease in response to nutrient limitation at neutral pH . When overnight cultures were transferred to PBS at pH 8 . 3 , the levels of ToxR started decreasing between 6 and 9 hours ( Fig 1C ) . These results indicate that in late stationary phase , ToxR levels decrease in response to nutrient limitation at alkaline pH . The levels of TcpP have been shown to be controlled by RIP through the site-2 protease RseP [18] . We determined whether the levels of ToxR in response to nutrient limitation at alkaline pH may also be influenced by proteolysis through RseP . As shown in Fig 2A , ToxR was largely undetectable in strain O395 after growth for 48 hours in LB with a starting pH of 7 . 0 or 9 . 3 , whereas the levels of ToxR in the ΔrseP mutant under these conditions were similar to wild-type grown in LB buffered at pH 7 . 0 with 100 mM HEPES . This finding indicates that RseP is involved in the proteolysis of ToxR during late stationary phase at alkaline pH , possibly functioning as a site-2 protease . The ToxR activated porin OmpU functions as an outer membrane sensor responding to damage induced by antimicrobial peptides and triggers activation of the σE-dependent envelope stress response by promoting DegS-mediated cleavage of RseA [43] . We hypothesized that OmpU may also function as a sensor responding to alkaline pH during late stationary phase and induce the activation of the σE pathway , ultimately leading to the proteolysis of ToxR . To address this , we assessed the levels of ToxR in a ΔrpoE mutant after growth for 48 hours in LB with a starting pH of 7 . 0 or 9 . 3 . As shown in Fig 2A , the levels of ToxR in the ΔrpoE mutant under these conditions were similar to the ΔrseP mutant . These findings indicate that both RseP and RpoE play a role in the proteolysis of ToxR during late stationary phase at alkaline pH . Although the proteolysis of ToxR in late stationary phase at alkaline pH depends upon the σE pathway , this process is either independent of the porin OmpU , or OmpU is insufficient to trigger the proteolysis of ToxR since an ompU mutant did not restore ToxR levels under this condition ( S1 Table ) . To further investigate the role of RpoE in the proteolysis of ToxR , we cloned rpoE into an expression vector , pBAD22 , and tested whether ectopic overexpression of rpoE affected the stability of ToxR ( S1B Fig ) . We found that ectopic expression of rpoE does not trigger the proteolysis of ToxR , as ToxR was detectable at every time point we measured ( S1B Fig ) . In addition , we tested the effect of conditions that induce the σE pathway on the stability of ToxR ( S1C Fig ) [43 , 44] . We determined that growth in 3% ethanol or exposure to P2 antimicrobial peptide did not induce proteolysis of ToxR ( S1C Fig ) . Thus , it appears that the σE pathway alone is not sufficient to induce the proteolytic cascade that culminates with the RIP of ToxR . It is possible that a second pathway might act in conjunction with the σE pathway to orchestrate the proteolysis of ToxR , in a similar manner as the Cpx pathway and the σE pathway work in combination in order to monitor the cell envelope in Escherichia coli [45 , 46] . Interestingly , the Cpx two-component system , which partially overlaps with the σE pathway in the periplasmic stress response [46 , 47] does not play a role in the proteolysis of ToxR . In late stationary phase at alkaline pH , deletion of cpxR , a regulator of the pathway , did not restore the levels of ToxR ( S1 Table ) . The involvement of σE in the proteolysis of ToxR raises the possibility that the role of this pathway is to provide a site-1 protease that contributes to the cleavage of its periplasmic domain . Since RseP is known to activate the σE pathway [25 , 26] , the function of RseP in the proteolysis of ToxR may be to activate this pathway , or to also function directly as the site-2 protease of ToxR . To assess this , we generated periplasmic truncations of ToxR that should bypass the requirement for a site-1 protease and assessed the roles of RseP and σE in the proteolysis of these truncations . If RseP functions indirectly in the proteolysis of ToxR the introduction of a ΔrseP mutation into strains carrying the truncations should have no influence on their stability . A periplasmic truncation was generated by introducing a stop codon in ToxR at position 249 ( amber , toxR248 ) . When cells are grown under inducing conditions the production of TCP allows for the formation of microcolonies , clusters of bacterial cells tethered together; this process is dependent on the production of the major pilin subunit TcpA [48] . Due to their size , microcolonies flocculate and form a pellet in a phenomenon known as autoagglutination [5 , 48] . As shown in Fig 2B , the strain carrying toxR248 failed to autoagglutinate , and neither TcpA nor ToxR could be detected by immunoblot ( Fig 2C and2D ) indicating that the truncated protein is proteolyzed . However , introduction of ΔrseP into the toxR248 strain restored autoagglutination ( Fig 2B ) , TcpA production ( Fig 2C ) and produced an intermediate species of ToxR ( Fig 2D ) indicating that RseP influences the stability of the truncated ToxR protein . In contrast , a ΔrpoE mutation in toxR248 failed to autoagglutinate ( Fig 2B ) and did not stabilize the truncated ToxR protein ( Fig 2D ) . These findings indicate that RseP plays a direct role in the cleavage of ToxR when a portion of its periplasmic domain is missing , and suggest that the effect of the σE pathway occurs prior to site-2 proteolysis of ToxR by RseP . The proteolysis of ToxR under nutrient limitation at alkaline pH might provide an advantage to V . cholerae by preventing the expression of genes with roles in nutrient rich environments , such as OmpU , and promoting those with roles in nutrient poor conditions , such as OmpT [36–38] . Under conditions that are not conducive to active growth , such as nutrient limitation , V . cholerae is capable of entering a dormant , nonculturable state referred to as VBNC or CVEC that facilitates its survival and persistence [30 , 31 , 49–51] . We investigated whether the loss of ToxR is associated with the entry of V . cholerae into a nonculturable state . To assess this , the culturability of V . cholerae was measured by plating cultures of V . cholerae O395 between 12 and 48 hours after inoculation on LB medium with a starting pH of 9 . 3 and also in LB medium buffered at pH 7 . 0 with 100 mM HEPES at 6 hour intervals , and determining the colony forming units ( CFUs ) at each time point ( Fig 3A ) . As shown in Fig 3A , the number of CFU/ml of O395 grown in LB 100 mM HEPES do not change over time whereas the number of CFU/ml of cultures grown on LB with a starting pH of 9 . 3 start getting reduced in some cultures around 24 hours of growth with a final CFU count nearly 5 logs lower than the cultures grown in LB buffered at pH 7 . 0 ( Fig 3A ) . Interestingly , the culturability of V . cholerae is reduced more or less in parallel with the proteolysis of ToxR ( Fig 1B ) . These results indicate that growth of V . cholerae to late stationary phase at alkaline pH , defined as ToxR proteolysis inducing ( TPI ) conditions , decreases the culturability of V . cholerae . There appears to be a correlation between the proteolysis of ToxR and the loss of culturability of V . cholerae as the levels of the protein decrease at a similar time point as V . cholerae begins to lose culturability ( Fig 1A and3A ) . We assessed whether a ΔtoxR mutant strain would become nonculturable at a faster rate than the wild-type as , given that it will not produce ToxR from the beginning of the incubation period , it might lose culturability as soon as the suitable conditions are met . We found that , after 18 hours of growth on LB pH 9 . 3 , when the nutrients are becoming scarce as the bacterium reaches mid-stationary phase , ΔtoxR starts losing culturability ( Fig 3B and S2B Fig ) . After 48 hours of growth at LB pH 9 . 3 the number of CFUs recovered ranged between 0 and 102 ( Fig 3B ) , indicating that ΔtoxR becomes nonculturable at a faster rate than the wild-type strain when cultured on LB pH 9 . 3 . On the other hand , the number of CFUs remained relatively constant when ΔtoxR was cultured on LB buffered at pH 7 . 0 with 100mM HEPES , confirming that both alkaline pH and nutrient limitation are required for loss of culturability ( S2 Fig ) . We also determined the culturability of a ΔtoxR strain harboring a plasmid that constitutively expresses toxR ( pVM7 ) [9] . The strain ΔtoxR pVM7 shows a similar number of CFU/ml as the wild-type strain indicating that ectopic expression of ToxR recovers wild-type phenotype in the ΔtoxR strain ( Fig 3B ) . Growth curves for both wild-type and ΔtoxR on LB buffered and LB pH 9 . 3 show a similar pattern , indicating that there is no growth difference between the strains in these conditions ( S2B Fig ) . Furthermore , a similar pattern , ΔtoxR losing culturability faster than wild-type at pH 9 . 3 , was found when the wild-type strain and ΔtoxR were cultured on PBS pH 7 and PBS pH 9 . 3 ( S3 Fig ) . The ΔtoxR strain starts losing culturability as soon as the cultures are transferred to nutrient limiting conditions ( PBS ) at alkaline pH ( S3B Fig ) . The same conditions that trigger the proteolysis of ToxR induce the loss of culturability of V . cholerae ( Fig 1A and 3A ) . Additionally , a ΔtoxR mutant becomes nonculturable at a significantly faster rate than the wild-type strain and its phenotype can be complemented by ectopic expression of ToxR ( Fig 3B ) . To further investigate the relationship between ToxR and the culturability of V . cholerae , the CFUs under TPI conditions were determined for wild-type O395 , ΔtoxR , ΔrseP and ΔrpoE mutants . As shown in Fig 4 , the number of CFU/ml for wild-type , which is able to proteolyze ToxR , was around 104 and the ΔtoxR mutant ranged between 0 and 102 . In contrast , the number of CFU/ml for ΔrseP and ΔrpoE mutants that are unable to proteolyze ToxR was approximately 109 under this condition , similar to the number of CFUs recovered when the wild-type strain was grown on LB buffered at pH 7 . 0 with 100 mM HEPES ( Fig 4 ) . Introduction of the ΔtoxR mutation into the ΔrseP and ΔrpoE backgrounds decreased the CFUs under TPI conditions similar to that of the ΔtoxR mutant alone . ToxR undergoes proteolysis in strains toxR248 and toxR248ΔrpoE ( Fig 2 ) . We found that both strains show a highly reduced number of CFU/ml under TPI conditions , similar to the ΔtoxR strain ( Fig 4 ) . The strain toxR248ΔrseP does not proteolyze ToxR , and an intermediate species of the regulator can be detected for this strain ( Fig 2D ) . Consistently , the number of CFU/ml under TPI conditions for toxR248ΔrseP is similar to that of strains that cannot proteolyze ToxR such as wild-type under buffered conditions ( Fig 4 ) . To further study the association between the loss of ToxR and loss of culturability of V . cholerae , we constructed a fusion strain , toxR-phoA , in which ToxR does not undergo proteolysis under TPI conditions ( S3C Fig ) [52] . The toxR-phoA fusion strain does not lose culturability under TPI conditions and show a similar number of CFUs as the strains that do not proteolyze ToxR ( wild-type on LB buffered , ΔrseP , ΔrpoE , or toxR248ΔrseP ) ( Fig 4 ) . Thus , the absence of ToxR , either by proteolysis or genetically , appears to be required for the loss of culturability of the strains . To determine whether the decreased culturability of the wild-type and some of the mutants under TPI conditions is due to their entry into a dormant state and not cell death , the viability of the cells in the cultures in Fig 4 were examined using the LIVE/DEAD BacLight Bacterial Viability and Counting Kit . This kit allows for the discernment between viable cells ( green ) and dead cells ( red ) using fluorescent microscopy . The entry of V . cholerae into a dormant state is associated with loss of culturability ( Fig 4 ) , maintenance of viability ( green cells ) , and a change in its morphology from elongated rods to round , coccoid-shaped cells [53 , 54] . Dead cells ( O/N HK ) can be seen as red under fluorescence ( F ) , whereas cells from an overnight culture ( O/N ) are green and elongated ( F and DIC ) ( Fig 5 ) . We found that when grown in LB pH 7 . 0 buffered with 100 mM HEPES ( 48h Buff ) the wild-type shows a morphology and viability similar to O/N ( Fig 5 ) , indicative of a culturable state . Under TPI conditions ( 48h pH 9 . 3 ) , the cells remain viable and alive ( green ) and their morphology changes to a coccoid form , indicative of entry into a dormant state ( Fig 5 ) . The viability and morphology of the mutant strains tested in Fig 4 is consistent with their culturability ( Fig 6 ) . The strains that cannot proteolyze ToxR ( ΔrseP , ΔrpoE , toxR248ΔrseP , and toxR-phoA ) show a similar morphology and viability to cells in O/N cultures ( Figs 5 and 6 ) , whereas the mutants that do not encode ToxR ( ΔtoxR , ΔrsePΔtoxR , and ΔrpoEΔtoxR ) or proteolyze it ( toxR248 and toxR248ΔrpoE ) are viable and round ( Fig 6 ) . These findings indicate that in V . cholerae the loss of ToxR is associated with entry into a dormant state . V . cholerae is classified into more than 200 serogroups , however , only the O1 serogroup causes epidemic cholera . V . cholerae O1 is further classified into two biotypes , classical and El Tor . Strain O395 , where previous studies regarding termination of virulence had been made , belongs to the classical biotype [17 , 18] . Strains of the El Tor biotype are the source of the current pandemic of cholera and were recently shown to enter VBNC differentially when compared with classical [55] . We determined the effect of TPI conditions in the El Tor biotype strain N16961 . We found that severely reduced levels of ToxR in late stationary phase were also observed with the El Tor biotype strain N16961 ( S4A Fig ) . In this strain , it was necessary to incubate for 72 hours at pH 9 . 3 to visualize complete loss of ToxR by immunoblot . For N16961 , the number of CFU/ml of cultures grown for 72 hours in LB with a starting pH of 9 . 3 was over 4 logs lower than the number of CFU/ml of cultures grown in LB buffered at pH 7 . 0 with 100mM HEPES ( Fig 4B ) . We also found that when grown in LB pH 7 . 0 buffered with 100 mM HEPES , N16961 remains viable ( green ) and elongated ( S4C Fig ) , indicative of a culturable state whereas under TPI conditions , the cells are viable and round ( S4C Fig ) , indicative of entry into a dormant state . V . parahaemolyticus is an intestinal pathogen that causes bloody diarrhea and is generally transmitted through the consumption of raw or uncooked fish . Recently , Whitaker et al showed that ToxR is required for intestinal colonization of V . parahaemolyticus in the adult murine model [56] . V . parahaemolyticus is known to enter VBNC under starvation conditions [57] . We determined whether the entry of V . parahaemolyticus into dormancy was also associated with loss of ToxR . Unexpectedly , V . parahaemolyticus cannot grow on LB with a starting pH of 9 . 3 , however , like V . cholerae , it also alkalinizes the pH of the media during growth . Nonetheless , we found that ToxR undergoes proteolysis in V . parahaemolyticus RIMD2210633 when the bacterium is cultured on LB pH 7 . 0 unbuffered for 48 hours ( Fig 7A ) . Furthermore , the bacterium loses culturability and adopts a viable coccoid form under unbuffered conditions and nutrient limitation ( Fig 7B and 7C ) . Our data suggests that entry into dormancy might be mediated by ToxR proteolysis among other species of the family Vibrionaceae encoding ToxR .
The mechanisms by which V . cholerae terminates virulence and prepares for entry into the aquatic environment have recently begun to be elucidated . In this study , we found a link between the loss of virulence regulator ToxR and entry of V . cholerae into a nonculturable , dormant state . Unlike TcpP , which functions primarily in virulence gene regulation , ToxR is present in non-pathogenic strains of V . cholerae and other members of the family Vibrionaceae , indicating it has roles in addition to virulence . The association between the proteolysis of ToxR and entry into a dormant state sheds new light on the function of ToxR outside of the human host and the molecular mechanisms for entry into an environmentally persistent state . We show here that ToxR in V . cholerae O395 undergoes proteolysis in late stationary phase ( by 48 hours ) as the medium is depleted for nutrients and becomes alkaline . That alkaline pH decreases the stability of ToxR is interesting in light of the fact that ToxR mediated activation of ompU expression plays a protective role for V . cholerae during organic acid stress [58] . This suggests that upregulation of ToxR repressed genes , such as ompT , once ToxR has been proteolyzed may protect V . cholerae under alkaline stress . The conditions identified here that induce the proteolysis of ToxR appear to be specific since we examined a wide variety of other potential stimuli and none were found to influence the stability of ToxR in V . cholerae . The finding that RseP , the site-2 protease that influences the levels of TcpP through RIP [18] , also influences the levels of ToxR in response to nutrient limitation at alkaline pH raised the possibility that ToxR might be subject to RIP . RIP is a mechanism commonly used by bacteria to rapidly adapt to changes in their environment that are important for their survival and establishment [19 , 20] . One of the best-studied examples of RIP activates the expression of the stress response genes associated with the σE pathway [22 , 23] . Other important processes regulated by RIP include sporulation [59] , cell division [60] , pheromone production [61] , quorum sensing [62] and biofilm formation [63 , 64] . We also found a link between the σE dependent stress response pathway and the proteolysis of ToxR . The σE pathway has been shown to play important roles in the virulence of V . cholerae and other Vibrio species and is induced under alkaline pH in Yersinia pseudotuberculosis [44 , 65–68] . The role of RpoE in the proteolysis of ToxR raised the possibility that RseP might not function directly as the site-2 protease of ToxR but instead might act indirectly through its ability to activate the σE pathway . To assess this , a periplasmic truncation of ToxR that would bypass the requirement for a site-1 protease , and which has been previously shown to decrease ToxR stability , was analyzed [52 , 69] . This truncation was found to be proteolyzed in an RseP-dependent , RpoE-independent manner , indicating that RseP plays a direct role in the proteolysis of ToxR . We have thus far been unable to identify any other factors that play a role in the proteolysis of ToxR . Deletion of ompU , encoding a protein that acts as an outer membrane sensor that responds to damage induced by antimicrobial peptides and triggers activation of the σE pathway [43] did not restore the levels of ToxR in late stationary phase at alkaline pH . This led us to determine whether the σE pathway was sufficient to trigger the proteolysis of ToxR . Given that ectopic expression of rpoE and induction of the σE pathway are not sufficient to trigger the proteolysis of ToxR , it appears that a second pathway might work synergistically with the σE pathway in order to initiate the RIP of ToxR . Deletion of Cpx , which partially overlaps with the σE pathway did not restore wild-type levels of ToxR under TPI conditions [46 , 47] . RpoS , a stationary phase sigma factor , which has been shown to influence the culturability of classical biotype V . cholerae [55] , did not influence the levels of ToxR in late stationary phase at alkaline pH . DegS , the site-1 protease involved in activation of the σE pathway in E . coli , also did not influence the proteolysis of ToxR , nor did the σE-regulated proteases DegP or VC0554 ( S1 Table ) [70] . It seemed that the proteolysis of ToxR under nutrient limitation at alkaline pH might provide an advantage to V . cholerae by increasing its ability to survive in nutrient depleted environments outside of the host . Bacteria have evolved a variety of adaptive responses that allow them to survive when conditions are not conducive to active growth . One such response is their ability to enter a dormant state where they remain viable , but are no longer culturable [71 , 72] . We found that under nutrient limitation at alkaline pH V . cholerae becomes nonculturable , and this occurs as the levels of ToxR are reduced . The cells remained viable as they became nonculturable , consistent with their entry into a dormant state [53] . Nutrient limitation , along with other factors such as temperature or salinity , have been previously shown to influence entry of V . cholerae into VBNC [51 , 72–74] . To our knowledge this is the first time that alkaline pH has been found to affect the entry of V . cholerae or other member of the Vibrionaceae into a dormant state . From our data it can be gleaned that V . cholerae has evolved a tightly regulated response to avoid premature proteolysis of ToxR . As our results show , the presence of both nutrient limitation and alkaline pH are required to initiate the proteolysis of ToxR and the changes associated with it . Furthermore , short-term exposure to these conditions does not immediately trigger the proteolytic cascade of ToxR , indicating that the cells possess mechanisms that prevent untimely proteolysis of the virulence regulator . Given the drastic metabolic changes associated with entry into dormancy , it is possible that cells avoid proteolysis of ToxR when nutrient limitation and alkaline conditions are only transient . We found a time difference in the proteolysis of ToxR between V . cholerae N16961 and O395 . While ToxR cannot be detected in O395 cultures grown in LB for 48 hours at pH 9 . 3 , ToxR can still be detected , even though at significantly reduced levels , in N16961 cultures under the same conditions . Nonetheless , ToxR becomes undetectable when the cultures of N16961 are incubated for a longer time . The loss of ToxR was also associated with entry into a dormant state of N16961 . Our results are consistent with previous findings showing differential entry into dormancy between classical and El Tor biotypes [55] . In the aquatic environment , a significant proportion of toxigenic V . cholerae can be found as VBNC [30–32] . Thus , in the wild-type , the proteolysis of ToxR is associated with the formation of a dormant state that appears similar to the VBNC/CVEC state that is observed in the natural environment . Further work is needed to determine how V . cholerae can be recovered from this dormant state and whether this recovery is associated with the presence of ToxR . V . parahaemolyticus , an intestinal pathogen that causes a bloody diarrhea , is known to enter VBNC under starvation conditions [57] . Interestingly , ToxR is also required for intestinal colonization of this pathogen [56] . We found that ToxR undergoes proteolysis in V . parahaemolyticus under alkaline conditions and nutrient limitation . Furthermore , similar to V . cholerae , the bacterium also loses culturability and adopts a viable coccoid form under these conditions . ToxR is encoded among other members of the family Vibrionaceae [75–78] . Our data suggests that the association between the loss of ToxR and entry into VBNC might be a widespread phenomenon among those species . In Photobacterium profundum ToxR becomes undetectable by immunoblot when cultured at high pressure ( 272 atm ) relative to atmospheric pressure ( 1 atm ) [79] . It has been suggested that ToxR is proteolyzed under high pressure and its loss may serve to increase the production of the porin OmpH ( analogous to that of OmpT ) , which has a larger channel than OmpL ( analogous to OmpU ) [79 , 80] . This trait could be important in the deep-sea where nutrients are particularly scarce [79 , 80] . Furthermore , strains of P . profundum with mutations in an rpoE-like locus are pressure sensitive , suggesting that RpoE might also play a role in the proteolysis of ToxR in this species [81] . It remains to be determined at which stage of the V . cholerae life cycle ToxR undergoes proteolysis . It is tempting to speculate that the proteolysis of ToxR occurs during the late stages of infection , as a consequence of the resulting nutrient limitation and alkaline pH in the intestine [27 , 29 , 82] . The proteolysis of ToxR at this stage in the life cycle of V . cholerae would contribute to the termination of virulence and upregulate genes repressed by ToxR , such as ompT , that play a role in environmental survival . Consistent with this hypothesis is the finding that several genes involved in glycerol metabolism , which are downregulated by ToxR [35] , have been found to play a role in survival in pond water [27] . Unlike ToxR , proteolysis of TcpP does not appear to be involved in the entry into VBNC since cultures transferred from inducing to non-inducing conditions , which triggers proteolysis of TcpP , do not lose culturability and keep growing in the media they are transferred to ( S5 Fig ) . A sequential model for the RIP of ToxR during late stationary phase is shown in Fig 8 . In the early stages of colonization , when nutrients are abundant , ToxR upregulates the expression of genes such as ompU and those involved in virulence and downregulates the expression of genes such as ompT with roles in environmental survival . During the late stages of colonization , as nutrients become depleted and the environment becomes alkalinized , ToxR is proteolyzed . This occurs due to activation of the σE pathway via sequential degradation of RseA by either DegS or another site-1 protease and RseP , the site-2 protease , which releases RpoE to activate its regulated genes . It is possible that one of these genes may encode a site-1 protease that cleaves a periplasmic portion of ToxR , however , at least a second protease/system appears to be necessary for the site-1 proteolytic event to occur . Next , RseP cleaves at an intramembrane site within ToxR leading to full proteolysis of the regulator . This process induces and prevents , respectively , expression of ToxR repressed and activated genes , providing an advantage in the environment . The loss of ToxR ultimately leads to entry into dormancy . This study has identified a RIP cascade involving RseP and RpoE that is responsible for the proteolysis of ToxR under nutrient limitation at alkaline pH . Further work is necessary in order to fully understand the pathway . This includes the identification of the site-1 protease/s that cleave ToxR as well as other genes regulated by ToxR that provide an advantage under this condition .
V . cholerae O395 ( O1 classical ) , V . cholerae N16961 ( O1 El Tor ) , and V . parahaemolyticus RIMD2210633 were the wild-type strains for this study . E . coli S17-1λpir [83] was used for both cloning purposes and conjugation with V . cholerae . Unless otherwise indicated , cultures were grown O/N in LB at 37°C on a rotary shaker . To induce expression of V . cholerae virulence genes , cultures were grown in LB pH 6 . 5 at 30°C . Antibiotics were used at the following concentrations: ampicillin ( Ap ) , 100 μg/ml; gentamicin ( Gm ) , 30 μg/ml; streptomycin ( Sm ) , 1 mg/ml . Deletion and substitution mutations were constructed by PCR amplifying two approximately 500 bp fragments of DNA upstream and downstream of the region of interest . For the substitutions , nucleotide changes were introduced into the primers . After amplification , the inserts were digested with the appropriate restriction enzymes , ligated into pKAS32 [84] and electroporated into E . coli S17-1λpir [83] . The various mutations were then transferred into V . cholerae by allelic exchange [84] . V . cholerae classical biotype contained plasmid pMIN1 [85] conferring Gmr as a counter-selection for conjugation . DNA sequencing was used to confirm the correct deletion or mutant sequence in the V . cholerae genome . The sequences of the primers used in this study are available upon request . Whole cell protein extracts were prepared from cultures grown in LB pH 6 . 5 at 30°C . Protein concentration was quantitated using Pierce BSA Protein Assay quantitation kit from Thermo Scientific . Protein samples were normalized and an equal amount of protein was loaded per well . The extracts were subjected to SDS-PAGE on 16% Tris Glycine gels ( Invitrogen ) and transferred to nitrocellulose using iBlot ( Invitrogen ) . The membranes were blocked O/N in Tris-Buffered Saline , 3% BSA . Primary antibodies were diluted 1:10 , 000 in TBST ( Tris-Buffered Saline , 0 . 5% Tween-20 ) . Membranes were incubated with primary antibody for 2 hours at room temperature . After incubation , the membranes were washed with TBST four times . Goat anti-rabbit secondary antibodies ( BioRad ) were diluted 1:10 , 000 in TBST and incubated for 30 minutes at room temperature . The membranes were washed 4 times with TBS ( Tris-Buffered Saline ) . Reactive protein bands were detected via ECL ( Amersham ) . From each bacterial suspension , a 1 ml aliquot was centrifuged at 10 , 000 rpm for 1 min and the pellet was resuspended in phosphate buffered saline ( PBS ) twice . 1 ml of the mixture was then transferred to a 50 ml centrifuge tube with 24 ml PBS , and was centrifuged at 7 , 840 rpm for 10 minutes . The pellet was resuspended in 10 ml PBS , and a 1 ml aliquot was stained with a 3 μl mixture ( 1:1 ) of SYTO9 and propidium iodide ( PI ) nucleic acid stain ( Molecular Probes , OR ) . After incubation in the dark for 15 min at 25°C , the stained cells were mounted on a glass slide and low-fluorescence immersion oil was added on the cover slide . The cells were then examined with a Zeiss Ax-iovert inverted microscope , and pictures were taken with AxioVision microscopy software ( Zeiss ) . Samples were serially diluted and 5 μl of each dilution was plated on LB . Plates were incubated overnight at 37 °C , and colony forming units were counted . 1 ml of initial culture was plated for those strains where no colonies were recovered after plating 5 μl . Values were plotted using Prism software . The bars represent the mean of at least four independent experiments and the error bars indicate the standard deviation . | Non-obligate bacterial pathogens must alter their gene expression profiles when transitioning between environments . Vibrio cholerae is a natural inhabitant of aquatic ecosystems and the etiological agent of the severe diarrheal disease , cholera . Its virulence gene regulation is controlled by a complex transcriptional cascade involving a membrane-localized regulator termed ToxR . Here we show that ToxR undergoes proteolysis under nutrient limitation at alkaline pH and this loss is associated with the entry of V . cholerae into a dormant state , similar to that found in its natural environment between epidemics . Thus , to our knowledge , we provide the first evidence of a link between the proteolysis of a virulence regulator and the entry of a bacterial pathogen into an environmentally persistent state . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Proteolysis of Virulence Regulator ToxR Is Associated with Entry of Vibrio cholerae into a Dormant State |
Advances in high-throughput DNA sequencing technologies have determined an explosion in the number of sequenced bacterial genomes . Comparative sequence analysis frequently reveals evidences of homologous recombination occurring with different mechanisms and rates in different species , but the large-scale use of computational methods to identify recombination events is hampered by their high computational costs . Here , we propose a new method to identify recombination events in large datasets of whole genome sequences . Using a filtering procedure of the gene conservation profiles of a test genome against a panel of strains , this algorithm identifies sets of contiguous genes acquired by homologous recombination . The locations of the recombination breakpoints are determined using a statistical test that is able to account for the differences in the natural rate of evolution between different genes . The algorithm was tested on a dataset of 75 genomes of Staphylococcus aureus and 50 genomes comprising different streptococcal species , and was able to detect intra-species recombination events in S . aureus and in Streptococcus pneumoniae . Furthermore , we found evidences of an inter-species exchange of genetic material between S . pneumoniae and Streptococcus mitis , a closely related commensal species that colonizes the same ecological niche . The method has been implemented in an R package , Reco , which is freely available from supplementary material , and provides a rapid screening tool to investigate recombination on a genome-wide scale from sequence data .
Recombination , the integration of foreign DNA in the chromosome of an acceptor cell , is one of the major evolutionary forces in bacterial species . Recombination can be mediated by viral infections [1] , direct cell-to-cell contact [2] or transformation , when exogenous DNA is up-taken from the environment [3] . Homologous recombination occurs through the replacement of genomic segments with the homologous DNA from a donor of the same species , or from another , often closely related , species . Since the efficiency of RecA-mediated recombination decreases with increasing sequence divergence [4] , recombination events are far more likely to occur between closely related DNA sequences , although homologous recombination is possible also when the sequence identity between the recipient and donor strains is as small as 70% [5] . In general , the incoming DNA must contain regions of high similarity to the recipient genome of length comprised between 25 and 200 bp to initiate DNA pairing and strand exchange [6] . Homologous recombination may involve whole genes or even larger segments . Whole genome sequencing has shown that homologous recombination is frequent in Streptococcus pneumoniae [7] , [8] , with a mean length of the insert of approximately 6 . 3 kb [9] . Exchanges of much larger DNA fragments occur in several other bacterial species . The distribution of Single Nucleotide Polymorphisms ( SNPs ) showed that isolates of Streptococcus agalactiae and Clostridium difficile can recombine DNA segments exceeding 300 kb with unrelated strains of the same species [10] , [11] . Evidence of recombination involving the exchange of large chromosomal elements has been found in Staphylococcus aureus , involving the acquisition of sequences up to 557 kb [12] . These processes represent a major source of genomic diversity and are important in driving the emergence of clonal complexes and hypervirulent strains [10] , [11] , [12] . Numerous approaches have been developed to measure the frequency of recombination and to determine the chromosomal locations of the inserted sequences . Parametric methods estimate the recombination rate ρ = 4Ner ( where Ne is the effective population size and r is the per-base recombination rate ) from the decline of linkage disequilibrium ( the non-random association between alleles at different loci ) with increasing distance along the chromosome [13] , [14] by applying Markov Chain Monte Carlo methods [15] within the framework of coalescent theory [16] , [17] . Hudson [18] developed a flexible , ad hoc method for estimating ρ by combining the coalescent likelihoods of each haplotype configuration for all pair-wise comparisons of segregating sites . Comparative methods that take into account the effects of recombination events on base composition , codon usage and base identity are the most common among non-parametric methods [19] , [20] . Finally , phylogenetic methods infer recombination by comparing phylogenies from different parts of the genome , based on the assumption that high level of congruence among trees correlates with a lower frequency of recombination while little or no congruence is related to a higher rate of recombination [21] , [22] , [23] . Many of these algorithms for recombination detection have been combined in a single software package [24] , [25] . These methods , although very accurate , are applicable only to relatively short and recently diverged sequences , for which an accurate multiple alignment is available . Here , we introduce a novel method that , using a discrete filtering procedure of the gene conservation profiles in a panel of unrelated strains , identifies sets of contiguous genes likely acquired by homologous recombination . Due to its modest requirements in terms of computational resources , the method can be applied to large panels of complete genome sequences . The method was able to confirm known events of recombination involving genomes of S . aureus , accurately detecting the donor genomes and the genomic positions of the recombinant sequences . Additionally , the algorithm identified previously undetected recombination events in the genomes of S . aureus and S . pneumoniae . The filtering of sequence conservation data makes it feasible to also detect inter-species recombination events , where methods based on multiple sequence alignments are more difficult to apply . The method , implemented in the package Reco , provides a rapid and lightweight screening tool to investigate recombination on a genome-wide scale from sequence data .
S . aureus . A collection of 75 genomes of S . aureus ( 70 publicly available from NCBI and 5 newly sequenced ) has been created including 71 strains isolated from humans and 4 strains isolated from cattle , sheep and swine . A complete list is reported in Table S1 and additional information on the newly sequenced genomes are reported in Table S2 . Streptococci . A collection of 44 complete and draft genomes of S . pneumoniae , 4 genomes of Streptococcus mitis , 1 genome of Streptococcus oralis and 1 genome of Streptococcus infantis were downloaded from NCBI . A complete list of strains is reported in Table S3 . Clonal complexes were defined running eBurst [26] on the MLST databases for S . aureus and S . pneumoniae downloaded from the MLST website ( http://www . mlst . net ) in November 2010 . In MLST , a Sequence Type ( ST ) is uniquely determined by the allelic profile at seven loci , i . e . internal fragments of the following genes arcC , aroE , glpF , gmk , pta , tpi , yqiL for S . aureus , and aroE , gdh , gki , recP , spi , xpt , and ddl for S . pneumoniae . Clonal Complexes ( CCs ) are groups of STs that share a recent common ancestor , defined by the eBURST algorithm by partitioning the MLST data set into groups of single-locus variants ( SLVs ) , i . e . , profiles that differ at 1 of the 7 MLST loci . This partitioning associates each ST with one CC and identifies the most likely founder ST , which is defined as being the ST with the greatest number of SLVs within the CC . Computed CCs were named after the ST of the predicted founder . For an example , see Fig . S1 , where we report the structure of CC8 of S . aureus . Phylogenetic analysis of the complete genome sequences has been performed using Mega4 [27] . The complete genome sequences have been aligned using Mauve [28] . From the region of the multiple genome alignments that are common to all sequences ( the core genome ) we have extracted the polymorphic sites , from which distance matrices were computed applying the Maximum Composite Likelihood Method implemented in Mega4 . The trees were computed using the Neighbor Joining algorithm with 1000 bootstrap replicates . The genes of each strain in the collection of 75 genomic sequences of S . aureus and of the 50 streptococci were aligned using FASTA version 3 . 5 [29] against all other strains in the same collection . Orthologs were identified using the reciprocal-best-hit algorithm with lower bounds of 75% identity on 75% of the sequence length . In general , other choices are possible . The only requirement of the method is that there is a 1-to-1 correspondence between the genes in each genomic sequences . In the case of nearly identical reciprocal-best-hits the regions harboring these should be treated separately . Being based on comparison between genes , the algorithm does not distinguish if a gene is located in different regions in the different strains . This feature allows to study recombination events also in cases where a complex series of rearrangements occurred after the recombination event , and to use directly data from Next Generation sequencers , where the assembly of long contigs can be problematic . However , where possible , the user should directly check the position of the genes involved in the putative recombination on the genomic sequences . Using these data , for each genome we computed the identity matrix , where is the pair-wise percentage of identity of the n-th gene with the homologous gene in the m-th genome . The purpose of the algorithm is the identification of recombination events affecting groups of adjacent genes in a genomic sequence . In the following , we will define as “recombinant” the strain ( s ) containing the recombinant segment , as “major parent” the strain ( s ) contributing the genetic backbone of the recombinant strain , and as “minor parent” the strain ( s ) contributing the sequence that was inserted into the backbone by the recombination event . The identification of recombination events affecting more than one gene has to face two obstacles , which are related to the age of the event itself , i . e . the amount of time since the event occurred , and the subsequent evolution of the sequence . Neighboring genes , coding for proteins with different functions and experiencing widely different selective pressures , can evolve at different rates , progressively erasing the phylogenetic signals of recombination . Moreover , later recombination events can superimpose on older ones , resulting in fragmented patterns , which are difficult to reconstruct . To overcome these difficulties , we designed a digital filter that first performs a smoothing of the conservation data of neighboring genes , and then reduces the smoothed data to a binary scale . Using this filter , the fragmented pattern is averaged out and the signals from the events larger then a given length scale are enhanced . Since this scale is determined by a free parameter of the algorithm ( the sliding window size l , see below ) , using different values of the parameters can lead to a clearer picture of the signal present in the data . The discretization is based on the gene-specific distribution of sequence conservation , and is therefore independent from the rate of evolution of single genes . The procedure can be schematically divided into the following steps:
S . aureus is a major cause of nosocomial infections , including bacteremia , metastatic abscesses , septic arthritis , endocarditis , osteomyelitis , and wound infections . However , its pathogenic mechanisms have not yet been fully elucidated . In particular , the factors that render certain strains , such as the methicillin resistant ( MRSA ) strain TW20 , highly transmissible and invasive have not yet been identified [12] . Horizontally acquired DNA could represent a critical element of the evolution and acquisition of virulence mechanisms in S . aureus . Although the estimated rate of recombination is lower than in other pathogenic bacteria like Neisseria meningitidis and S . pneumoniae [22] , events of recombination involving unusually large portions of the genome have been identified [32] . To test the effectiveness of our method , we have analyzed a collection of 75 genomes of S . aureus ( 70 available in the public domain and 5 sequenced for this study , Table S1 ) , identifying several recombination events , some of which had not been reported before . S . pneumoniae is the causative agent of several human diseases , which include chronic otitis media , sinusitis , pneumonia , septicemia , and meningitis . S . pneumoniae is a naturally competent organism that is known to easily transfer genetic material both within the species and from closely related species . Despite evidences of extensive recombination , it has recently been shown that in a phylogenetic analysis using whole genome sequences , strains of the same ST or CC always form a monophyletic branch [7] , suggesting that recombination events are not able to destroy the phylogenetic signal contained in whole genome sequences . We analyzed a dataset of 50 genomes including S . pneumoniae , S . mitis , S . oralis and S . infantis , to identify putative recombination events .
Population genetic studies on many bacterial species , such as N . meningitidis , S . aureus and S . pneumoniae , have provided extensive evidence of the exchange of genetic material amongst unrelated strains . Knowledge of basic parameters , including the population size , as well as the mutation , recombination and migration rates might help us to predict the extent to which genes are exchanged amongst strains within the same population and between geographically separated populations of a species . In particular , for pathogenic microorganisms this information might help us to understand the dynamics of drug resistance spread , the evolution of vaccine escape mutants , and , more generally , the evolution of pathogenicity . Several methods for the identification of recombination events in sequence data are present in the literature . However , these methods are computationally demanding and difficult to use on whole genome sequences . On the other hand , the rapid diffusion of Next Generation Sequencing technologies requires the development of lightweight , user-friendly algorithms and tools for sequence analysis . Here we presented a new method to identify recombination events in genomic sequences that can be readily applied to large sequence datasets . We have applied the algorithm to two pathogens , S . pneumoniae and S . aureus that are reported to have widely different propensity to exchange genetic material [7] , [40] . Although many studies have indicated that S . aureus has a low recombination rate [41] , we found evidences of several recombination events , also involving large portions of the genome . Except for the large chromosomal replacement found in ST239 , most of these events involved highly mobile elements , particularly phage encoding islands . Differently , most of the recombinant regions identified in S . pneumoniae and its related species contained surface proteins or virulence factors suggesting that these regions are exchanged more frequently than others , possibly due to the selective pressure from the host immune system and confirming that genes involved in DNA replication , transcription and translation are less likely to be horizontally transferred [42] . We found one recombination event potentially including the capsular biosynthesis locus of Serotype 1 strains . Interestingly , both the donor and acceptor strains are of Serotype 1 . It had already been noted that , differently from other serotypes , the Serotype 1 capsule is found only in a group of clonally related strains [7] . This finding could be due to a general inability of Serotype 1 strains to participate to the exchange of genetic material , either as donor or as acceptor . The event identified here rather suggests the existence of a unknown barrier to recombination with strains of unrelated Serotypes , possibly due to the peculiar lifestyle of Serotype 1 strains , that are very rarely carried and are commonly retrieved only from invasive disease [43] . Extending the analysis to the S . pneumoniae-S . mitis complex we identified an example of interspecies recombination that involved contiguous genes that were transferred and maintained in the host genomes . While all genes involved in this event are functional in S . pneumoniae , many of them were pseudogenes in S . mitis , suggesting that this region was recently imported into S . mitis from S . pneumoniae and that genes that do not confer a selective advantage to the former are in the process of being purged . It has been estimated that as much as 30% of the genetic diversity of S . pneumoniae could be attributed to homologous recombination with S . mitis [7] , and the extent of genetic exchange within the S . mitis-S . pneumoniae complex has lead to the hypothesis that S . mitis is the genetic reservoir of S . pneumoniae [39] . The rate of homologous recombination varies greatly between different species [44] , and between different lineages of the same species [45] . The effects of homologous recombination on the evolution of bacterial species are profound , and far from being fully understood . The advent of Next Generation Sequencing technologies is providing an unprecedented amount of sequence data that , due to their complexity , cannot be analyzed using available methods . We introduced a simple algorithm that is able to handle large amounts of data and could provide a rapid screening tool to globally investigate recombination from sequence data . | The extent to which recombination occurs in natural populations is either unknown or controversial but it is widely accepted that recombination plays a crucial role in the evolution of many bacterial species . Numerous methods have been developed for the investigation of recombination events , but most of them require expensive computations and are applicable only to a limited number of genomes or to short nucleotide sequences . Here we present a new algorithm designed to identify recombination events affecting a group of adjacent genes . The procedure is based on the comparison of gene sequences and requires as input the matrix of gene conservation of a test genome against a group of reference genomes . The method is fast , and has minimal computational requirements . Therefore , it can be applied to datasets composed of a large number of complete genomes , and can be easily adapted to analyze data directly from high-throughput sequencing projects . We applied the algorithm to a dataset of S . aureus and streptococcal genomes and we found evidence of yet undetected inter and intra-species recombination events , suggesting that the use of Reco will shed new light on the evolution of bacterial species , and provide important information to improve classification criteria of bacterial species . | [
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] | 2012 | A Novel Computational Method Identifies Intra- and Inter-Species Recombination Events in Staphylococcus aureus and Streptococcus pneumoniae |
It is well known that inbreeding increases the risk of recessive monogenic diseases , but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia . One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity ( ROH ) in genome-wide single nucleotide polymorphism arrays . Using data for schizophrenia from the Psychiatric Genomics Consortium ( n = 21 , 868 ) , Keller et al . ( 2012 ) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous ( β = 16 . 1 , CI ( β ) = [6 . 93 , 25 . 7] , Z = 3 . 44 , p = 0 . 0006 ) . Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium ( n = 39 , 830 ) . Using the same ROH calling thresholds and procedures as Keller et al . ( 2012 ) , we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data , although the effect was in the predicted direction , and the combined ( original + replication ) dataset yielded an attenuated but significant relationship between Froh and schizophrenia ( β = 4 . 86 , CI ( β ) = [0 . 90 , 8 . 83] , Z = 2 . 40 , p = 0 . 02 ) . Since Keller et al . ( 2012 ) , several studies reported inconsistent association of ROH burden with complex traits , particularly in case-control data . These conflicting results might suggest that the effects of autozygosity are confounded by various factors , such as socioeconomic status , education , urbanicity , and religiosity , which may be associated with both real inbreeding and the outcome measures of interest .
Close inbreeding ( e . g . , cousin-cousin mating ) is known to decrease fitness in animals[1] and to increase risk for recessive Mendelian diseases in humans[2] , a phenomenon known as inbreeding depression . Inbreeding depression is thought to occur due to evolutionary selection against genetic variants that decrease fitness—e . g . , variants that increase risk of disorders[3] . Such fitness-reducing variants should not only be more rare , but also more recessive than expected under a neutral evolution model ( i . e . , show directional dominance ) . If so , individuals with a greater proportion of their genome in autozygous stretches ( two homologous segments of a chromosome inherited from a common ancestor identical by descent [IBD] ) should have higher rates of disorders . This is because autozygous regions reveal the full , harmful effects of any deleterious , recessive alleles that existed on the haplotype of the common ancestor . Whether inbreeding increases risk for complex disorders like schizophrenia is less clear . Previous studies have found that inbreeding is associated with higher rates of complex disorders[4–9] . However , sample sizes have typically been small and the possibility that confounding factors might explain the results has left the links inconclusive . Moreover , close inbreeding accounts for fewer than 1% of marriages in industrialized countries[10] , and information on pedigrees going back many generations is difficult to collect reliably . For these reasons , investigators have recently begun looking at signatures of very distant inbreeding ( e . g . , common ancestry up to ~100 generations ago ) using genome-wide single nucleotide polymorphism ( SNP ) data in an attempt to understand whether autozygosity increases the risk to schizophrenia and other complex diseases[11] . Autozygosity in SNP data is typically inferred from runs of homozygosity ( ROHs ) : long , contiguous stretches ( e . g . , > 40 ) of homozygous SNPs . The proportion of the genome contained in such ROHs , Froh , can then be used to predict complex traits[12–19] . Keller et al . [11] showed that Froh is the optimal method for detecting inbreeding signals that are due to rare , recessive to partially recessive mutations , such as those thought to occur when traits are under directional selection[3] . The low variation in Froh means that large sample sizes ( e . g . , >12 , 000 ) are required to uncover realistic effects of distant inbreeding on complex diseases in samples unselected for inbreeding[11] . In 2012 , Keller et al . [20] used the original Psychiatric Genomics Consortium schizophrenia data ( 17 case-control datasets , total n = 21 , 831 ) to investigate whether Froh is associated with increased risk of schizophrenia . The authors estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is contained in autozygous regions ( β = 16 . 1 , CI ( β ) = [6 . 93 , 25 . 7] , p = 6x10-4 . ) This was by far the largest study to that date examining the association between Froh and any psychiatric disorder , and the significant relationship between Froh and case-control status remained robust through secondary analyses of various covariate combinations , common vs . rare IBD haplotypes , and SNP thresholds used to define ROHs . These results are consistent with the hypothesis that autozygosity causally increases the risk of schizophrenia . Nevertheless , because various confounding factors may increase likelihood of distant inbreeding as well as the probability of having offspring with schizophrenia , these results do not imply a causal relationship . For example , parents higher on schizophrenia liability may pass their higher liability to offspring and mate with more genetically similar partners ( e . g . , due to decreased mobility , educational opportunities , etc . ) . The current study seeks to provide a well-powered , independent replication of Keller et al . ( 2012 ) [20] . In light of the growing concern about publication bias[21 , 22] and dearth of well-powered replications[23 , 24] , this follow-up analysis is a necessary step in validating the Froh—schizophrenia relationship . The present study used genome-wide SNP data from 22 independent schizophrenia case-control datasets ( n = 39 , 830 ) from the PGC[25] to further examine the relationship between Froh and schizophrenia . Our replication attempt is an important contribution to the growing body of literature examining autozygosity and psychiatric disorders , and should help verify whether autozygosity estimated from ROHs is robustly related to schizophrenia risk and , by extension , can help elucidate whether schizophrenia risk alleles are biased , on average , toward recessive effects .
For each dataset , we regressed case-control status on Froh using mixed effects logistic regression treating dataset as a random factor , and controlled for 20 principal components ( PCs ) from the genomic relationship matrix[27] and two SNP quality measures ( excess heterozygosity and SNP missingness; see Methods ) . In Keller et al . ( 2012 ) , the authors used mixed effects models to test the ROH burden association with schizophrenia . However , in the current analysis we used fixed effect logistic regression models , treating dataset as a fixed , because a minority of the mixed effects models failed to converge . When the mixed effects models did converge , the results were highly similar to the respective fixed effect models . Figs 1 and S1 show the predicted change in odds of schizophrenia risk ( and 95% confidence intervals ) for every 1% increase in average Froh for each logistic regression in the replication data using ROHs defined by either ≥110 consecutive homozygous SNPs ( Fig 1 ) or ROH length ≥ 2 . 3 Mb ( S1 Fig ) . The overall association between schizophrenia and Froh in the replication data was in the predicted direction but not significant for ROHs defined as at least 110 consecutive homozygous SNPs ( β = 0 . 19 , CI ( β ) = [−4 . 50 , 4 . 88] , Z = 0 . 08 , p = 0 . 94 ) or for ROHs defined as ≥ 2 . 3 Mb ( β = 0 . 75 , CI ( β ) = [−4 . 05 , 5 . 56] , Z = 0 . 31 , p = 0 . 76 ) . The results from analyses on ROHs called from imputed rather than raw SNP data were also non-significant ( S5 Fig ) . As in Keller et al . , we also explored increasingly long SNP and Mb ROH thresholds to assess the stability of the Froh-schizophrenia relationship ( Figs 2 and 3 ) . Across all thresholds , the only thresholds that approached significant associations between Froh and schizophrenia in the replication data were at the upper limits of the Mb-length ROH thresholds; the strongest association was for ROHs defined as ≥ 19 Mb ( β = 8 . 64 , CI ( β ) = [−0 . 85 , 18 . 13] , Z = 1 . 78 , p = 0 . 07 ) . We conducted a series of follow-up analyses to ensure that the failure to replicate our original report was not due to analytical error , inclusion of outlier individuals or datasets , or suppressing covariates in the replication data . We reran the same analyses described above on SNP data from the “original” report using the exact same quality control and analytic procedures performed on the replication data . Results were virtually identical to those obtained in Keller et al . ’s 2012 study ( S2–S4 Figs ) , increasing our confidence that the procedures used in the replication attempt were identical to those used in the original analysis and that the results from the original analysis were not due to analytic or procedural errors . We then reran analyses in the replication data after ( a ) omitting individuals with very long ( >30 Mb ) ROHs , ( b ) omitting only long ROHs , ( c ) including all combinations of covariates in the model ( SNP missingness , average heterozygosity , 10 or 20 principle components ) , and ( d ) including only the longest ROH for each individual . The Froh-schizophrenia relationship remained non-significant in these follow-up analyses ( results shown in S2 Table ) . We noticed that there was greater variability in Froh in the replication datasets and that this greater variability was mostly driven by replication datasets that had n < 300 . Under the premise that smaller samples might differ in genotypic or phenotypic quality , we excluded seven samples that contained fewer than 300 cases ( “egcu” , “ersw” , “lie2” , “pews” , “top8” , “umes” ) , reran our baseline analysis ( including all covariates mentioned above and using an ROH threshold of ≥ 110 consecutive homozygous SNPs ) , but still observed a non-significant Froh-schizophrenia relationship ( β = 1 . 04 , CI ( β ) = [−3 . 88 , 5 . 96] , Z = 0 . 42 , p = 0 . 68 ) in the predicted direction . Therefore , this post-hoc analysis does not lend support to the possibility that small samples in the replication set added noise to our analysis , obscuring an Froh-schizophrenia relationship . Although results from the replication analysis were not significant , they were in the same direction as the original analysis . It could therefore be argued that the best estimate of the association between ROHs and schizophrenia is obtained by combining the two datasets . When we reran our analyses on the combined original + replication data ( n = 61 , 661 ) , all Froh associations based on ROH thresholds greater than 60 consecutive homozygous SNPs or longer than 1 Mb were significant ( Figs 4 and 5 ) . For an ROH threshold of ≥ 110 consecutive homozygous SNPs ) , we observed a significant Froh-schizophrenia relationship in the combined data ( β = 4 . 86 , CI ( β ) = [0 . 90 , 8 . 83] , Z = 2 . 40 , p = 0 . 02 ) . In this combined dataset , we also used a replication status-by-Froh interaction to conclude that the Froh-schizophrenia association was only marginally higher in the original compared to the replication datasets ( interaction β = −3 . 98 , Z = −1 . 84 , p = 0 . 07 ) for ROHs defined as at least 110 consecutive homozygous SNPs . To assess the relative importance of distant versus close inbreeding , we compared the effects of short versus long ROHs . As in the original study , we chose our ROH length threshold based on the Mb length cutoff that resulted in equal Froh variances , calculating Froh_short as the proportion of the genome contained in ROHs < 8 Mb long , and Froh_long as the proportion of the genome contained in ROHs > 8 Mb long . Although neither association was significant , the effect of Froh_short ( β = −5 . 06 , CI ( β ) = [−12 . 08 , 1 . 95] , Z = −1 . 42 , p = 0 . 16 ) , caused by autozygosity arising from more ancient common ancestors , was negative ( “protective” ) and in the opposite direction of effect of Froh_long ( β = 1 . 23 , CI ( β ) = [−4 . 78 , 7 . 25] , Z = 0 . 40 , p = 0 . 69 ) , caused by autozygosity arising from more recent common ancestors , which predicted increased risk for schizophrenia ( Fig 6 ) .
Despite exploring various homozygous SNP length thresholds , Mb thresholds , and combinations of covariates , the findings from this study do not lend much support to the original observation of a highly significant Froh-schizophrenia association[20] , and provide only equivocal support , based on combining the original and replication data , for the hypothesis that autozygosity is a risk factor for schizophrenia . Perhaps the simplest explanation for this pattern of results is that the conclusions about distant inbreeding from the original data represent a type-I error or that the lack of replication in the current report was a type-II error . Despite the fact that the effect in the original study was highly significant ( p = 6x10-4 ) and the statistical power in the replication study to detect the observed effect size in the original study was nearly 100% , it is possible that the estimated effects of the original analysis could have been over-estimated and/or those of the replication analysis under-estimated , due to sampling variability . There is some support for this interpretation , as there was not a significant difference in results between replication versus original datasets ( interaction p = 0 . 07 ) . An alternative explanation for the overall pattern of results has to do with the potential influence of unmeasured confounding factors in both the original and replication analyses . Unlike genotype frequencies , which change very slowly and are unaffected by inbreeding , ROH levels can change substantially after even a single generation of inbreeding , making ROH analyses highly susceptible to confounding factors associated with both disease risk and the degree of inbreeding/outbreeding . For example , contrary to initial predictions , Abdellaoui et al . [28] identified a significant and negative ( “protective” ) relationship between Froh and risk for major depressive disorder ( MDD ) in the Dutch population . However , the authors found that religiosity was significantly associated with both higher autozygosity and lower MDD in this population . When religiosity was accounted for in their regression model , the original association between MDD and Froh disappeared . A similar effect was detected for educational attainment: highly educated individuals were more likely to migrate and mate with highly educated and more diverse partners , making highly educated spouse pairs share less ancestry and leading to their offspring having lower Froh[29] . Thus , assortative mating on variables such as education or religion could subtly influence observed Froh associations , potentially affecting results in ways that can be difficult to account for . For example , an observed Froh-schizophrenia relationship could be due to parents with a higher schizophrenia liability mating with less genetically diverse mates due to , e . g . , fewer educational opportunities or lower migration rates . Thus , the causation may be reversed: schizophrenia liability in parents could cause not only higher schizophrenia risk , but also higher Froh , in offspring rather than Froh in offspring increasing their schizophrenia liability . Such reverse and third variable causation possibilities can only be tested if relevant socio-demographic variables in subjects and ( optimally ) their parents are collected . The possibility of unmeasured variables confounding Froh-disorder relationships seems particularly likely in analyses conducted on ascertained samples . Ascertainment of cases and controls not perfectly matched on socio-demographic factors that might affect degree of outbreeding ( e . g . , socioeconomic status , education level , age , religion , urbanicity ) can mask any true Froh association and bias the observed association in either direction . Such a scenario might explain otherwise contradictory findings in previous ROH case-control analyses[18 , 28 , 30–36] . For example , following two studies showing that genome-wide autozygosity was significantly associated with schizophrenia risk , including the original Keller et al . study[13 , 20] , two newer studies failed to replicate this association[34 , 35] , although both replication sample sizes ( n = 3 , 400 and 11 , 244 respectively ) were substantially smaller than the current one ( n = 39 , 830 ) . ( It should be noted that the sample used in the latter study[36] overlapped with the samples in both the original Keller et al . [20] study and the current replication study ) . Even within the same study , Froh results in ascertained samples have been inconsistent . Using PGC MDD data , Power et al . [36] found a significant positive Froh-MDD relationship in data from three German sites but a significant negative Froh-MDD relationship in six non-German sites . A possible explanation for this and other such examples of heterogeneity across sites they observed is that cases and controls differed on socio-demographic factors that were associated with Froh , and the direction of this ascertainment bias was inconsistent across data collection sites . We believe that similar ascertainment biases could have affected results in the present study as well as in the original Keller et al . [20] report . Many of the PGC schizophrenia datasets used cases ascertained from hospitals , clinics , health surveys , and advertisements but controls from previous biomedical research volunteers , university students , blood donors , and population registries . While such differences in ascertainment between cases and controls are highly unlikely to lead to allele frequency differences , and thus are of little concern to genome-wide association studies , they could very easily lead to Froh differences due to differences in degree of inbreeding/outbreeding in the populations from which cases and controls were drawn . Controlling for ancestry principal components in this case would only help to the degree that degree of inbreeding/outbreeding is associated with ancestry . Unfortunately , none of the other variables that might statistically control for such biases due to differences in case/control ascertainment are currently available in the PGC data collection . The PGC collection of studies was designed for association analyses; it was not optimally designed for ancillary purposes , such as ROH analyses . It is important to recognize that even ascertainment biases that differ at random across sites would substantially inflate type-I error rates because the proper degrees of freedom for the test should be closer to the number of independent sites rather than the number of independent cases and controls . To demonstrate this , we permuted data under the null hypothesis of no relationship between Froh and schizophrenia in the 17 datasets from the original 2012 study by randomly flipping case or control status within each dataset for each permutation ( e . g . , cases and control statuses in a dataset either remained the same or were flipped to the opposite status ) . We then calculated the overall Froh ~ schizophrenia relationship with the same logistic regression model and using the same covariates as in the original analysis . Across 1 , 000 permutations , 183 p-values were significant ( p < 0 . 05 ) , implying a type-I error rate of 0 . 18 and demonstrating how false conclusions about Froh relationships can be reached even when ascertainment biases are random across multiple sites .
Given concerns about the false discovery rate in science[22] , there has been increasing emphasis on the need for well-powered , direct replications of novel findings in genetics[23 , 37 , 38] and other fields[39–41] . The current study was a well-powered , direct replication attempt that failed to replicate an earlier finding that autozygosity arising from distant common ancestors was significantly associated with schizophrenia . As is typical with null findings , it is difficult to identify the reason for this failure to replicate . However , we have argued that a likely cause is that ROH associations are highly susceptible to confounding , especially in case-control ( ascertained ) samples . Thus , we believe that the conclusions of the original study were premature and the true causal relationship between schizophrenia and autozygosity could be either stronger/more positive ( if the populations from which controls were ascertained were , on average , slightly less outbred than populations from which cases were ascertained ) or weaker/more negative ( the reverse ) than reported here . Unfortunately , we do not have the ability to test these hypotheses directly in the current datasets , and doing so awaits either new samples in which cases and controls are carefully matched or the collection of information that allows potential confounders to be statistically controlled . This creates a dilemma for ROH analyses using existing case-control genome-wide data: GWAS datasets usually do not match cases and controls to the degree necessary to rule out confounding effects on ROH analyses and typically do not collect the relevant socio-demographic information necessary to control for potential confounders . The current study therefore serves as a cautionary tale for analyzing ROHs in existing ascertained GWAS datasets . Such datasets may be perfectly adequate for their designed purpose–GWAS–but may be problematic and even misleading for ROH analyses .
Our study used 37 datasets from the Psychiatric Genomics Consortium’s SCZ2 data–these data included 28 , 985 schizophrenia cases and 35 , 017 controls , collected from 37 sites in 13 countries . Data collection and ascertainment details are described elsewhere . [25] Keller et al . [20] used 17 datasets from the PGC SCZ1[26] data . Several of these original 17 studies recruited additional subjects by the time of our study , necessitating two well-defined , independent datasets: one including all of the individuals analyzed in the original 2012 study ( “original” dataset ) , and one containing only subjects not included in Keller et al . ’s 2012 report ( the “replication” dataset , comprised of 22 studies and a total sample size of 18 , 562 cases and 21 , 268 controls after QC; see Table 1 ) . Three of the original case-control datasets from the PGC’s SCZ1 added more subjects and/or controls in SCZ2 , but only two of these datasets had enough subjects to pass QC and merit inclusion in the current study—thus there is a “top8” dataset ( N = 180 ) in this replication study , comprised of the samples that were added to the “top3” dataset ( N = 598 ) from the original 2012 study , and a “boco” dataset ( N = 1 , 870 ) , which includes the new cases and controls that were added to the original “bon” dataset ( N = 1 , 778 ) . For consistency with the original Keller et al . ( 2012 ) study[20] , we excluded the three family-based datasets of parent-proband trios and three East Asian datasets . We followed the same QC procedures as Keller et al . [20] . We removed a ) one individual from any pair of individuals who were related with π^ >0 . 2 , b ) individuals with non-European ancestry as determined by principal components analysis; c ) samples with SNP missingness >0 . 02; or d ) samples with genome-wide heterozygosities >6 standard deviations above the mean . SNPs were excluded if they a ) deviated from Hardy-Weinberg equilibrium at p<1×10−6; b ) had missingness >0 . 02; or c ) had a missingness difference between cases and controls >0 . 02 . Early in the analysis process , we found that only including SNPs with imputation dosage r2 > . 90 across all datasets , as was done in the original study[20] , left us with too few SNPs with which to conduct viable ROH analyses in the replication data . Because having ROHs of similar length and SNP density is important for comparing present results to those from the 2012 study , we decided that having a similar number of SNPs to Keller et al . [20] was more important than following the exact same QC procedures . Thus , to arrive at a similar number of genome-wide SNPs in the new and old datasets , some of the QC measures described below were different than in the 2012 investigation . SNPs were imputed using the 1000 Genomes reference panel[42]; imputation procedures are described elsewhere[25] . Imputation dosages were converted to best-guess ( highest posterior probability ) SNP calls because ROH detection algorithms require discrete SNP calls , and extremely stringent QC thresholds were employed to achieve accuracy rates similar to those in genotyped SNPs[43] . We excluded any imputed SNPs that were not included in the HapMap3[44] reference panel , as done in the 2012 study . Unlike the original QC procedures , we did not require that the dosage r2 had to be > . 90 in each individual datasets . We excluded any imputed SNPs that had a dosage r2<0 . 98 or >1 . 02 in the overall sample ( calculated using average dosage r2 weighted by sample size ) or that had MAF<0 . 15 within each sample ( vs . . 05 in original ) , leaving 340 , 084 high-quality imputed SNPs ( vs . 398 , 325 in original ) . Again , we followed the same ROH calling procedures as in Keller et al[20] . As recommended in a separate investigation[45] by three of the authors of the present study , we chose PLINK software[46] for its computational efficiency and superior detection of autozygous stretches . As in the 2012 study , we pruned for LD using PLINK’s—indep flag , which ensures more uniform SNP coverage across the genome and reduces false autozygosity calls by removing redundant markers . We pruned SNPs for LD using a VIF threshold of 10 , which is equivalent to multiple R2 > 0 . 90 between the focal SNP and the 50 surrounding SNPs . We called ROHs using PLINK’s—homozyg flags , defining initial ROHs as being ≥40 homozygous SNPs in a row with no heterozygote calls allowed . We required that ROHs have a density greater than 1 SNP per 200 kb , and split an ROH into two if a gap >500 kb existed between consecutive homozygous SNPs . We then post-processed the initial ROH calls by altering the SNPs-in-a-row threshold and the Mb length threshold; specifically , we looked at ROH calls with a minimum of 40 to 200 consecutive homozygous SNPs in increments of 10 , and ROH calls with minimum lengths ranging from 1 to 20 Mb by increments of 1 Mb . We varied ROH thresholds this widely to ensure that no potential effects of autozygosity were missed , but the primary results presented here are based on two replication attempts in the unimputed data: ( a ) using the same SNP thresholds that gave the most straightforward comparison with the original report ( this was 110 SNPs-in-a-row for the unimputed data , spanning ~1 to ~2 . 1 Mb in the replication datasets , and 65 SNPs-in-a-row for the imputed data ) , and ( b ) using the physical length threshold ( 2 . 3 Mb ) that corresponded to the average Mb length for 110 SNPs-in-a row in the original report . After calling ROHs , we summed the total length of all autosomal ROHs for each individual and divided that by the total SNP-mappable distance ( 2 . 77x109 bases ) to calculate Froh . Froh , the proportion of the genome contained in long homozygous regions , was used as the predictor of schizophrenia case-control status in analyses described below . As confounding factors such as population stratification , SNP missingness , call quality , and plate effects can influence Froh , we included the first 20 principle components ( based on a genome relationship matrix calculated from ~30K LD-pruned SNPs ) , percentage of missing SNP calls in the raw data , and excess heterozygosity in all regression models[20] . We then regressed case-control status on Froh using a mixed linear effects logistic regression model ( available in the lme4 package in R version 3 . 1 . 0 ) , treating dataset as a random factor , to assess the overall effect of Froh on schizophrenia across all sites . Some of the models with random effects did not converge; thus , for consistency , we modeled dataset as a fixed factor for all analyses . The results from mixed linear effects models that converged were very similar to fixed effects models , giving us confidence that the fixed effects results of this analysis and the random effect results from the original Keller et al . ( 2012 ) study are commensurate . We also ran logistic regressions in each of the 22 datasets separately . This research was approved by CU Boulder's Institutional Review Board with regard to protocol number 13–0266 on 3/29/2016 in accordance with Federal Regulations at 45 CFR 46 . Written patient consent was obtained for each individual study by the study PI , with the exception of the "clm3" and "clo3" datasets , which obtained anonymous samples via a drug monitoring service under ethical approval and in accordance with the UK Human Tissue Act . | It is well known that mating between relatives increases the risk that a child will have a rare recessive genetic disease , but there has also been increasing interest and inconsistent findings on whether inbreeding is a risk factor for common , complex psychiatric disorders such as schizophrenia . The best powered study to date investigating this theory predicted that the odds of developing schizophrenia increase by approximately 17% for every additional percent of the genome that shows evidence of inbreeding . In this replication , we used genome-wide single nucleotide polymorphism data from 18 , 562 schizophrenia cases and 21 , 268 controls to quantify the degree to which they were inbred and to test the hypothesis that schizophrenia cases show higher mean levels of inbreeding . Contrary to the original study , we did not find evidence for distant inbreeding to play a role in schizophrenia risk . There are various confounding factors that could explain the discrepancy in results from the original study and our replication , and this should serve as a cautionary note–careful attention should be paid to issues like ascertainment when using the data from genome-wide case-control association studies for secondary analyses for which the data may not have originally been intended . | [
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... | 2016 | No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study |
Searching for stimulators of the innate antiviral response is an appealing approach to develop novel therapeutics against viral infections . Here , we established a cell-based reporter assay to identify compounds stimulating expression of interferon-inducible antiviral genes . DD264 was selected out of 41 , 353 compounds for both its immuno-stimulatory and antiviral properties . While searching for its mode of action , we identified DD264 as an inhibitor of pyrimidine biosynthesis pathway . This metabolic pathway was recently identified as a prime target of broad-spectrum antiviral molecules , but our data unraveled a yet unsuspected link with innate immunity . Indeed , we showed that DD264 or brequinar , a well-known inhibitor of pyrimidine biosynthesis pathway , both enhanced the expression of antiviral genes in human cells . Furthermore , antiviral activity of DD264 or brequinar was found strictly dependent on cellular gene transcription , nuclear export machinery , and required IRF1 transcription factor . In conclusion , the antiviral property of pyrimidine biosynthesis inhibitors is not a direct consequence of pyrimidine deprivation on the virus machinery , but rather involves the induction of cellular immune response .
Infections by RNA viruses represent a major burden for public health . It includes major human pathogens such as influenza virus , measles virus , dengue virus or respiratory syncytial virus that are responsible for hundreds of thousands of human death every year . Although efficient prophylactic treatments , and in particular vaccines , can be used to protect individuals from some of these pathogens , our therapeutic arsenal is extremely limited [1] . Clinically used treatments are essentially based on ribavirin or recombinant type I interferons ( IFN-α/β ) that are of highly variable efficacy depending on targeted virus [1] , [2] . Inhibitors of influenza virus or human respiratory syncytial virus have been developed , but such virus-specific treatments are useless against other RNA viruses [1] . Furthermore , RNA viruses are extremely diverse in terms of replication machinery , and this greatly complicates the design of broad-spectrum antiviral molecules . They also tend to escape drugs that target viral proteins through mutations , thus calling for innovative therapeutic approaches . Among possible strategies , chemical modulators of host pathways [3] , [4] , [5] , [6] , and in particular stimulators of innate immune response that boost cellular defenses to eliminate viral pathogens are of growing interest [7] , [8] , [9] , [10] . In principle , such molecules would be efficient against a large panel of viral pathogens since the host immune response relies on a multiplicity of antiviral effectors that block viruses at several steps of their replication , and cover the variety of replication strategies they use . The innate immune response is initiated by the recognition of Pathogen-Associated Molecular Patterns ( PAMPs ) by different classes of Pattern Recognition Receptors ( PRRs ) . Along their replication cycle , RNA viruses produce several well-characterized PAMPs , such as double-stranded RNA , uncapped 5′-triphosphate RNA or single-stranded RNA molecules [11] , [12] . PRRs that recognize such virus-associated molecular motifs essentially belong to two protein families: toll-like receptors ( TLRs ) and RIG-I like receptors ( RLRs ) . TLRs are transmembrane receptors , and only three members of this family have been reported to detect RNA molecules with their extracellular domain: TLR3 that binds double-stranded RNA and TLR7/8 that are activated by G/U rich single-stranded RNA [13] . RIG-I and IFIH1/MDA5 are best-characterized members of the RLR family . These cytosolic sensors are expressed by virtually all cell types to detect short 5′-triphosphate and long double-stranded RNA molecules , respectively [14] . Upon activation by their ligands , TLRs and RLRs initiate signaling cascades that converge on three families of transcription factors ( NF-κB , IRF3/7 , and ATF-2/Jun ) to induce genes encoding antiviral effectors and type I IFN ( IFN-α/β ) secretion . Secreted IFN-α/β subsequently amplify the antiviral response through binding to their membrane receptor at the surface of both infected cells and neighboring cells [11] , [15] . This activates a Jak/STAT signaling cascade that further stimulates the expression of antiviral genes in the infected organ . Human genome contains hundreds of IFN-inducible genes , and a large fraction encode for restriction factors to target viruses at multiple steps of their replication cycle [16] , [17] . To identify chemical compounds stimulating this pathway , several strategies have been developed . Molecules that engage TLRs or IFN-α/β receptors have been identified using various combinations of functional screens , in silico molecular docking and binding assays [18] , [19] . Phenotypic screens have also been used to identify stimulators of the antiviral gene cluster [9] , [20] , [21] , [22] , [23] , [24] , [25] . Several groups have recently described similar assays based on cells transfected with a reporter gene under control of IFN-stimulated response elements ( ISRE ) [21] , [22] , [23] . This led to the identification of small molecules showing some antiviral activity in vitro . In the current report , we used a similar strategy to screen a compound collection of 41 , 353 molecules , and identified compound DD264 as a molecule stimulating the expression of antiviral genes in treated cells and exhibiting a potent antiviral activity in vitro . While searching for its mode of action , we found that compound DD264 targets the de novo pyrimidine biosynthesis pathway . This allowed us to establish for the first time a link between inhibition of pyrimidine biosynthesis , amplification of antiviral gene expression , and inhibition of RNA virus infections .
To identify chemical compounds that stimulate expression of IFN-inducible genes , we have developed a high-throughput screening assay based on human HEK-293T cells transiently transfected with a luciferase reporter gene under control of five IFN-stimulated response elements ( ISRE ) . A total of 41 , 353 chemical compounds , with final concentrations ranging from 30 to 130 µM depending on the library , were screened with this assay for their capacity to stimulate ISRE-luciferase expression in human cells ( see Materials and Methods and Figure S1 for details ) . For each screening plate , the amplification of luciferase signal in control wells was found >45 when recombinant IFN-β was added . Although most tested molecules were not inducers of reporter gene expression , five compounds showed a reproducible although modest >4-fold amplification of luciferase activity ( data not shown ) . Three of them showed a strong toxicity in culture and were discarded . Two compounds from the chemical library of Institut Curie were finally selected for further studies including DD264 ( Figure 1A and 1B ) and DD363 , which will be described elsewhere . To further investigate DD264 biological activity , we established HEK-293 cells stably transfected with the ISRE-luciferase reporter gene . In agreement with screening data , DD264 induced some significant expression of the ISRE-luciferase reporter gene in this system ( Figure 1C ) , but was much less efficient than recombinant IFN-β to stimulate the ISRE promoter ( Figure 1D ) . Interestingly , ISRE stimulation by DD264 was independent of IFN-α , β , or γ induction as assessed by qRT-PCR and ELISA ( Table S1 and S2 ) . A cocktail of blocking antibodies against IFN-α/β also had no effect on ISRE-luciferase induction by DD264 ( data not shown ) . Altogether , this demonstrated that a different pathway is involved . Visual examination of treated cultures suggested that DD264 inhibited cellular proliferation . This was confirmed by quantifying over several days the number of living cells in cultures treated or not with increasing amounts of DD264 ( Figure S2A ) . Cells proliferated in control wells and did not when treated with DD264 , but the number of living cells remained stable ( did not collapse ) . Inhibition of cellular proliferation was also shown by propidium iodide labeling and flow cytometry analysis of cellular DNA content ( Figure S2B ) . We finally determined that DD264 did not induce cell death even at the highest concentration tested ( 80 µM ) as assessed by quantifying DNA fragmentation ( Figure S2B ) . DD264 was tested for its inhibitory effect on the replication of several human viruses of clinical importance and from different families . DD264 was first tested on measles virus ( MV ) , a Paramyxoviridae that can be considered as a prototype of negative-strand RNA viruses . Human HEK-293T cells were infected with a recombinant MV strain expressing either EGFP ( MV-EGFP ) or luciferase ( MV-Luc ) from an additional transcription unit , and then treated with DD264 . As shown in Figure 2A and 2B , DD264 suppressed MV replication as assessed by inhibition of EGFP or luciferase expression . As shown in Figure 2B , IC50 of DD264 was about 15 µM in this assay . DD264 also inhibited the production of infectious viral particles ( Figure S3A ) . Finally , we also established that DD264 antiviral activity was not restricted to HEK-293T cells , and could be observed on other cell lines like HeLa and MRC5 cells ( Figure S3B and S3C ) . We then tested DD264 activity on the growth of a positive-strand RNA virus , chikungunya virus ( CHIKV ) , an emerging mosquito-transmitted Alphavirus ( Togaviridae family ) responsible for arthralgia in human . HEK-293T cells were infected with wild-type CHIKV or a recombinant variant expressing Renilla luciferase as a reporter [26] , [27] , [28] , and viral infection was determined by immunostaining with anti-E2 mAb or measuring luciferase activity . DD264 efficiently suppressed CHIKV growth in infected cell cultures ( Figure 2C and 2D ) . Similar results were obtained on West Nile virus , an emerging mosquito-transmitted Flavivirus ( Flaviviridae family ) associated to acute encephalitis ( Figure 2E ) . DD264 also inhibited the production of infectious WNV particles in a dose-dependent manner ( Figure 2F ) . Antiviral activity of DD264 was also tested on a non-enveloped positive-strand RNA virus , coxsackievirus B3 ( CVB3 ) , which is a prototype of Picornaviridae family . Following DD264 treatment , CVB3 viral production in cell cultures was strongly impaired ( Figure S3D ) . Therefore , DD264 has a potent antiviral activity against various unrelated RNA viruses in cell cultures . To identify chemical features of DD264 accounting for its antiviral activity , a broad set of about 70 structural analogs were synthesized from appropriate β-chloro-β-nitrostyrenes according to known procedures ( [29] , [30] , [31] , [32] and Protocol S1 ) , and then tested for their antiviral activity on MV . All 2 , 3-dihydro-2-nitro derivatives ( Table S3 , formula A ) were almost inactive . Regarding compounds with general formula B , a larger halogen ( DD700 , GAC25 ) , a vinyl ( GAC50 ) or an alkyne group ( JP4 , JP6 ) could replace the chlorine atom at position R8 of DD264 . Other substituents led to a significant or total loss of antiviral activity . In addition , the meta position of the chlorine atom was found to be optimal , as compounds bearing a chloro substituent in ortho ( DD706 ) or para ( DD703 ) were much less active . Interestingly , the chlorine atom at position R8 could be replaced by a much larger group such as 3-chlorophenyl ( JP13 ) or 3-bromophenyl ( JP33 ) without any loss of activity . It was also possible to add a halogen at position R1 ( e . g . a bromine atom ) and this even increased the compound potency . Unfortunately , the synthesis of this bromo derivative ( JP61f2 ) only proceeded in a poor yield , and further functional analyses were thus performed on DD264 . Finally , we found that the carbonyl function can be moved from position 4 to position 7 of the tetrahydrobenzofuran skeleton without any significant loss of potency ( DD829 ) . All other tested structural modifications , substitutions or additions performed on the cyclohexanone nucleus induced a decrease or a complete loss of activity . Altogether , this established a clear structure/activity relationship for this series , and provides a chemical framework to further improve activity and pharmacological properties of DD264 . Despite a poor capacity to induce the ISRE-luciferase reporter gene by itself , DD264 has a broad-spectrum antiviral activity . This led us to hypothesize that DD264 rather amplifies the cellular response to viral PAMPs and/or IFN signaling , which would account for its potent antiviral activity . To test this hypothesis , cells stably transfected with the ISRE-luciferase reporter gene were transfected with increasing doses of short synthetic 5′-triphosphate RNA molecules ( ssRNA ) or treated with recombinant IFN-β , then incubated in the presence of DD264 or DMSO alone . Because ssRNA molecules mimic a viral PAMP , they activated the host antiviral response and induced expression of the ISRE-luciferase reporter gene ( Figure 3A ) . Cellular response to suboptimal doses of ssRNA or IFN-β was strongly amplified in the presence of DD264 ( Figure 3A and 3B ) . Interestingly , ISRE-luciferase induction by ssRNA transfection was partially dependent on the IFN-α/β secretion loop , as assessed by the addition of blocking antibodies against these cytokines ( Figure S4A ) . However , amplification of cellular response by DD264 was still observed when blocking antibodies were added , establishing that DD264 activity is essentially independent of the IFN-α/β loop ( Figure S4A ) . Accordingly , DD264 antiviral activity was not affected by the same cocktail of antibodies directed against IFN-α/β ( Figure S4B ) . We also tested a subset of DD264 analogs ranging from strong antiviral to inactive molecules for their capacity to amplify cellular response to ssRNA . A clear correlation was found between their antiviral activity and their capacity to amplify cellular response to ssRNA ( Figure 3C ) , thus supporting a functional link between these two activities . To further support this observation , we determined by quantitative RT-PCR the transcriptional level of twelve interferon-stimulated genes ( ISGs ) when activating cells with ssRNA ( Figure 4 ) . We observed a significant 2 to 3-fold increase in expression levels of most tested ISGs when ssRNA-transfected cells were cultured in the presence of DD264 . In contrast , control genes like 18S , GAPDH or HPRT1 were not affected . Altogether , this demonstrated that DD264 amplified cellular response to ssRNA and expression levels of antiviral genes , and this correlated with its antiviral activity . Several recent reports have shown that inhibitors of pyrimidine biosynthesis pathway are potent broad-spectrum antiviral molecules [33] , [34] , [35] , [36] . Although DD264 is structurally unrelated to these molecules , we tested if DD264 could interfere with this particular metabolic pathway . We first determined intracellular concentrations of purine and pyrimidine nucleosides by HPLC and spectrophotometry analysis . As shown in Figure 5A , DD264 treatment decreased intracellular concentrations of uridine and cytidine , whereas purines were either unchanged ( adenosine ) or slightly increased ( guanosine ) likely as a consequence of the control loops connecting purine and pyrimidine metabolic pathways . Similar changes in nucleosides profile were previously reported in RKO cells treated with leflunomide , which is a clinically used inhibitor of pyrimidine biosynthesis pathway [37] . To determine whether inhibition of pyrimidine biosynthesis by DD264 is actually linked to viral growth inhibition , cells infected with MV-luciferase were treated or not with DD264 and the culture medium was either supplemented or not with increasing doses of pyrimidine ( uridine ) or purine ( guanosine ) nucleosides . Adding uridine fully restored viral replication in DD264-treated cells as assessed by luciferase expression ( Figure 5B ) , whereas guanosine had no effect ( Figure 5C ) . Conversely , guanosine but not uridine abolished the antiviral activity of mycophenolic acid , a molecule that was previously reported to block viral replication by inhibiting purine biosynthesis [38] , [39] , [40] . Altogether , this demonstrated that a lowered level of pyrimidine nucleosides is responsible for the antiviral activity of DD264 . Different intermediates of the pyrimidine nucleosides de novo biosynthesis pathway were added to the culture medium to seek for those able to counteract the inhibition of viral growth by DD264 . It turned our that in DD264-treated cells , MV replication was restored in the presence of orotate but not dihydroorotate ( Figure 5D and 5E ) . Along pyrimidine biosynthesis , dihydroorotate is oxidized into orotate by dihydroorotate dehydrogenase ( DHODH ) , the fourth enzyme of this metabolic pathway . Although this is not a formal demonstration because we could not control for equal entry and stability of orotate and dihydroorotate in HEK-293T cells , these data strongly suggested that within pyrimidine biosynthesis pathway , DHODH is the target of DD264 . This enzyme has also been described recently as the cellular target for several newly identified compounds with a broad-spectrum antiviral activity [33] , [34] , [35] , [36] . We have shown that the antiviral activity of DD264 relies on some imbalance in the pool of pyrimidines , which is mediated by the inhibition of the pyrimidine biosynthesis pathway probably via DHODH . However , DD264 was originally identified for its capacity to amplify expression of ISRE-regulated genes . We thus sought for a functional relationship existing between these two mechanisms . We first determined the ISRE-luciferase expression when culture medium was supplemented with uridine . As shown in Figure 6A , DD264-mediated amplification of cellular response to ssRNA was impaired in the presence of uridine . Similarly , uridine abolished the amplification effect of DD264 on the expression of ISGs in cells treated with low doses of ssRNA ( Figure 6B ) . Finally , we determined if DHODH overexpression could compensate for DD264 treatment , and prevent the amplification of ISRE-luciferase expression in ssRNA-transfected cells . As shown in Figure 6C , the effect of DD264 on ISRE-luciferase expression was abolished when DHODH was overexpressed . This established a functional link between the lack of pyrimidine nucleosides in cells treated with DD264 and the amplification of cellular response to ssRNA . To further demonstrate that DHODH inhibition could amplify cellular response to ssRNA , we tested in our functional assay a well-known inhibitor of this enzyme: brequinar [41] . Cells were transfected with increasing doses of ssRNA and culture medium was supplemented with brequinar or DMSO alone . As shown in Figure 6D , brequinar amplified cellular response to ssRNA in a similar manner to DD264 . Accordingly , brequinar also increased the expression of ISGs when stimulating cells with ssRNA ( Figure 4 ) . Altogether , these results demonstrated that inhibition of pyrimidine biosynthesis directly amplified cellular response to ssRNA and expression of ISGs . We have established a link between pyrimidine biosynthesis pathway and innate immune response . This opened the possibility that antiviral activity of DD264 is not a direct consequence of pyrimidine nucleoside deprivation that prevents viral transcription or replication , but rather relies on the amplification of the host antiviral response . To test this hypothesis , we treated HEK-293T cells with α-amanitin , a molecule that inhibits human RNA polymerase II and blocks transcription of cellular genes . As expected , α-amanitin showed some toxicity but did not impair MV replication . Most interestingly , MV inhibition by DD264 was abrogated in the presence of α-amanitin ( Figure 7A and 7B ) , thus demonstrating that the antiviral activity of DD264 required the transcription of cellular genes . Similarly , α-amanitin blocked the antiviral activity of brequinar ( Figure 7A and 7B ) . This argued against a direct impact of pyrimidine deprivation on viral replication , and rather involved the host response as assessed by the need for cellular gene expression . This conclusion was further supported by experiments performed with leptomycin B ( LMB ) , a potent inhibitor of Crm1-dependent nuclear export . Since cellular gene transcription is required for DD264 or brequinar to block viral growth , we reasoned that inhibiting mRNA export out of the nucleus should have similar effects . Thus , HEK-293T cells were infected with MV strain expressing luciferase , and cultured with DD264 or brequinar in the presence of LMB . As shown in Figure 7C , LMB efficiently restored viral replication in DD264 or brequinar-treated cells . Together with experiments performed with α-amanitin , this clearly involved both cellular gene transcription and nuclear export in the antiviral activity of DD264 and brequinar . Expression of ISGs is essentially controlled by members of the interferon regulatory transcription factor ( IRF ) family that bind IRE ( IRF regulatory elements ) or ISRE in promoter sequences of antiviral genes . In particular , expression of IRF1 was shown to drive the expression of many ISGs and confer resistance to various viruses [16] , [42] , thus demonstrating a key role in host resistance to viral infections . Therefore , we investigated the role of IRF1 in ISRE-luciferase and ISG expression , and in the antiviral state induced by DD264 . First , IRF1 gene silencing by siRNA transfection was found to abolish ISRE-luciferase expression in ssRNA-treated cells , whether DD264 was added or not ( Figure 8A ) . It should be noticed that when compared to Figure 3A , lower amounts of ssRNA were required to stimulate the ISRE-luciferase , suggesting that siRNA transfection had somehow sensitized cells to ssRNA in line with a recent report [43] . Furthermore , IRF1 silencing also suppressed the induction of various ISGs in ssRNA-treated cells , whether DD264 was added or not , whereas housekeeping genes were not affected ( Figure 8B ) . Similarly , IRF1 silencing suppressed the induction of ISGs by MV infection ( Figure S5A–L ) . Altogether , this demonstrated that IRF1 is essential for the expression of ISGs in these cells . If the antiviral activity of DD264 relied on the amplification of ISG expression as we hypothesized , knocking-down IRF1 was expected to suppress its antiviral activity . As shown in Figure 9A and 9B , IRF1 silencing partially restored MV and CHIKV replication in DD264-treated HEK-293T cells . IRF1 knockdown also restored viral growth in brequinar-treated cells ( Figure 9C and 9D ) . Similar results were obtained when performing this experiment with HeLa cells , establishing that DD264 or brequinar antiviral activity required IRF1 expression in both cell types ( Figure S6A–D ) . As a control , IRF1 expression levels in siRNA-treated HEK-293T or HeLa cells are presented in Figure S7A–B . This demonstrated the critical role of IRF1 in the antiviral state induced by DD264 or brequinar . Most importantly , this established that pyrimidine synthesis inhibitors , and in particular DHODH inhibitors , prevent viral replication by promoting antiviral gene expression , thus delineating a novel link between this metabolic pathway and innate immunity .
Here , we describe DD264 , a molecule that was selected from a high-throughput functional screen for its capacity to stimulate expression of interferon-inducible antiviral genes . This compound demonstrated a strong and broad antiviral activity that correlated with its capacity to amplify cellular response to ssRNA and expression levels of antiviral genes . We further showed that DD264 immuno-stimulatory activity depends on the inhibition of pyrimidine biosynthesis , similarly to brequinar , a well-characterized inhibitor of this metabolic pathway . Altogether , this allowed us to establish a yet unsuspected link between inhibition of pyrimidine biosynthesis pathway and stimulation of innate immunity . It is surprising that several groups , who recently performed functional screenings for inhibitors of viral growth in infected cells , all independently identified molecules targeting pyrimidine biosynthesis pathway and in particular DHODH [33] , [34] , [35] , [36] . This suggests a strong bias for inhibitors of this enzyme when looking for antiviral molecules with such functional assays . Altogether , our results support the idea that the antiviral activity of pyrimidine synthesis inhibitors , including aforementioned compounds [33] , [34] , [35] , [36] , does not simply rely on depriving viral polymerases of nucleosides , but is mediated through amplification of innate immune response . Conversely , other research groups have isolated broad-spectrum antiviral molecules while searching for stimulators of innate antiviral genes much like we did , using cell-based assays with reporter genes [9] , [21] , [22] , [23] , [24] , [25] . With our results in mind , it should be determined if the antiviral molecules they identified based on their capacity to stimulate interferon-inducible antiviral genes are in fact pyrimidine synthesis inhibitors , or if they stimulate innate immunity through alternative pathway . Our data show that antiviral activity of pyrimidine synthesis inhibitors such as DD264 or brequinar , although independent on IFN-α/β secretion ( Table S1 and S2; Figure S4 ) , critically relies on the expression of cellular genes as assessed by experiments performed with α-amanitin , leptomycin B and siRNA targeting IRF1 . IRFs are transcription factors that bind ISRE and closely related IRE elements in the promoter region of target genes . Most IRFs are critically involved in the regulation of immune response [44] , [45] , [46] , [47] , [48] , [49] , [50] . Since IRF1 is a master regulator of antiviral gene expression , as supported by literature [16] , [42] and confirmed by our own data , its implication in virus inhibition by DD264 and brequinar is consistent . But how inhibition of pyrimidine synthesis stimulates innate immune response remains a pending question . Interestingly , our data parallel a previous work showing that inhibition of pyrimidine synthesis induces a cellular stress that translates into p53 up-regulation and nuclear accumulation [37] . In this system , NAD ( P ) H:quinone oxidoreductase 1 ( NQO1 ) and NRH:quinone oxidoreductase 2 ( NQO2 ) induced p53 upregulation by preventing its degradation in the 20S proteasome . We did not detect any upregulation of IRF1 , as assessed by qRT-PCR and western-blot ( Table S1 and Figure S8A ) or modification of its nuclear localization pattern in DD264 or brequinar-treated cells ( Figure S8B ) , which could account for the increased activity of the ISRE promoter . However , IRF1 binds numerous cellular proteins , and these interactions can regulate the transcriptional activity of both IRF1 and associated transcription factors like p53 or NF-kB [51] , [52] , [53] , [54] , [55] . Besides , post-translational modifications of IRF1 , like ubiquitination , SUMOylation , and acetylation , have been reported to modulate its activity [53] , [56] , [57] . Associations with cofactors or post-translational modifications could increase IRF1 transcriptional activity in DD264 or brequinar-treated cells , and this could explain the enhanced expression of ISGs when blocking pyrimidine biosynthesis . Another interesting observation came from the analysis of other nucleoside synthesis inhibitors , in particular mycophenolic acid . This potent inhibitor of inosine monophosphate dehydrogenase ( IMPDH ) blocks the de novo synthesis of purine nucleosides , and is also known for its broad-spectrum antiviral activity since the 60's [38] . In a recent report by van der Laan and colleagues , mycophenolic acid was shown to increase expression of various ISGs , including IRF1 , when stimulating cells with low doses of IFN-α [58] . Furthermore , inhibition of hepatitis C virus replication by mycophenolic acid was shown partially mediated by IRF1 . These data clearly match our observations on pyrimidine synthesis inhibitors . Altogether , this suggests that inhibition of either purine or pyrimidine synthesis mediates a cellular stress that promotes expression of ISGs and induces a resistance state to infections by RNA viruses . Interestingly , inhibition of pyrimidine synthesis was recently found to reverse the inhibition of mRNA nuclear export by different virulence factors such as influenza virus NS1 or the matrix protein of vesicular stomatitis virus [59] . How this relates to our findings will have to be addressed in the future . Finally , although DD264 was found to inhibit the replication of various RNA viruses , this compound was inefficient to prevent cellular infection by two related DNA viruses: Herpes simplex virus type 1 ( HSV1 ) and type 2 ( HSV2 ) ( Figure S9 ) . This is surprising as pyrimidine biosynthesis inhibition was previously reported to block the replication of other DNA viruses like human adenovirus 5 or vaccinia virus [35] . This suggested that DNA viruses in general are sensitive to inhibitors of pyrimidine biosynthesis pathway , but HSV1 and 2 have apparently evolved some strategy to escape the cellular response induced by this type of drug . Indeed , herpes simplex viruses are well known for their large arsenal of virulence factors that block the antiviral response through different mechanisms [60] . Screening collections of HSV1 or 2 proteins to identify which ones are susceptible to alleviate the antiviral state induced by pyrimidine biosynthesis inhibitors should be relatively straightforward . This is probably a specific feature of Alphaherpesvirinae since human Cytomegalovirus ( CMV ) , which belongs to Betaherpesvirinae , was found sensitive to the inhibition of pyrimidine biosynthesis [61] . Interestingly , it was also reported that CMV and HSV1 have divergent effects on cellular metabolism . Although CMV enhances lipid biosynthesis , HSV1 rather promotes the synthesis of pyrimidine nucleosides [62] . This could also account for a different sensitivity to pyrimidine biosynthesis inhibitors . Importantly , although pyrimidine biosynthesis inhibitor leflunomide was reported to block HSV1 replication , some off-target effect of this drug on kinases is most likely responsible for this activity [63] . Our data also strongly suggest that DD264 directly inhibits DHODH , the fourth enzyme of pyrimidine biosynthesis metabolic pathway . This mitochondrial enzyme catalyzes dihydroorotate oxidation to orotate via a two-step mechanism that requires FMN and ubiquinone as prosthetic groups [64] , [65] . Best-known inhibitors of DHODH such as brequinar or A77-1726 , the active form of leflunomide , are inserted within a hydrophobic tunnel of the enzyme where they probably compete with ubiquinone [41] . Interestingly , DD264 exhibits two hydrophobic rings and a planar structure that matches chemical features of DHODH inhibitors targeting the ubiquinone-binding pocket , and this was confirmed by in silico docking experiments ( Figure S10A–C ) . This will require confirmation using enzymatic assays and structure analysis . Whether DHODH represents a viable target for antiviral treatments in vivo will need to be determined . Several attempts to use pyrimidine synthesis inhibitors in the treatment of systemic viral infections have failed [33] , [34] , [36] . This is probably because the high uridine concentration in peripheral blood ( about 3–8 µM ) compensates for the inhibition of the de novo pyrimidine synthesis pathway . However , it has been shown that A77-1726 , which blocks both kinase activity and de novo pyrimidine synthesis , could be useful in the treatment of airways when infected by respiratory viruses such as human respiratory syncytial virus [66] , [67] . This suggests that DHODH inhibitors should be further evaluated in vivo in the local treatment of respiratory tract infections . In conclusion , our data demonstrate that inhibition of pyrimidine synthesis increases expression of antiviral genes , which finally confers resistance to viral infections . In the future , physiological or pathological circumstances activating this transduction pathway will have to be determined .
The compound collection amounts to a total of 41 , 353 molecules arrayed in 520 96-well microplates . About one third of the collection comprises commercial chemical libraries acquired through Prestwick Chemical ( 1 , 120 compounds; www . prestwickchemical . com ) and ChemDiv ( eu . chemdiv . com ) , which provided 9 , 360 compounds from a kinase inhibitor subset library ( CDI ) and 4 , 624 compounds from a nucleobase analog subset library ( NECAN ) . The rest of the collection is from the French “Chimiothèque Nationale” [68] . All compounds were stored in DMSO at −20°C . Compounds from Prestwick Chemical and ChemDiv were at 2 mg/ml , which corresponds to 6 . 32±2 . 8 mM ( Prestwick Chemical ) , 5 . 57±0 . 89 ( CDI ) and 5 . 35±1 . 11 mM ( NECAN ) , respectively . Compounds from the “Chimiothèque Nationale” were at the following concentrations for the different subsets: Université de Lyon at 10 mM , Faculté de Pharmacie de Strasbourg at 2 mg/ml ( 7 . 43±2 . 48 mM ) , Centre d'Etude et de Recherche sur le Médicament de Normandie ( CERMN ) at 3 . 3 mg/ml ( 10 . 5±3 . 35 mM ) , Institut de Chimie des Substances Naturelles at 1 mg/ml ( 3 . 02±1 . 28 mM ) , Institut Curie at 2 mg/ml ( 7 . 34±2 . 76 mM ) , Institut Pasteur-A at 3 . 3 mg/ml ( 12 . 8±3 . 71 mM ) , Institut Pasteur-B at 2 mg/ml ( 6 . 81±2 . 13 mM ) , Mutabilis at 5 mM , and Novexel at 5 mg/ml ( 11 . 08±2 . 31 mM ) . Brequinar sodium salt hydrate was from Sigma ( SML0113 ) . α-amanitin was from Sigma ( A2263 ) . Leptomycin B from Sigma ( L2913 ) was kindly provided by Dr . Agata Budkowska ( Institut Pasteur ) . Short synthetic 5′-triphosphate RNA molecules ( ssRNA ) were synthesized from pCI-neo vector digested with XbaI using T7 RiboMAX Express large scale RNA production system ( Promega ) , and then purified with a filtering membrane ( Millipore ) . ELISA kits to determine IFN-α and IFN-β levels in culture supernatants were from PBL Biomedical Laboratoris . ELISA kit to determine IFN-γ level in culture supernatants was from eBioscience . Sheep polyclonal antibodies against IFN-α ( 31100-1 ) and IFN-β ( 31400-1 ) were from PBL Biomedical Laboratories . HEK-293T , HeLa and MRC5 cells ( ATCC ) were maintained in Dulbecco's modified Eagle's medium ( DMEM; Gibco-Invitrogen ) containing 10% fetal calf serum ( FCS ) , penicillin , and streptomycin at 37°C and 5% CO2 . Cellular viability was determined by quantification of adenosine triphosphate ( ATP ) in culture wells using the CellTiter-Glo Assay ( Promega ) following manufacturer's recommendations . Apoptosis was detected by TUNEL ( terminal deoxynucleotidyltransferase dUTP nick end labeling ) using the APO-Direct assay kit from BD . Cell cycling was determined by propidium iodide staining and flow cytometry analysis . Chikungunya virus ( CHIKV ) infections were performed with wild-type strain 05115 from La Réunion Island . The recombinant CHIKV strain expressing Renilla luciferase from a coding sequence inserted between nsP3 and nsP4 non-structural proteins has already been described [28] . All CHIKV stocks were produced on VERO cells , and titrated by TCID50 ( 50% Tissue Culture Infective Dose ) on HEK-293T cells . Recombinant measles virus strain expressing firefly luciferase ( rMV2/Luc ) or EGFP ( rMV2/EGFP ) from an additional transcription unit were derived from vaccine strain Schwarz , and have been previously described [69] , [70] . Virus stocks were produced on VERO cells , and titrated by TCID50 on VERO cells . The highly neurovirulent West Nile virus ( WNV ) strain IS-98-ST1 was propagated in mosquito AP61 cells and virus titer was determined on primate VERO cells by virus plaque-forming assays . The titer of IS-98-ST1 virus stock was about 10+10 plaque-forming units ( PFU ) per millimeter . Cy3-conjugated antibody against CHIKV E2 protein was previously reported ( Clone 3E4 ) [71] . Cy3-conjugated antibody against WNV E protein ( Clone E24 ) was kindly provided by Dr . MS . Diamond . Coxsackievirus B3 ( CVB3 , strain Nancy ) was kindly provided by Dr . M . Vignuzzi ( Institut Pasteur ) . Virus was amplified on HeLa cells , harvested by one freeze–thaw cycle and titrated by TCID50 . Herpes simplex virus type 1 ( HSV1 , strain KOS ) and 2 ( HSV2 , strain VR734 ) were kindly provided by Dr . T . Mourez ( Assistance Publique – Hôpitaux de Paris ) . Virus stock were produced on VERO cells , and titrated by TCID50 . Anti-herpesvirus antibody was from Argene ( Ref 11-090 ) . All robotic steps were performed on a TECAN Freedom EVO platform . All compounds were screened at a 1∶100 dilution of the original stock . Compounds were transferred from mother plates into white , flat bottom , bar-coded tissue culture 96-wells plates ( Greiner Bio One ) : 1 µl of a DMSO solution was spiked into dry wells of daughter plates ( 80 compounds per plate ) . For each plate , columns 1 and 12 were dedicated to controls: culture wells were alternatively spiked with 1 µl of DMSO alone ( negative control ) or supplemented with recombinant IFN-β so that final concentration equals 1 , 000 IU/ml ( positive control ) . Human HEK-293T cells were transfected in bulk with pISRE-luciferase reporter plasmid ( Stratagene , Ref 219089 ) . Cell transfection was performed with a 1 mg/ml poly-ethylenimine ( PEI “Max”; Polyscience ) solution adjusted at pH = 7 . For one 96-well culture plate , 17 µg of plasmid were diluted in 500 µl of DMEM ( Gibco-Invitrogen ) . In parallel , 53 µg of poly ( ethyleneimine ) from Sigma-Aldrich ( PEI ) were diluted in 500 µl of DMEM ( Gibco-BRL ) . PEI and plasmids were mixed together and incubated for 30 min at room temperature . This mix was added to 2×106 cells in a final volume of 10 ml of DMEM containing 10% fetal bovine serum , penicillin , and streptomycin . Finally , 100 µl of this cell suspension were added to each well of the microplate already containing one chemical compound . After 24 hours of incubation at 37°C in the presence of 5% CO2 , the firefly luciferase substrate ( Bright-Glo , Promega ) was added directly into the wells ( 50 µl ) and luciferase activity was measured 6 minutes later on a Safire2 ( TECAN ) using a 100 ms integration time . For each plate , means of luminescence and corresponding standard deviations were calculated for positive and negative controls ( μ+ , σ+ , μ− , and σ− , respectively ) to determine the signal-to-background ratio ( S/B = μ+/μ− ) and the Z′-factor ( Z′-factor = 1–3* ( σ++σ− ) / ( μ+−μ− ) ) . Average Z′-factor was determined to be 0 . 806±0 . 1 ( no value below 0 . 5; Figure S1 ) and signal-to-background ( S/B ) ratio , which corresponds to luciferase signal in the presence of recombinant IFN-β relative to DMSO alone , was >45 for all plates . Altogether , this demonstrated the robustness of our assay , which can be categorized as excellent [72] . For each compound , the induction factor was calculated as the ratio of luminescence signal measured in the corresponding well to the mean of luminescence for negative controls in the same plate . We generated a reporter plasmid carrying both the ISRE-luciferase gene and neo as a G418-resistance selection marker . First , pCi-neo plasmid ( Promega ) was digested with BglII and XbaI enzyme to remove the entire CMV promoter sequence . Plasmid extremities were filled using Pfu polymerase , and Gateway cassette C1 ( Invitrogen ) was cloned between the two blunt ends to produce a Gateway-compatible destination vector called pCi-neoΔCMV-GW . In parallel , the ISRE-luciferase sequence was amplified by PCR from pISRE-luciferase reporter plasmid ( Stratagene , Ref 219089 ) using Gateway ( Invitrogen ) primers AttB1-AACGTTATTTTTCACTGCATTCTAG and AttB2-AGATCTCACTGCTCCCATTCATCAG . Corresponding DNA fragment was cloned by in vitro recombination ( BP reaction ) into pDONR207 entry vector . ISRE-luciferase gene was finally recombined from pDONR207 into pCi-neoΔCMV-GW by in vitro recombination ( LR reaction ) . This new plasmid was transfected in HEK-293 cells ( ATCC ) using JetPrime reagent ( Polyplus Transfection ) . Two days later , culture medium was removed and replaced by fresh medium containing G418 at 500 µg/ml . Transfected cells were amplified and subsequently cloned by serial limit dilution . A total of 44 clones were screened for luciferase activity , and STING-37 clone was selected for its optimal signal to background ratio when stimulated or not with recombinant IFN-β . DHODH sequence was amplified by PCR ( Ex-Taq; Takara ) from an IMAGE full-length cDNA clone ( IMAGE ID: 6064723 ) , and cloned into pDONR207 using an in vitro recombination-based cloning system ( Gateway technology; Invitrogen ) . DHODH encoding sequence was subsequently transferred from pDONR207 into a modified pCI-neo vector ( Promega ) compatible with the Gateway system . Identical plasmids encoding for either cellular protein UBA3 or nsP4 of chikungunya virus were used as transfection controls ( CT1 and CT2 , respectively ) . Transfection was performed with jetPRIME reagent ( Polyplus Transfection ) following manufacturer's recommendations . Anti-DHODH monoclonal antibody was from Abnova ( Clone 6E1 ) . Silencer Select siRNA were purchased from Invitrogen , and transfected in STING-37 cells following manufacturer's recommendations . IRF1 silencing was achieved with a pool of two siRNA ( s7502 and s7503 ) , whereas controls correspond to a pool of two siRNA directed against IRF5 ( s7513 and s7515 ) . In each well of a 96-well plate , 2 pmol of siRNA were mixed with 20 µL of Opti-MEM ( Gibco-Invitrogen ) and 0 . 25 µL of Lipofectamine RNAiMAX transfection reagent ( Invitrogen ) . This mix was incubated for 10 minutes at room temperature , and supplemented with 80 µL of DMEM +10% FCS without penicillin and streptomycin and 15 , 000 STING-37 cells . Cells were incubated for 48 hours at 37°C and 5% CO2 , and then stimulated with DD264 and recombinant IFN-β at 500 IU/ml or transfected with ssRNA molecules with JetPrime . After 24 hours of culture , firefly luciferase activity was determined using the Bright-Glo reagent following manufacturer's recommendations ( Promega ) . Anti-IRF1 antibody was from Cell Signaling ( D5E4 ) . HEK-293T cells were plated in 24-well plates ( 2×105 cells per well ) . One day later , cells were transfected with 50 ng/well of ssRNA molecules . Transfections were performed with JetPrime PEI ( Polyplus transfection ) , and stimulated or not in the presence of DD264 . 24 hours later , cells were recovered in PBS and total RNA isolated with the RNeasy Mini Kit ( Qiagen ) according to manufacturer's protocol . Following elution , RNA yields were evaluated using a Nanodrop spectrophotometer ( Nanodrop technologies ) . A two-step qRT-PCR ( Taqman technology , Applied Biosystems ) was performed to measure transcription levels for 11 genes of interest ( primer references are indicated ) : IFI27 ( Hs00271467_m1 ) , IFI35 ( Hs00413458_m1 ) , IFI44 ( Hs00197427_m1 ) , IFI6 ( Hs00242571_m1 ) , IFIH1 ( Hs01070332_m1 ) , IFIT1 ( Hs01911452_s1 ) , IFIT3 ( Hs01922752_s1 ) , IFITM1 ( Hs00705137_s1 ) , ISG15 ( Hs01921425_s1 ) , MX1 ( Hs00895608_m1 ) and OAS1 ( Hs00973637_m1 ) . Expression levels of four housekeeping genes , including 18S ( Hs99999901_s1 ) , GAPDH ( Hs99999905_m1 ) , and HPRT1 ( Hs99999909_m1 ) , were also determined and used as internal reference controls . Starting from 1 µg of total RNA , cDNA synthesis was achieved in 20 µL using the SuperScript VILO cDNA Synthesis Kit following manufacturer's recommendations ( Life Technologies ) . Quantitative PCR reactions were performed on 0 . 6 µL of cDNA synthesis reaction mix using the TaqMan Fast Advanced Master Mix ( Applied Biosystems ) on a StepOnePlus Real-Time PCR machine ( Applied Biosystems ) . Results were normalized using expression levels of the four housekeeping genes . Transcription levels of IRF1 and IFN genes presented in Table S1 were determined with a two-step qRT-PCR ( SYBR green technology , SABiosciences ) using RT2 Profiler PCR Array System . Starting from 5 µg of total RNA , cDNA synthesis was achieved using RT2 First-Stand cDNA Synthesis Kit following manufacturer's recommendations ( SABiosciences ) . Quantitative PCR reactions were performed using RT2 qPCR SYBR Green Master Mix ( SABiosciences ) . HEK-293T cells were plated in 6-well plates ( 1×106 cells per well ) . One day later , culture medium was supplemented with increasing doses of DD264 or DMSO alone . After an additional 24 hours of culture , cells were harvested , carefully counted and monitored for viability by trypan blue exclusion , and washed with ice-cold phosphate-buffered saline ( PBS ) . All the extraction steps were performed on ice . Cellular pellets were deproteinized with an equal volume of 6% trichloroacetic acid ( TCA ) , vortex-mixed for 20 seconds , ice-bathed for 10 min , and vortex-mixed again for 20 seconds . Acid cell extracts were centrifuged at 13 , 000 rpm for 10 min at 4°C . The resulting supernatants were supplemented with an equal volume of bi-distilled water , vortex-mixed for 60 seconds , and neutralized by the addition of 5M K2CO3 . Cells extracts were subjected to an optimised SPE procedure as previously described [73] . All fractions were injected into the HPLC system separately and the results pooled to calculate the total amount of nucleotides and nucleosides present in the original samples . HPLC analysis was performed with a Shimadzu HPLC system interfaced to the LabSolution software . Samples were injected onto an ABZ Supelco 5 µm ( 150×4 . 6 mm ) column ( Sigma ) . The HPLC columns were kept at 40°C in a column oven . The mobile phase was delivered at a flow-rate of 1 ml/min during the analysis using a stepwise isocratic elution with a buffer containing 10 mM acetate ammonium adjusted to pH 6 . 9 . Detection was done with the diode array detector ( PDA ) . The LC Solution workstation chromatography manager was used to pilot the HPLC instrument and to process the data . The products were monitored spectrophotometrically at 254 nm and quantified by integration of the peak absorbance area , employing a calibration curve established with various known concentrations of nucleosides . Finally , a correction coefficient was applied to correct raw data for minor differences in the total number of cells determined in each culture condition . | Our therapeutic arsenal to treat viral diseases is extremely limited , and there is a critical need for molecules that could be used against multiple viruses . Among possible strategies , there is a growing interest for molecules stimulating cellular defense mechanisms . We recently developed a functional assay to identify stimulators of antiviral genes , and selected compound DD264 from a chemical library using this approach . While searching for its mode of action , we identified this molecule as an inhibitor of pyrimidine biosynthesis , a metabolic pathway that fuels the cell with pyrimidine nucleobases for both DNA and RNA synthesis . Interestingly , it was recently shown that inhibitors of this metabolic pathway prevent the replication of RNA viruses . Here , we established a functional link between pyrimidine biosynthesis pathway and the induction of antiviral genes , and demonstrated that pyrimidine biosynthesis inhibitors like DD264 or brequinar critically rely on cellular immune response to inhibit virus growth . Thus , pyrimidine deprivation is not directly responsible for the antiviral activity of pyrimidine biosynthesis inhibitors , which rather involves the induction of a metabolic stress and subsequent triggering of cellular defense mechanisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Inhibition of Pyrimidine Biosynthesis Pathway Suppresses Viral Growth through Innate Immunity |
Tissue-resident memory CD8 T ( TRM ) cells defend against microbial reinfections at mucosal barriers; determinants driving durable TRM cell responses in non-mucosal tissues , which often harbor opportunistic persistent pathogens , are unknown . JC polyomavirus ( JCPyV ) is a ubiquitous constituent of the human virome . With altered immunological status , JCPyV can cause the oft-fatal brain demyelinating disease progressive multifocal leukoencephalopathy ( PML ) . JCPyV is a human-only pathogen . Using the mouse polyomavirus ( MuPyV ) encephalitis model , we demonstrate that CD4 T cells regulate development of functional antiviral brain-resident CD8 T cells ( bTRM ) and renders their maintenance refractory to systemic CD8 T cell depletion . Acquired CD4 T cell deficiency , modeled by delaying systemic CD4 T cell depletion until MuPyV-specific CD8 T cells have infiltrated the brain , impacted the stability of CD8 bTRM , impaired their effector response to reinfection , and rendered their maintenance dependent on circulating CD8 T cells . This dependence of CD8 bTRM differentiation on CD4 T cells was found to extend to encephalitis caused by vesicular stomatitis virus . Together , these findings reveal an intimate association between CD4 T cells and homeostasis of functional bTRM to CNS viral infection .
Tissue-resident memory T cells ( TRM ) , the largest memory T cell subset , are non-recirculating cells parked in both nonlymphoid and lymphoid tissues [1–3] . The importance of CD8 TRM cells in limiting infections , their distinct transcriptional profile , the signals driving their differentiation , and their capacity to control reinfections at mucosal portals of pathogen entry are well documented [1 , 4] . Far less is known about the requirements for establishing CD8 TRM cells in non-mucosal tissues , particularly those populated by large populations of non-renewable cells , such as the brain , where rapid control of infection may prove lifesaving . Recent studies using acutely resolved viral meningo-encephalitidies have revealed the durability of brain-resident memory CD8 ( bTRM ) cells and their role in clearing CNS viral infections [5] . However , little is known of the requirements for establishing and maintaining CD8 TRM cells to persistent viral CNS infections . Polyomaviruses are natural pathogens that persist as silent , lifelong infections in healthy hosts of many vertebrates . Thirteen polyomaviruses to date have been identified as constituents of the human virome , but several [BKPyV , JCPyV , and Merkel cell polyomavirus ( MCPyV ) ] are opportunistic pathogens known to cause life-threatening diseases in immunocompromised individuals [6] . JCPyV is acquired in early adolescence probably via gastrointestinal routes of infection , reaches seropositivity rates over 60% by sixty years of age , and persists in the kidney , urinary tract , bone marrow , and possibly the brain [7 , 8] . With altered immune status as a consequence of HIV/AIDS , immune-modulating therapeutics for autoimmune diseases ( e . g . , natalizumab for relapsing-remitting multiple sclerosis ) , and biologic anti-cancer agents , JCPyV can cause progressive multifocal leukoencephalopathy ( PML ) [6] . Polyomaviruses productively infect and persist only in their host reservoir species . An acknowledged impediment to understanding PML pathogenesis and the immunovirologic factors that put patients at risk for PML is the absence of tractable animal models [9] . Human astrocytes/oligodendrocytes engrafted in brains of immune and myelin deficient ( RAG2-/-MBPshi/shi ) mice support JCPyV replication and virus-induced loss of these glial cells results in demyelination [10] . However , deciphering the immunological deficits that predispose patients to PML remains to be determined . CD4 T cells are necessary for regulating the phenotype and function of CD8 memory T cells in lymphoid organs [11] . In acute viral infections , CD8 T cells primed in the absence of CD4 T cells ( “unhelped” CD8 T cells ) lose the ability to produce effector cytokines such as IFN-γ , TNF-α , IL-2 , as well as the cytolytic protein granzyme B , and are unable to control primary infection or infections by reencountered pathogens [12–14] . Furthermore , memory differentiation is aberrant in unhelped CD8 T cells , as demonstrated by impaired upregulation of L-selectin ( CD62L ) , IL-7Rα ( CD127 ) , and the CD27 costimulatory molecule [15 , 16] . Recall responses of unhelped memory CD8 T cells to infection with vaccinia virus are restrained by PD-1 [17] , and vaccine-elicited unhelped CD8 T cells express multiple inhibitory receptors [18] . Unhelped CD8 T cells infiltrate the brain in response to vesicular stomatitis virus ( VSV ) [19] and lymphocytic choriomengitis virus ( LCMV ) [5] . Other models of central nervous system ( CNS ) viral infection , however , suggest that CD4 T cell help is necessary for CD8 T cell function and CD8 bTRM development . Unhelped CD8 T cells cannot control West Nile Virus ( WNV ) infection and gradually lose the ability to produce effector cytokines [20] . CD8 T cells in the CNS of CD4 T cell-deficient mice inoculated intracerebrally with a neurotropic mouse coronavirus had reduced IFN-γ and granzyme B expression , impaired viral control , disrupted memory differentiation , and increased apoptosis [21–23] . CD4 T cell help to CD8 T cells and B cells is also pivotal in the control of measles virus encephalitis [24 , 25] . For PML , it is interesting to note a case report documenting isolation of JCPyV DNA carrying a mutation that ablates a JCPyV-specific CD4 T cell epitope [26] . These studies highlight the discrepant data on the dependence of CD4 T cell help for sustaining CD8 bTRM formation during CNS viral infections , and point toward the possibility that such CD4 T cell dependence may be context-dependent . Mouse polyomavirus ( MuPyV ) is a ubiquitous natural mouse pathogen that establishes a lifelong infection [6 , 27] . MuPyV infects a wide variety of cells such as epithelial cells , mesenchymal cells , macrophages , and dendritic cells [28 , 29] . MuPyV persistently infects multiple organs including the spleen , brain , kidney , and bone marrow , with the site of inoculation affecting the organ distribution of persistent viral infection [30] . Mice lacking secondary lymphoid organs fail to generate an anti-MuPyV CD8 T cell response [31] . Previous work has shown that CD8 T cells contribute a large part of the host defense against MuPyV infection in the periphery [32 , 33] . In this study , we asked whether CD4 T cell help was essential for generating CD8 bTRM in mice infected with MuPyV . Upon intracerebral ( i . c . ) MuPyV inoculation , virus-specific CD8 T cells are recruited to the brain and establish a CD8 bTRM population [34–36] . MuPyV inoculated i . c . spreads systemically [34] . We found that unhelped virus-specific CD8 T cells infiltrated the brain and were functional during early stages of MuPyV infection , but failed to control virus during reinfection . We previously described the contrast in dependence of brain-infiltrating CD4 T cells , but not of CD8 T cells , on their circulating counterparts [34] . Here , we found that maintenance of unhelped CD8 T cells required resupply from CD8 T cells in the vasculature . The transcriptome of unhelped CD8 T cells showed disruption of genes involved in pathways of TRM function and homeostasis . Moreover , CD4 T cell insufficiency impaired differentiation of functional virus-specific CD8 bTRM not only at the stage of naïve CD8 T cell priming , but also after MuPyV-specific CD8 T cells had infiltrated the brain . The importance of CD4 T cells for homeostasis of virus-specific CD8 T cells during a persistent viral encephalitis has clear clinical ramifications for establishing durable immunosurveillance of persistent CNS infections .
A large body of evidence has shown that CD4 T cell deficiency during recruitment of naïve CD8 T cells has negative consequences on memory CD8 T cell differentiation [11] . To ask whether availability of CD4 T cell help during priming of virus-specific CD8 T cells affected recruitment and maintenance of CD8 T cells during MuPyV encephalitis , CD4 T cells were depleted by intraperitoneal ( i . p . ) administration of CD4 mAb before MuPyV infection and weekly thereafter until endpoint . CD4 T cell-sufficient and -deficient mice showed similar frequency and number of CD8 T cells specific for the dominant DbLT359 epitope in both the brain and spleen in acutely [day 8 post-infection ( p . i . ) ] and persistently ( day 30 p . i . ) infected mice ( Fig 1A & 1B ) . The helped and unhelped virus-specific CD8 T cell responses in the spleen decreased similarly between days 8 and 30 p . i . ( Fig 1B ) . In contrast , the frequency and number of virus-specific CD8 T cells in the brain did not significantly change between days 8 and 30 p . i . in either CD4 T cell-sufficient or–deficient mice ( Fig 1A ) . In addition , unhelped MuPyV-specific CD8 T cells in the brain proliferated similarly as compared to helped CD8 T cells and expressed Bcl-2 ( S1A & S1B Fig ) . Independent confirmation of these results was made using MHC II-/- mice inoculated i . c . with MuPyV , where no differences were found in the frequency or number of virus-specific CD8 T cells in the brains of MHC II-/- and wild type ( WT ) mice ( Fig 1C & 1D ) . This equivalence in helped vs unhelped virus-specific CD8 T cell responses in the brain mirrors that reported for WT and MHC II-/- mice given VSV i . n . [19] . CD4 T cell availability did not affect the pattern of effector/memory differentiation of MuPyV-specific CD8 T cells in either the brain or spleen based on surface co-expression of KLRG1 and CD127 , and expression of the transcription factors T-bet , eomesodermin ( eomes ) , TCF-1 , and Blimp-1 ( S1C–S1H Fig ) . We next asked whether unhelped CD8 T cells exhibited functional deficits . Previous studies have shown that CD4 T cell help is necessary for the development of functionally competent CD8 T cells [11] . In contrast , similar numbers of helped and unhelped DbLT359-specific CD8 T cells produced IFN-γ , TNF-α , and IL-2 , and retained cytotoxic effector potential ( i . e . , intracellular granzyme B and peptide-induced CD107 cell surface expression ) during acute and persistent MuPyV infection ( Fig 2A & 2B ) . In the spleen , however , fewer unhelped DbLT359-specific CD8 T cells produced IFN-γ in persistently infected mice ( S2A Fig ) . Helped and unhelped DbLT359-specific CD8 T cells in the brain had similar sensitivity to antigen stimulation , as evidenced by the expression of IRF4 ( Fig 2C ) , a transcription factor upregulated by TCR engagement [37] . Furthermore , IFN-γ mRNA and CXCL9 mRNA , an IFN-γ-induced chemokine , were upregulated compared to uninfected control mice similarly in brains of CD4 T cell-sufficient and -deficient mice ( Fig 2D ) . Although no difference in viral load was observed in brains of CD4 T cell-deficient and -sufficient mice during acute infection , viral loads trended higher during persistent infection in the absence of CD4 T cells ( Fig 2E ) . Thus , CD4 T cells appear not to overtly impact the magnitude , differentiation , or function of virus-specific CD8 T cells infiltrating the brains of MuPyV-infected mice . As we recently reported , approximately 40% of DbLT359-tetramer+ CD8 T cells in the brain express CD103 in persistently infected mice [35] . In CD4 T cell-deficient mice , few CD103+ MuPyV-specific CD8 T cells were detected in the brain 30 days after MuPyV inoculation ( Fig 3A & 3B and S3A Fig ) , although these cells expressed CD69 at levels similar to those in CD4 T cell-sufficient mice ( Fig 3C ) . During WNV infection of the brain , TGF-β produced from regulatory T cells is important for the upregulation of CD103 [38] . In our model , FoxP3+CD25+ CD4 T cells infiltrate the brain but constitute only 5% of CD44+ CD4 T cells in WT mice ( S3B Fig ) . After stimulation with PMA/ionomycin , brain CD4 T cells showed a transient 4-fold increase in TGF-β mRNA compared to unstimulated CD4 T cells ( S3C Fig ) . IL-21 has also been associated with establishing CD8 TRM and their expression of CD103 [39] . CD4 T cells produced >100-fold more IL-21 mRNA after PMA/ionomycin stimulation ( S3D Fig ) . Together , these data support the possibility that TGF-β and IL-21 contribute to upregulating CD103 on the virus-specific CD8 T cells during MuPyV infection . Furthermore , unhelped virus-specific CD8 T cells had higher PD-1 expression compared to helped virus-specific CD8 T cells ( Fig 3D and S3E & S3F Fig ) . Diminished expression of CD103 , a commonly used marker of TRM cell differentiation , and elevated PD-1 expression raised the possibility that CD4 T cell help qualitatively modulated MuPyV-specific CD8 bTRM residing in the brain . We recently demonstrated that systemic depletion of CD8 T cells after their entry into the brain did not impact their maintenance , while i . p . administration of a depleting CD4 mAb led to a dramatic decline in numbers of CD4 T cells in the brain [34] . These data indicated that brain-resident CD8 and CD4 T cells during MuPyV encephalitis showed a dichotomy in their dependence on cells in the circulation . We asked whether maintenance of unhelped MuPyV-specific CD8 T cells in MuPyV-infected mouse brain retained independence from the vascular compartment . To do this , CD8 T cell-depleting mAb was given at day 10 p . i . , which was after MuPyV-specific CD8 T cells had infiltrated the brain [34] ( Fig 3E ) . In CD4 T cell-sufficient mice , depletion of circulating CD8 T cells had no effect on the number of total CD8 T cells or DbLT359-specific CD8 T cells in the brain at day 30 p . i . ( Fig 3E ) . In marked contrast , the number of total CD8 T cells and DbLT359-specific cells declined approximately 100-fold in CD4 T cell-deficient mice depleted of circulating CD8 T cells at this timepoint ( Fig 3E ) . Together , these data suggest that CD4 T cell availability for development and maintenance of CD8 bTRM is critical during persistent viral CNS infections . A differential dependence of CD4 T cell help on development of virus-specific CD8 TRM in different viral systems may depend on the type of viral infection . To explore this possibility , we used a recombinant vesicular stomatitis virus encoding the DbLT359 epitope ( rVSV-LT359 ) [40] . CD4 T cell-sufficient and -deficient mice had similar frequencies of DbLT359-specific CD8 T cells in the brain 30 days after rVSV-LT359 intranasal ( i . n . ) inoculation ( Fig 4A ) . This result confirms that of Wakim et al . who found no differences in antigen-specific CD8 T cell responses between WT and CD4 T cell-deficient mice in brains of mice with VSV encephalitis [19] . We further observed that unhelped virus-specific CD8 T cells in brains after i . n . rVSV-LT359 inoculation failed to upregulate CD103 ( Fig 4B ) . PD-1 expression on unhelped CD8 T cells , however , was not significantly higher ( Fig 4C ) . Systemic CD8 T cell depletion resulted in loss of DbLT359-specific CD8 T cells in CD4 T cell-depleted mice , but not in CD4 T cell-sufficient mice given αCD8 ( Fig 4D ) . Using primers against VSV genomic RNA ( gRNA ) , we were able to detect low levels of VSV gRNA during persistence in both CD4 T cell-sufficient and–deficient mice ( S4 Fig ) . Similarly , a previous study reported persistent VSV gRNA after i . n . infection , but detected no VSV mRNA at the same time point [41] . Collectively , these data indicate that CD4 T cell help is essential for generating CD8 bTRM in both VSV and MuPyV CNS infections . To exclude the possibility that antibody-mediated CD4 T cell depletion increased the permeability of the blood brain barrier ( BBB ) and allowed CNS access by anti-CD8α , we measured extravasation of sodium fluorescein into brains of CD4 T cell-sufficient and -deficient mice 10 days after MuPyV infection . CD4 T cell-deficient mice showed no change in the concentration of sodium fluorescein dye in the brain , indicating that the integrity of the BBB was unaltered by systemic CD4 T cell depletion ( S5A Fig ) . Furthermore , systemically administered CD8 T cell-depleting mAb did not stain CD8 T cells in the brain parenchyma , irrespective of CD4 T cell status ( S5B Fig ) . Unhelped virus-specific CD8 T cells expressed the adhesion molecules VLA-4 , PSGL1 , and LFA-1 , suggesting that CD4 T cell availability did not alter the ability of these cells to home to and traffic into the infected brain ( S5C Fig ) . To ask whether unhelped CD8 T cells remained in the vasculature and , thus , directly exposed to depleting anti-CD8α , we performed intravascular staining with FITC-conjugated CD45 mAb . No difference in the ratio of extravascular to intravascular total and virus-specific CD8 T cells was seen between helped and unhelped mice ( S5D Fig ) . Collectively , these data confirm that bTRM become dependent on hematogenous replenishment in the absence of CD4 T cell help . A central defect of unhelped memory CD8 T cells in lymphoid tissues is their failure to expand upon reencountering cognate antigen [42] . TRM accelerate control of viral reinfection in nonlymphoid tissues [1]; however , a requirement for CD4 T cell help for bTRM to retain their recall response capability is unknown . We previously showed that MuPyV-infected mice , which possess potent neutralizing virus antibodies , mount recall responses in the brain after i . c . challenge with homologous MuPyV [36] . We asked whether availability of CD4 T cell help affects recall responses of virus-specific CD8 T cells to MuPyV reinfection and ability to control the challenge infection . Mice were depleted of circulating CD4 T cells before i . c . inoculation with MuPyV and then reinfected i . c . with MuPyV at day 30 p . i . ( Fig 5A ) . At day 5 after reinfection , viral load was significantly higher in CD4 T cell-deficient mice reinfected with MuPyV compared to CD4 T cell-sufficient mice with reinfection ( Fig 5B ) . However , the viral load was not significantly higher than CD4 T cell-deficient mice receiving mock rechallenge ( Fig 5B ) . Although the viral load was trending lower in rechallenged CD4 T cell-sufficient mice , the difference did not reach statistical significance ( Fig 5B ) . This loss of viral control was observed despite similar numbers and proliferation of brain virus-specific CD8 T cells ( Fig 5C & 5D ) . Although MuPyV-specific CD8 T cells proliferated rapidly in the reinfected mice , no significant increase was seen in the number of DbLT359+ CD8 T cells in the brain upon rechallenge . This discrepancy between cell proliferation and numbers suggests engagement of a concurrent cell death process . Interestingly , no difference in IRF4 was seen between CD4 T cell helped and unhelped virus-specific CD8 T cells , implying comparable levels of TCR activation ( Fig 5E ) . Yet , the frequency of IFN-γ+ CD8 T cells upon ex vivo LT359 peptide stimulation was lower in rechallenged CD4 T cell-deficient than -sufficient mice ( Fig 5F ) ; although significant , there was <10% difference between the mock vehicle injected persistently infected rat IgG-treated and CD4 T cell-deficient groups . Using IFN-γ eYFP reporter mice to visualize effector function by MuPyV-specific CD8 T cells in situ , we found that a significantly higher fraction of CD103+ than CD103- cells produced IFN-γ upon reinfection , with CD103- DbLT359 tetramer+ CD8 T cells in both CD4 T cell-sufficient and -deficient mice producing little IFN-γ ( Fig 5G ) . This defect was not due to a decrease in CD103- cells in brain ( Fig 5G ) . Without rechallenge , CD103- T cells from CD4 T cell -sufficient and -deficient mice have similar IFNγ-eYFP production to CD103+ CD8 T cells ( Fig 5H ) . These results indicate that CD4 T cells during recruitment and maintenance of CD8 bTRM are necessary for effective control of MuPyV CNS reinfection and improved ability to produce IFN-γ . Absence of virus-neutralizing antibodies may be associated with increased viral burden , with the consequent high antigen levels driving virus-specific T cell dysfunction . However , the contribution of antiviral antibodies to offsetting T cell exhaustion depends on the experimental viral system . MuPyV infection elicits a virus-neutralizing CD4 T cell-independent IgG response directed to VP1 , the major polyomavirus capsid protein [43 , 44] . Similarly , influenza virus infection also generates a T cell-independent influenza-specific IgG that helps resolve primary influenza infection and prevents reinfection [45] . Despite a decrease in αVP1 IgG titers in CD4 T cell-depleted mice at day 30 p . i . , sera from CD4 T cell-deficient and -sufficient mice exhibited strong virus-neutralization capability during acute and persistent infection ( Fig 6A & 6B ) . To formally exclude an effect of MuPyV-neutralizing antibodies on peripheral viral load and T cell function during MuPyV rechallenge , WT and MHC II-/- mice were passively immunized with a neutralizing VP1 IgG mAb [46] from day 10 p . i . to MuPyV i . c . reinfection at day 30 p . i ( Fig 6C ) . Despite passive immunization , unhelped virus-specific CD8 T cells still exhibited significant deficits in IFN-γ production ( Fig 6D ) , while PD-1 expression was increased compared to CD4 T cell-sufficient mice ( Fig 6E ) . Viral loads were similar in MHCII-/- mice with and without mAb VP1 treatment ( Fig 6F ) . Serum from MHCII-/- and WT mice that were passively immunized with αVP1 possessed similar virus-neutralization capabilities ( S6A Fig ) . These data demonstrate that virus-specific antibodies did not rescue the unhelped CD8 T cell response . The common defect in IFN-γ production by CD103- MuPyV-specific CD8 T cells in WT and CD4 T cell-deficient mice led us to survey the transcriptional landscape of CD103+ and CD103- DbLT359 tetramer+ CD8 T cells sorted from brains of persistently infected WT and MHCII-/- mice ( S7A Fig ) ; for this analysis , we refer to DbLT359 tetramer+ CD8 T cells from MHC-II-/- mice as MHCII-/--CD103- CD8 T cells and DbLT359 tetramer+ CD103- and CD103+ from WT mice as CD103- and CD103+ . 377 transcripts were differentially expressed between CD103- and MHCII-/--CD103- CD8 T cells , whereas 267 transcripts were differentially expressed between CD103+ and MHCII-/--CD103- CD8 T cells ( Fig 7A–7C ) . Only 73 transcripts , however , were differentially expressed between helped CD103- and CD103+ CD8 T cells ( Fig 7A–7C ) . These data reveal that CD103- and CD103+ cells had similar transcriptomes , both of which were substantially different from the transcriptomes of MHC II-/--CD103- cells . Our recent report showing similar phenotype and function by brain-resident , MuPyV-specific CD8 T cells irrespective of CD103 expression [35] are in line with the highly overlapping transcriptomes of CD103+ and CD103- CD8 T cells . These findings support accumulating evidence that caution is warranted when considering CD103 as a stereotypical marker of TRM differentiation [1 , 36] . Ingenuity pathway analysis of MHCII-/--CD103- vs CD103- CD8 T cells revealed significant aberrations in the activation state and homeostasis of unhelped CD8 T cells . MHCII-/--CD103- exhibited significant downregulation of pathways including cdc42 , actin cytoskeleton remodeling , and actin-based motility , which are essential for cell migration ( Fig 7D ) . Additionally , MHCII-/--CD103- CD8 T cells had downregulated RhoA signaling , which has recently been identified as a central regulator of CD4 T cell viability , proliferation , and migratory capacity in the CNS of EAE mice [47] . Notably , MHCII-/--CD103- CD8 T cells had significant downregulation of Runx3 ( S1 Table ) , a recently identified component of the transcription factor signature of CD8 TRM [48] . MHCII-/--CD103- CD8 T cells also showed significant upregulation of phosphoinositide pathways ( Fig 7D ) . Gain-of-function mutations in phosphoinositide pathways have been reported to promote exhaustion and senescence of CD8 T cells [49 , 50] . MHCII-/--CD103- CD8 T cells differentially expressed genes involved in mitochondrial function; mitochondrial dysfunction is highly prevalent in CD8 T cells isolated from HIV+ patients [51] . By comparison to MuPyV-specific CD8 T cells in brains of MHC-II-/- mice , the CD103+ and CD103- cells in brains of WT mice shared most of the same gene expression pathways ( S7B Fig ) . Thus , unhelped CD8 T cells during persistent MuPyV infection have a profoundly altered transcriptome in a pattern indicating defective homeostasis and activation . Because CD4 T cell deficiency is often an acquired rather than an inherited condition , we asked whether delayed systemic deletion of CD4 T cells affected development of functionally competent CD8 bTRM during MuPyV encephalitis . We therefore started i . p . administration of CD4 T cell-depleting mAb at day 10 p . i . ( Fig 8A ) . The number of CD4 T cells significantly declined in the brain with systemic anti-CD4 depletion ( Fig 8B ) . Although no difference was seen in the number of virus-specific CD8 T cells or viral load in the brain with delayed CD4 T cell depletion ( Fig 8C & 8D ) , the frequency of virus-specific CD103+ CD8 T cells was significantly lower at 30 days p . i . compared to CD4 T cell-sufficient mice ( Fig 8E ) . Because the frequency of CD103+ DbLT359-specific CD8 T cells was significantly reduced in CD4 T cell-deficient mice , we asked whether a decline in CD4 T cells affected development of virus-specific CD8 TRM cells after CNS infiltration . To this end , systemic CD4 T cell depletion began at day 10 p . i . , coupled with circulating CD8 T cells at day 20 p . i . ( Fig 8F ) . The number of total CD8 T cells and MuPyV-specific CD8 T cell and the gMFI of CD8 on the MuPyV-specific CD8 T cells were significantly reduced in CD4 T cell-deficient mice ( Fig 8G ) . We have previously published that increased CD8 gMFI marks bTRM [34] . We next assessed the ability of these unhelped DbLT359-specific CD8 T cells to control MuPyV challenge infection ( Fig 8H ) . Five days after reinfection , the number of DbLT359-specific CD8 T cells was not significantly different between CD4 T cell-sufficient and -deficient mice ( Fig 8I ) , but the frequency of IFN-γ-producing cells was significantly lower ( Fig 8J ) . Virus levels , however , were the same between CD4 T cell-sufficient and–deficient mice ( Fig 8K ) . These data support the concept that CD4 T cells are required to sustain functional CD8 bTRM to persistent viral encephalitis .
CD4 T cells modulate the differentiation program of pathogen-specific CD8 T cells that establish permanent residence as memory cells in mucosal barrier tissues , but their role in driving TRM development in non-barrier tissues is less understood [11] . In this study , we determined that CD4 T cell help was essential for establishment and maintenance of CD8 bTRM to MuPyV encephalitis . CD4 T cells guided the differentiation of MuPyV-specific CD8 bTRM during naïve T cell priming and were required for maintenance of functional antiviral CD8 bTRM in brains of persistently infected mice . Notably , CD4 T cell insufficiency resulted in diminished effector competence of antiviral CD8 T cells encountering MuPyV reinfection in the brain . An ongoing dependence on CD4 T cells for induction and maintenance of virus-specific CD8 bTRM to a persistent CNS infection has clear clinical implications for individuals whose immune status is altered by infection or immunomodulatory therapeutic agents . Accumulating evidence supports the likelihood that JCPyV adapts to selective pressure applied by virus-specific CD4 T cells . JCPyV recovered from PML patients carry mutations in the VP1 capsid protein that affect binding to sialylated glycans , which serve as receptors for JCPyV entry into host cells [52] . Thus , JCPyV-PML VP1 mutations are thought to alter viral tropism and endow JCPyV with neuropathic potential . Recent evidence , however , supports the alternative possibility that these VP1 mutations serve an immune evasion purpose by preventing recognition by neutralizing antibodies [53] and by ablating VP1-specific CD4 T cell epitopes [26] . These findings have motivated efforts to isolate monoclonal antibodies capable of broadly cross-neutralizing wild type and VP1 mutant JCPyVs to protect patients at-risk of and as therapeutics for PML [54] . In the pre-combination antiretroviral therapy era , PML had an approximately 5% incidence in patients with HIV/AIDS , a disease initiated by profound CD4 T cell deficiency [55] . Individuals with idiopathic CD4 T cell lymphopenia , which manifests without overt changes in CD8 T cells , B cells , or NK cells , are also at elevated risk for PML [56] . JCPyV VP1/LT-specific CD8 T cell adoptive immunotherapy in a PML patient drove viral DNA below PCR detectability in the CSF and improved neurological status [57] . Our data together with these studies suggest that preserving and augmenting anti-JCPyV CD4 T cells or providing “helper” cytokines in PML-susceptible individuals , and using such interventions to supplement CD8 T cell immunotherapy for PML , could promote differentiation of brain-infiltrating CD8 T cells into CD8 bTRM and improve disease prognosis . Upon LCMV infection in the periphery , CD4 T cell deficiency is associated with sustained high viral load , which in turn , upregulates checkpoint inhibitory receptors ( e . g . , PD-1 ) on virus-specific CD8 T cells . Blockade of these receptors drives recovery of effector competence and control of persistent infection [58–60] . In the MuPyV system , however , CD4 T cell deficiency did not result in higher virus levels , and helped and unhelped CD8 T cells had equivalent functional competence ( Fig 2 ) . Yet , PD-1 expression was increased on unhelped virus-specific CD8 T cells ( Fig 2 and S3 Fig ) . Similarly , unhelped CD8 T cells that infiltrate HSV-1-infected sensory ganglia upregulate PD-1 and retain effector functionality [61] . Additionally , HSV-1 latency is maintained [61] . Together , these data raise the intriguing possibility that the major role of CD8 T cells in the persistently infected CNS may be to control resurgent viral infection . RNA-seq analyses revealed profound differences in the transcriptomes of helped vs unhelped MuPyV-specific CD8 T cells from the brains of persistently infected mice . Pathway analyses point toward significant defects in cell migration by unhelped CD8 T cells , which may impair their ability to co-localize with virus-infected cells . Changes in mitochondrial function and loss of RhoA signaling pathways in unhelped CD8 T cells suggest that unhelped CD8 T cells may inadequately survey the infected tissue due to their defective T cell activation and metabolism [47 , 62] . Also , depressed functional integrity of unhelped CD8 T cells may necessitate resupply of new effector T cells from the circulation for their maintenance in the brain . The nature of CD4 T cell help changes over the course of persistent viral infections . A large body of literature documents that the magnitude of the CD8 T cell response during persistent viral infections is regulated , in part , by IL-21 and IL-2 produced by CD4 T cells [63–65] . Other studies , however , have shown that IL-10 , usually considered an immunosuppressive cytokine , can promote maturation of memory CD8 T cells [66] . CD4 T cells may indirectly affect the quantity and quality of CD8 T cell responses via helping anti-viral antibody production and affinity maturation to control extent of persistent infection [11] . Maintenance of CD8 T cells during persistent MuPyV infection may also depend on de novo priming of naïve virus-specific CD8 T cells . Ongoing de novo recruitment over an infection that changes dynamically , including progressing from systemic to tissue-localized infection , could contribute to CD8 T cell heterogeneity [67] . De novo recruitment of MuPyV-specific CD8 T cells is CD4 T cell-dependent; thus , the level of availability of CD4 T cell help may conceivably regulate this avenue for CD8 T cell differentiation , including those that populate a TRM cell compartment [68] . Similarly , in mice persistently infected with the neurotropic strain of mouse hepatitis virus , naïve CD4 and CD8 T cells were primed and recruited to the CNS [69] . In sum , these studies demonstrate that the nature of CD4 T cell help is dynamic . Virus-specific CD8 T cells control infections in the CNS via cytopathic and non-cytopathic effector mechanisms [70] . We previously reported that MuPyV infection was controlled in mice lacking TNF receptors or perforin and/or Fas as efficiently as in WT mice , and that IFN-γ and IFN-I inhibited MuPyV replication in vivo [32 , 33 , 71] . Likewise , IFN-γ and IFN-I inhibited JCPyV replication in established human glial cell lines and primary human glial cells [72 , 73] . Noteworthy is an incidental observation in a phase III clinical study to evaluate the effectiveness of IFN-γ on reducing incidence of opportunistic infections in HIV/AIDS subjects where none of the subjects in the IFN-γ cohort developed PML as opposed to a 10% incidence in the placebo group [74] . In this study , we found that reinfection with MuPyV was more efficiently controlled in CD4 T cell-sufficient than -deficient mice when CD4 T cells were systemically depleted at the time of naïve virus-specific CD8 T cell priming; however , in MuPyV-infected mice where CD4 T cell depletion was delayed , no difference in viral control was seen on i . c . reinfection ( Fig 8 ) . Because MuPyV-specific CD8 T cells suffered a deficit in IFN-γ functionality in early and delayed CD4 T cell-deficient situations , IFN-γ may not be the sole anti-MuPyV effector mechanism . In this connection , PML has also been diagnosed in patients with autoimmune rheumatic diseases , albeit not as frequently as in HIV/AIDS patients [75] , and anti-TNFα treatment was associated with a low incidence of PML in these patients [76] . Unlike the high frequency of CD103+ memory CD8 T cells detected in tissues following resolution of acute viral infections [19 , 77] , fewer than half of MuPyV-specific CD8 T cells express CD103 , an αE integrin that pairs with β7 to bind E-cadherin and retains T cells in tissues [35] . The role of CD103 in maintaining TRM appears to vary between tissues; its expression is particularly important for retention in small intestine mucosal epithelium and skin epidermis [78 , 79] . Interestingly , we found that virus-specific CD8 bTRM expressing CD103 had superior IFN-γ activity upon CNS re-infection with MuPyV ( Fig 5 ) . Yersinia pseudotuberculosis-specific CD8 TRM lacking CD103 localize with infectious foci in the intestinal lamina propria , where early inflammatory cues from infiltrating macrophages control the size of the CD103- population [80] . However , no geographic differences based on CD103 expression have been described for CD8 bTRM responding to brain infections [5 , 81] . Studies are ongoing to define anti-MuPyV effector mechanism ( s ) in the CNS and potential preferential expression of effector activities by antiviral CD8 T cells . We previously reported and confirmed here that stable maintenance of brain-infiltrating CD4 T cells to MuPyV encephalitis depends on ongoing replenishment from the vascular compartment; in sharp contrast , numbers of virus-specific CD8 T cells in the brain are unaltered by systemic CD8 T cell depletion [34] . Using the VSV encephalitis mouse model , we determined that CD4 T cell help also rendered virus-specific CD8 T cells susceptible to systemic CD8 T cell depletion . Thus , we found that the link between CD4 T cell help and establishment of CD8 bTRM applies to both MuPyV and VSV encephalitis . Loss of CD4 T cells either during naïve CD8 T cell priming or after CD8 T cell effectors have accessed the CNS renders brain-localized CD8 T cells dependent on those in the circulation . Evidence presented here supports the concept that an intact systemic CD4 T cell compartment is essential for preserving a steady-state détente between CD8 bTRM and persistent viral infection in the CNS .
All experiments involving mice were conducted with the approval of Institutional Animal Care and Use Committee ( Protocol 46194 ) of The Pennsylvania State University College of Medicine in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The Pennsylvania State University College of Medicine Animal Resource Program is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . The Pennsylvania State University College of Medicine has an Animal Welfare Assurance on file with the National Institutes of Health's Office of Laboratory Animal Welfare; the Assurance Number is A3045-01 . Adult ( 6–12 wks of age ) female and male C57BL/6 ( B6 ) mice were purchased from the National Cancer Institute ( Frederick , MD ) . Adult female and male B6 . 129-H2-Ab1tm1Gru N12 mice ( MHC class II-deficient ) were purchased from Taconic Farms ( Germantown , NY ) . Adult female and male C . 129S4 ( B6 ) -Ifngtm3 . 1Lky/J mice ( IFN-γ eYFP reporter ) were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . Mice were bred and housed in accordance with the guidelines of the NIH Guide for the Care and Use of Laboratory Animals and the Institutional Animal Care and Use Committee at the Penn State College of Medicine . MuPyV . A2 was prepared in baby mouse kidney cells as described [82] . Mice were infected intracerebrally ( i . c . ) with 3x105 PFU MuPyV . A2 in 30 μL as described [34] . For rechallenge , mice were inoculated i . c . with 3x105 PFU MuPyV . A2 in 30 μl at day 0 , re-inoculated i . c . with 3x105 PFU MuPyV . A2 in 30 μl or vehicle at day 30 p . i . , then euthanized 4–5 days later . Recombinant VSV expressing the MuPyV DbLT359 epitope ( VSV . LT359 ) was grown and titered on BHK-21 cells ( CCL-10; ATCC , Manassas VA ) [40] . Mice were infected intranasally with 5x104 PFU rVSV-LT359 diluted in PBS . Mice were injected i . p . with 250 μg rat anti-CD4 or 250 μg rat anti-CD8α ( clone GK1 . 5 or clone YTS169 . 4 , respectively; Bio X Cell , West Lebanon , NH ) or ChromoPure whole rat IgG ( Jackson ImmunoResearch Laboratories , West Grove , PA ) as indicated . Depletion was confirmed in peripheral blood by flow cytometry-based cell number assay using Absolute Count Standard ( Bangs Laboratories , Fishers , IN ) . For passive immunization studies , the mice were injected i . p . with 250 μg rat VP1 mAb or Chromopure whole rat IgG beginning at day 10 p . i . and continuing weekly . Mononuclear cells from brains were isolated from transcardially perfused or intravascularly stained mice by collagenase-DNAse digestion and percoll gradient centrifugation as described [34] . Mononuclear cells were isolated from spleen as described [36] . For intravascular staining , animals were injected i . v . with FITC-conjugated anti-CD45 ( clone 30-F11 , BD Biosciences ) through the tail vein three minutes before the brains were excised as described [83] . After isolation from perfused or intravascularly stained mice , cells were stained with Fixable Viability Dye ( eBioscience , San Diego , CA ) , APC-DbLT359 tetramers ( NIH Tetramer Core Facility , Atlanta , GA ) , and the following surface antibodies: CD8α ( clone 53–6 . 7 , eBioscience ) , CD44 ( clone IM7 , eBioscience ) , PD-1 ( clone RMPI-30 , Biolegend ) , Tim-3 ( clone RMT3-23 , Biolegend ) , 2B4 ( clone m2B4 ( B6 ) 4581 , Biolegend ) , CD103 ( clone M290 , BD Horizon ) , CD69 ( clone HI . 2F3 , Biolegend ) , CD49d ( clone MRF4 . 8 , Biolegend ) , CD162 ( clone 2PH1 , BD Biosciences ) , CD11a ( clone 2D7 , BD Biosciences ) , CD127 ( clone A7R34 , Biolegend ) , KLRG1 ( clone 2F1 , BD Biosciences ) , CD25 ( clone PC61 . 5 . 3 , Invitrogen ) , CD44 ( clone IM7 , BD Biosciences ) and CD4 ( clone RM4-5 , BD Biosciences ) . For intracellular staining , cells were permeabilized and fixed in FoxP3 buffer fixation and permeabilization solutions ( Thermo Fisher Scientific , Waltham , MA ) , and stained for T-bet ( clone 4B10 , Biolegend ) , eomes ( clone Dan11mag , Invitrogen ) , Ki-67 ( clone 2F1 , BD Biosciences ) , Blimp-1 ( clone 5E7 , BD Biosciences ) , Tcf-1 ( clone C6309 , Cell Signaling Technologies ) , Granzyme B ( clone GB11 , BD Biosciences ) , IRF4 ( clone IRF4 . 3E4 , Biolegend ) , and Bcl-2 ( clone BCL/10C4 , Biolegend ) . For intracellular cytokine stimulation assays , lymphocytes were isolated from brain and spleen , cultured in DMEM/10% FBS for 5 h at 37°C with or without 1 μM LT359 peptide [84] , stained with Fixable Viability Dye , anti-CD8α , and anti-CD44 , then permeabilized and fixed in FoxP3 buffer fixation and permeabilization solutions . Intracellular staining included anti-IFN-γ ( clone XMG1 . 2; Biolegend ) , anti-TNF-α ( clone XMG1 . 2; Biolegend ) , anti- IL-2 ( clone JES6-5H4 , Biolegend ) , anti- CD107a ( clone 1D4B , BD Biosciences ) , and anti-CD107b ( clone ABL-93 , BD Biosciences ) . CD4 T cells were stained with anti-CD4 and anti-CD44 , permeabilized as described , and then stained with FoxP3 ( clone FJK-16s , Invitrogen ) . Lymphocytes isolated from IFN-γ eYFP reporter mice were surface stained with anti-CD8α and anti-CD44 . The intracellular signal of YFP was amplified by staining with anti-GFP ( clone FM264G , Biolegend ) . Samples were acquired on a BD LSRFortessa ( BD Biosciences , San Jose , CA ) or BD LSR II ( BD Biosciences ) and analyzed using FlowJo software ( FlowJo , LLC , Ashland , OR ) . MuPyV major capsid protein VP1-specific ELISA were performed as described [84] . 10-fold serial dilutions of VP1 mAb [46] was used to obtain a standard curve on each of the 96-well plates , and the VP1-specific IgG concentrations were calculated using this standard . Antibody neutralization assays were conducted in NMuMG cells ( CRL-1636; ATCC ) . 10 μg of VP1 mAb ( positive control ) , rat IgG ( negative control ) , or sera diluted 1:10 from MuPyV i . c . infected mice was incubated at 37°C for 1 hr with 5 x 103 PFU/mL of MuPyV . The mixtures were then placed on 5 x 104 adherent NMuMG cells in 12-well plates and incubated at 37°C for 1 hr . mRNA was harvested 24 hrs later and subjected to viral large T antigen quantification as previously described [85] . 0% neutralization was determined based on the mean number of LT transcripts observed with MuPyV when incubated with IgG . 100% neutralization is set at the limit of detection for the PCR assay . For quantifying viral genome DNA copies , Real-Time PCR was performed on samples containing 10 ng DNA purified from brain and spleen using the Maxwell 16 nucleic acid isolation system ( Promega , Madison , WI ) as described [86] . For quantifying mRNA transcripts , total RNA was isolated from brain tissue per the manufacturer’s instructions . cDNA was prepared using random primers and RevertAid H Minus Reverse Transcriptase Enzyme ( ThermoFisher Scientific ) . SYBR green quantitative PCR with gene-specific primers from IDT Technologies ( Coralville , IA ) for IFN-γ , TGF-β , IL-21 ( fwd 5’-CTATGTGTTCTAGGAGAGATGCTG-3’ , rev 5’-GGAGGAAAGAAACAGAAGCACA-3’ ) , and CXCL9 mRNAs and 18s rRNA was performed on ABI StepOnePlus Real-Time PCR System ( ThermoFisher Scientific ) using previously published primer sequences [87–90] . Relative fold change over uninfected control mice was determined using the threshold cycle ( 2−ΔΔ CT ) method [89] . For detection of VSV gRNA , total brain RNA was isolated using the Maxwell 16 simplyRNA Tissue kit . cDNA was prepared as above and SYBR green qPCR was carried out with primers amplifying VSV gRNA ( fwd 5’-ATGTCACTGCAAGGCCTAAGA-3’ , rev 5’-ATCTCTCCTACCGCCTGATCC ) . VSV genomes per copy of 18S RNA was determined by 2 ( 18S CT–VSV CT ) . For the stimulation of CD4 T cells , WT mice were inoculated i . c . with MuPyV . At sacrifice , the brains were digested as described . CD4 T cells were purified from total brain homogenates using the EasyStep Mouse CD4 Positive Selection Kit II CD4 positive selection kit ( Stemcell Technologies , Vancouver , Canada ) . Purified CD4 T cells were stimulated with PMA ( 50 ng/ml ) and Ionomycin ( 1 ug/ml ) for 3 hrs at 37°C . After stimulation , the cells were lysed in 1 ml of Trizol and cDNA was prepared as described above . 100 μl of 100 mg/ml sodium fluorescein dye ( Sigma Aldrich , St . Louis , MO ) was injected i . p . into mice at day 10 after i . c . inoculation with MuPyV . A2 or at 24 h after LPS administration ( 100 μg/μl ) . After 45 min , mice were cheekbled and transcardially perfused with PBS + 10% heparin . A 3 mm section was taken from the cerebrum , then processed as described [91] with fluorescein concentrations calculated using a standard curve . Mice were treated i . p . with 250 μg of anti-CD4 or Rat IgG 4 days and 1 day before i . c . inoculation with MuPyV . At day 10 p . i . , the mice received 250 μg of anti-CD8α i . p . The mice were sacrificed 15 hrs after receiving CD8 T cell depleting antibody . At sacrifice , the mice were perfused with 10mL of 10% heparin in PBS followed by 10 mL of 4% paraformaldehyde ( PFA ) . Spleens and brains were postfixed in 4% PFA for 6 hrs and then sucrose dehydrated in 30% sucrose . 12 μm sections of brain and spleen were taken on a Leica biosystem cryostat ( model CM1850 , Buffalo Grove , IL ) . Sections were stained with rabbit anti-CD8A ( Sino Biological Inc . , Wayne , PA ) primary antibody and goat anti-rabbit ( Jackson ImmunoResearch , West Grove , PA ) and goat anti-rat ( Jackson ImmunoResearch ) secondary antibodies . Mononuclear cells were isolated from brains as previously described [34] and pooled from 3–4 mice into groups designated CD103+ , CD103- , and MHCII-/--CD103- . Live cells were stained with DAPI ( Sigma Aldrich , Germany ) , CD44 , CD8 , APC-DbLT359 tetramer , and CD103 and then sorted under BSL-2 conditions on a BD FACS Aria SORP ( BD Biosciences ) instrument . The collected cells were lysed with 1% IGEPAL CA-630 ( Sigma-Aldrich ) and immediately frozen on dry ice for storage at -80°C until further processing . The cDNA libraries were prepared using the SMARTer Ultra Low Input RNA Kit for Sequencing–v4 ( TAKARA Bio , CA ) and Nextera XT DNA Library Prep Kit ( Illumina , CA ) as per the manufacturer's instructions . The unique barcode sequences were incorporated in the adaptors for multiplexed high-throughput sequencing . The final product was assessed for its size distribution and concentration using BioAnalyzer High Sensitivity DNA Kit ( Agilent Technologies , CA ) . The libraries were pooled and diluted to 2 nM in EB buffer ( Qiagen , MD ) and then denatured using the Illumina protocol . The denatured libraries were diluted to 10 pM by pre-chilled hybridization buffer and loaded onto HiSeq SR Rapid v2 flow cells on an Illumina HiSeq 2500 ( Illumina , San Diego , CA ) and run for 64 cycles using a single-read recipe ( HiSeq Rapid SBS Kit v2 , Illumina ) according to the manufacturer's instructions . Illumina CASAVA pipeline ( released version 1 . 8 , Illumina ) was used to obtain de-multiplexed sequencing reads ( fastq files ) passed the default purify filter . Reads were mapped to the mm10 transcriptome with STAR [92] and gene-level quantification performed with RSEM [93] . Differential tests were performed in DESeq2 using the SARTools pipeline [94] The list of differentially upregulated and downregulated genes with FDR < 0 . 05 was imported to Ingenuity Pathway Analysis software ( Qiagen , Hilden , Germany ) for pathway enrichment analysis using Ingenuity Knowledge Base ( IKB ) as the reference set . All analysis was done using the software contextual analysis settings for mouse CD8 T cells . The enrichment significance by P-value between the gene list and the canonical pathway analysis was measured by Fisher's exact test . The enrichment factor is the ratio of the number of genes in a given pathway divided by the total number of genes in the pathway . Experimental data were analyzed on Prism 6 . 07 ( GraphPad , La Jolla , CA ) using Mann-Whitney test , one-way ANOVA , and two-way ANOVA with Tukey or Sidak’s multiple comparisons test . Error bars indicate mean ± SD . All experiments were replicated independently . | Tissue resident memory cells ( TRM ) persist in nonlymphoid organs serving as frontline defense against microbial reinfection . The requirements for generating pathogen-specific TRM to acutely resolved infections is well documented; however , little is known about the development of TRM to persistent infections . In this study , we investigated the importance of CD4 T cell availability to CD8 TRM development during persistent viral encephalitis . Using mouse polyomavirus ( MuPyV ) brain infection and systemic CD4 T cell insufficiency , we found that loss of CD4 T cells abrogated brain TRM ( bTRM ) development , disrupted the metabolic homeostasis of CD8 T cells , and reduced CD8 T cell responses upon viral reinfection . Additionally , CD8 T cells in CD4 T cell-deficient mice required resupply from circulating CD8 T cells , directly contrasting the independence from circulation in canonical TRM . Upon delayed CD4 T cell depletion and brain infection with an acutely resolving viral infection , CD8 T cells also had aberrant bTRM development and required resupply from the vasculature . Our findings demonstrate that CD4 T cells are essential for establishing long-lasting , virus-specific CD8 bTRM for antiviral CNS immunosurveillance . Our data also raise important clinical implications for developing therapies to augment CD4 T cell help to bolster protective CD8 bTRM responses . | [
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"t... | 2018 | CD4 T cells control development and maintenance of brain-resident CD8 T cells during polyomavirus infection |
Deworming is recommended by the WHO in girls and pregnant and lactating women to reduce anaemia in areas where hookworm and anaemia are common . There is conflicting evidence on the harm and the benefits of intestinal geohelminth infections on the incidence and severity of malaria , and consequently on the risks and benefits of deworming in malaria affected populations . We examined the association between geohelminths and malaria in pregnancy on the Thai-Burmese border . Routine antenatal care ( ANC ) included active detection of malaria ( weekly blood smear ) and anaemia ( second weekly haematocrit ) and systematic reporting of birth outcomes . In 1996 stool samples were collected in cross sectional surveys from women attending the ANCs . This was repeated in 2007 when malaria incidence had reduced considerably . The relationship between geohelminth infection and the progress and outcome of pregnancy was assessed . Stool sample examination ( 339 in 1996 , 490 in 2007 ) detected a high prevalence of geohelminths 70% ( 578/829 ) , including hookworm ( 42 . 8% ( 355 ) ) , A . lumbricoides ( 34 . 4% ( 285 ) ) and T . trichuria ( 31 . 4% ( 250 ) ) alone or in combination . A lower proportion of women ( 829 ) had mild ( 21 . 8% ( 181 ) ) or severe ( 0 . 2% ( 2 ) ) anaemia , or malaria 22 . 4% ( 186 ) ( P . vivax monoinfection 53 . 3% ( 101/186 ) ) . A . lumbricoides infection was associated with a significantly decreased risk of malaria ( any species ) ( AOR: 0 . 43 , 95% CI: 0 . 23–0 . 84 ) and P . vivax malaria ( AOR: 0 . 29 , 95% CI: 0 . 11–0 . 79 ) whereas hookworm infection was associated with an increased risk of malaria ( any species ) ( AOR: 1 . 66 , 95% CI: 1 . 06–2 . 60 ) and anaemia ( AOR: 2 . 41 , 95% CI: 1 . 18–4 . 93 ) . Hookworm was also associated with low birth weight ( AOR: 1 . 81 , 95% CI: 1 . 02–3 . 23 ) . A . lumbricoides and hookworm appear to have contrary associations with malaria in pregnancy .
In 1994 and 2002 the World Health Organization ( WHO ) recommended anthelminthics be given to girls , pregnant and lactating women to reduce the burden of anaemia in areas where hookworm and anaemia are common [1]–[3] . Published evidence suggests that mebendazole [4]–[8] or albendazole [9]–[14] administered after the first trimester of pregnancy are safe . However the advantage of routine deworming of pregnant women is debatable , with different studies presenting different results . Several studies reported that systematic anthelminthic administration was associated with less anaemia [2] , [4] , [6] , [10] , [11] , [13] and with a beneficial effect on birth outcomes , reducing the rates of low birth weight [5] , [7]–[9] , [12] , very low birth weight [15] , stillbirth and perinatal death [7] . However a Cochrane review , including three prospective randomised controlled trials studying the effect of deworming in pregnancy , concluded that the evidence to date is insufficient to recommend use of antihelminthics for pregnant women after the first trimester of pregnancy [16] . A recent randomised controlled trial in Uganda showed no benefit of anthelminthic treatment on maternal anaemia , low birthweight and perinatal mortality [17] . There are also conflicting and often confusing results regarding the impact of geohelminth infections on other infectious diseases , and in particular malaria [18]–[24] . While some studies have failed to find any relationship between geohelminth infection and malaria [25] , others have shown an increased incidence of P . falciparum malaria in presence of geohelminths [26]–[28] . Ascaris ( A . ) lumbricoides infections were linked to severe P . falciparum malaria in Senegal [29] but they have more often been associated with a beneficial effect on malaria [20] , [30]–[34] . Several immunological hypotheses , including modulation of T-helper or dendritic cell responses and cytokine induction , have been proposed to explain these interactions [35]–[40] . There have also been haematological and entomological hypotheses to explain increased incidence [19] . Data from studies specific to pregnancy and helminths are also conflicting . Hookworm , not P . falciparum malaria , was considered the main cause of anaemia in some [41] , [42] , while others reported an opposite result [43] , [44] or did not find any association [15] . Maternal co-infection with P . falciparum and helminths resulted in a significantly lower mean birth weight than with P . falciparum infection alone in Nigeria and Ghana [45] , [46] . Two recent studies report an association with lower rates of P . falciparum infection in women co-infected with A . lumbricoides [47] , [48] . Yatich and colleagues report a 4 . 8 ( 95% 3 . 4–40 ) fold increased risk of P . falciparum with any geohelminth and the risk remained significant for hookworm and A . lumbricoides alone [49] . Two cross-sectional helminth surveys ( 1996 and 2007 ) conducted among women attending antenatal clinics on the Thai-Burmese border were reviewed to determine whether there was any association between geohelminth infection and malaria in this area , endemic for both P . falciparum and P . vivax malaria species and where there has been no systematic deworming during pregnancy .
The Shoklo Malaria Research Unit ( SMRU ) has five established clinics on the Thai-Burmese border . One is based in the largest of the refugee camps , Maela ( circa 45 , 000 people ) ; the others are stretched along 100 km of the border and serve a migrant population of circa 50 , 000 people . Antenatal clinics ( ANC ) have been operational since 1986 in the camp and 1998 in the migrant population . Malaria transmission is low and seasonal [50] . Treatment is complicated by a high level of multi-drug resistant strains of P . falciparum [51] . There is currently no safe and effective P . falciparum antimalarial drug that can be offered as intermittent presumptive therapy ( IPT ) or prophylaxis to pregnant women . Active weekly detection and early treatment of malaria has so far been the best method to prevent maternal death from malaria in this area [52] . The ANC performs a weekly malaria smear for all women , 2nd weekly haematocrit , provides routine iron and folate supplementation , and all necessary medical and obstetric care . A mother-to-child HIV transmission prevention program started in 2001 in the refugee camp and was introduced to the migrant population in 2008 . HIV prevalence is low ( <1 . 0% ) and test uptake high ( >90% ) [53] . The antimalarial drug regimen for the treatment of P . falciparum in pregnant women was quinine or mefloquine mono-therapy in 1996 , and quinine or artesunate with or without clindamycin in 2007 . P . vivax episodes are treated with chloroquine alone . In 1996 women with non-severe ( mild ) anaemia ( haematocrit between 20% and 29 . 9% , HB between 6 . 7 g/L and 10 g/L ) were treated with ferrous sulphate ( 200 mg three times daily ) and folic acid ( 5 mg daily ) until delivery . Women with severe symptomatic anaemia ( haematocrit <20% , HB <6 . 7 g/L ) were transfused . In 2007 all women received ferrous sulphate , 200 mg daily and folic acid , 5 mg weekly , from first consultation to delivery and treatment doses as stated previously if they became anaemic . Thailand has no deworming policy for pregnant women nor do the agencies working in the refugee camps . Women were encouraged to deliver with trained midwives in the SMRU delivery rooms , those requiring Caesarean section were transferred to the nearest Thai Hospital . Gestational age was estimated by Dubowitz score [54] in 1996 and by ultrasound ( or Dubowitz for late scans ) in 2007 [55] . Birth weight was measured on electronic Seca scales ( accuracy 10 grams ) or Salter hanging scales ( accuracy 50 grams ) . Women participating in the surveys were of similarly deprived socioeconomic groups . Refugees in the camps receive food assistance and have access to medical care , but cannot work . Migrants work hard for low wages and lack access to medical care . Both groups are poor and economically weak [56] . Housing of refugee and migrant women are the same . Houses are elevated on poles of wood and walls and floors are made of bamboo with leaf roofing . Most families have their own toilets . Flip flops ( flat sandal ) are normally worn on the feet in all age groups . Contact with soil that is reportedly highly contaminated with helminth is inevitable [57] , more common in the rainy season and in those involved in agricultural work . Miscarriage was loss of products of conception or foetus before 28 completed weeks of gestation; stillbirth was delivery of a dead foetus aged 28 weeks or more; low birth weight ( LBW ) was a birth weight of <2500 grams measured in the first 5 days of life , and prematurity a delivery before 37 . 0 weeks of gestation; congenital abnormality was considered if a major defect was present at birth . The surveys in 1996 and 2007 were both conducted during the rainy season period ( May–Oct ) in order to allow comparison without having to take into account seasonality and because malaria peaks at that time of the year . Both surveys were exhaustive . The first survey , when SMRU only worked in the refugee camp , was to determine if worm infection was associated with anaemia . Every woman was asked to participate . The 2nd survey was done as a response to the preparation of a border wide medical guideline . The refugee camps on the border fall under the care of different NGOs ( Non-Government Organisations ) and there was debate on deworming in pregnancy . Since the last survey was old it was decided to resurvey pregnant women to determine if there was a need for deworming in pregnancy . At the time of this survey SMRU also provided antenatal care for migrants who have less access to health care than refugees . Hence refugee and migrant women were surveyed if they voluntarily gave a stool sample . Every 5th woman was asked to participate . Before each survey a general announcement was made to all pregnant women attending the ANC . Participation was voluntary . It was explained that if their stools were found positive for worms they would receive anthelminthic treatment . The importance of providing a fresh stool sample was explained . Stool samples were examined on site . As Necator americanus and Ankylostoma duodenale ova cannot be differentiated by microscopy , the term hookworm was used . Women with a positive stool test result for hookworm , A . lumbricoides ( roundworm ) or Trichuris ( T . ) trichuria ( whipworm ) were treated with mebendazole 200 mg once daily for 3 days . In 1996 this treatment was given after delivery , in 2007 at the time of diagnosis or after the first trimester . In this area the natural immunity to malaria is weak because the transmission is very low , so that most patients with malaria parasites become symptomatic . However because of the systematic weekly screening regardless of symptoms , many episodes are detected before symptoms arise . For this reason we cannot strictly speak of incidence or prevalence during the entire follow up period . Furthermore the number of infections of malaria relates not just to transmission but also to the poor response to antimalarial treatment . At the time of the 1996 survey quinine had an estimated failure rate of 23% and mefloquine 28% [58] . This makes it difficult to assign each episode as a new case , as it might be a treatment failure . In the analysis , women were categorized as “free of malaria” if all the malaria smears done at antenatal visits up to the day of the stool test were negative; women with a positive malaria smear up to the day of the stool test were categorized into one of 3 groups: “P . vivax group” or “ P . falciparum group” or “ mixed infection group” . Women in the P . vivax group only had one or more episodes of P . vivax , women in the P . falciparum group only had one or more episodes of P . falciparum and women in the mixed group may have had a single mixed infection of P . falciparum and P . vivax or on separate occasions a P . falciparum and a P . vivax . Results are given as proportions . Following the ANC system as well as participation in the stool survey was voluntary . Providing a stool sample did not involve any risk for the pregnant woman . For these reasons no informed consent was obtained . Pregnancy records have been routinely entered to a data recording system since 1987 . Ethical approval for analyzing these patient records was given by the Oxford Tropical Research Ethics Committee ( reference: OXTREC 28–09 ) . Stool samples were prepared using the formalin–ethyl acetate sedimentation technique [59] and hookworm ova counts performed . Two wet preparations were done for each sample to increase the sensitivity of detection and verify negative slides . The stool assessment was quantitative: a standard dilution of the stool sample was made and 100 µl ( taken with a Gilson pipette ) was put on a slide . The entire area under the cover slip ( 22×22 mm ) was examined with the X10 objective . Hookworm ova were counted and the number multiplied by 10 was the estimated number of ova per ml of faeces . Other geohelminths were reported as: 1 ova per slide rare , 2–3 per slide 1+ , 4–10 per slide 2+ and >10 per slide 3+ . In 1996 , all stool samples were quality controlled by laboratory staff ( WB ) from the Liverpool School of Tropical Medicine and Hygiene with good agreement . Thick and thin malaria smears were stained with Giemsa and examined under oil immersion; the presence of any asexual blood stage parasite was declared as malaria positive . Smears were declared negative after reading 200 fields . Blood samples ( finger prick ) were centrifuged at 12000 rpm for 3 minutes and read using a standard haematocrit reader . The haematocrit value measured the day of the stool test was used to describe anaemia . If not available , the result the closest to the stool test day ( but within 8 weeks prior to ) was chosen . Data were entered using Microsoft Access , and analyzed using SPSS version 14 for Windows ( SPSS , Benelux inc . , Gorinchem , Netherlands ) and Epi Info ( Centre for Disease Control and Prevention ) . Student's t-test and Mann-Whitney test were used for comparison of means and ranks respectively . Categorical data were compared using the chi-squared test or the Fisher's exact test , as appropriate . To assess independent predictors of malaria , anaemia and LBW , a multivariate unconditional logistic regression model was fitted using the variables that were significantly associated in univariate analysis .
Overall 70% ( 578 ) of the 829 women were infected with at least one geohelminth , including hookworm ( 43% ( 355 ) ) , A . lumbricoides ( 34% ( 285 ) ) or T . trichuria ( 31% ( 250 ) ) alone or in combination ( Table 2 ) . Prevalence was significantly higher in 1996 than 2007: 81% ( 95% CI: 76–84 ) ( 273/339 ) vs . 62% ( 95% CI: 58–66 ) ( 305/490 ) , P<0 . 001 . The intensity of worm infections was low , with high hookworm ova counts ( ≥1000 ova/mL ) found in <10% of the positive results , and a maximum count of 2900 ova/mL . Hookworm ( Table 2 ) and T . trichuria intensities of infection decreased between the 2 surveys , while A . lumbricoides decreased in all intensities except the highest group . Thirty three pregnant women had their first malaria infection after stool testing and were excluded from further analysis related to geohelminths and malaria and anaemia . For the purpose of this analysis geohelminth infections were assumed to be present until mebendazole treatment was administered , as there was no routine deworming policy . Overall 153/796 women ( 19% ) had malaria detected at least once prior to , or at the day of the stool test . Most of the women presented with single species infections; 35% ( 53 ) had P . falciparum infections only and 54% ( 83 ) P . vivax only . P . falciparum and P . vivax simultaneously or on separate occasions occurred in the remaining 11% ( 17 ) . The proportion of women with malaria in pregnancy was similar in those with geohelminth co-infection or without: 18% ( 44/242 ) vs . 20% ( 109/554 ) , P = 0 . 77 ( Table 3 ) . There were important differences in the proportions of women with malaria depending on the type of geohelminth co-infection ( Figure 1 ) . The highest proportions of both P . falciparum and P . vivax malaria were seen with hookworm ( ±T . trichuria ) co-infections and the lowest with A . lumbricoides ( ±T . trichuria ) co-infections . The protective effect of A . lumbricoides ( ±T . trichuria ) remained significant for P . vivax malaria when stratifying by malaria species ( Table 3 ) . The overall proportion of women with malaria in women with A . lumbricoides infections was approximately half that in hookworm infections . Temperature , days of fever , number of episodes of malaria and parasitaemia were not significantly different between the worm groups ( data not shown ) . There were only 3 women with hyper-parasitaemic malaria ( more than 4% of the red blood cells infected with P . falciparum ) , 2 of them without worm infection , and 1 woman with hookworm infection . The relationship between malaria and stool ova counts for the hookworm ( ±T . trichuria ) group and for ova count in the A . lumbricoides ( ±T . richuria ) group was explored ( Figure 2 ) . There was no relationship between malaria and the hookworm ( P = 0 . 76 ) or A . lumbricoides ( P = 0 . 92 ) stool ova counts . There was not sufficient data to study interaction between geohelminth single infections and their association with malaria , as nearly half of all infections were combinations of worms ( Table 2 ) . The proportions , by age and gravid groups , of women with malaria and those with geohelminths are presented ( Figure 3 ) . Age was not significantly associated with malaria , but gravidity was: 25% ( 49/198 ) in primigravida vs . 17% ( 104/595 ) in multigravida , P = 0 . 029 . The proportion of hookworm infection was higher in teenage women , although this was not significant . In a multiple regression model , primigravida ( AOR: 1 . 53 , 95% CI: 1 . 01–2 . 32 , P = 0 . 043 ) and hookworm co-infection ( AOR: 1 . 66 , 95% CI: 1 . 06–2 . 60 , P = 0 . 027 ) remained the two independent factors associated with an increased risk of malaria while the protective effect of A . lumbricoides co-infection remained significant ( AOR: 0 . 44 , 95% CI: 0 . 23–0 . 86 , P = 0 . 015 ) . Year of survey and ova counts for hookworm and A . lumbricoides were non-significant . Sixty five women ( 8% ) did not have a haematocrit measurement at the time of or before the stool test and were not included in this part of the analysis . Mean haematocrit was similar whether geohelminth infection was present or not in the remaining 733 pregnant women . The proportion of women with anaemia was higher in women with high intensity hookworm infection compared to those with lower counts , 41% ( 14/34 ) vs . 21% ( 150/699 ) , ( P = 0 . 011 ) ; in multigravida compared with primigravida , 24% ( 134/547 ) vs . 16% ( 30/183 ) , ( P = 0 . 024 ) ; those who were older than 25 years , 25% ( 100/394 ) vs . 19% ( 64/337 ) , ( P = 0 . 041 ) ; and those who had malaria , 30% ( 45/152 ) vs . 20% ( 119/581 ) , ( P = 0 . 021 ) . In a logistic regression model high hookworm load ( AOR: 2 . 05 , 95% CI: 1 . 01–4 . 20 ) , P = 0 . 049 ) , malaria ( AOR: 1 . 83 , 95% CI: 1 . 12–2 . 74 , P = 0 . 004 ) , being multigravid ( AOR: 1 . 79 , 95% CI: 1 . 15–2 . 78 , P = 0 . 009 ) and participating in the 1996 survey ( AOR: 1 . 57 , 95% CI: 1 . 03–2 . 08 , P = 0 . 032 ) remained independently associated with anaemia . Pregnancy outcome data were available for 94% ( 783/829 ) of women . There were 14 abortions ( 2% ) and 8 stillbirths ( 1% ) . Eleven infants were born with congenital abnormalities ( 8 live-births and 3 stillbirths ) . Neither stillbirth nor congenital abnormalities were significantly associated with geohelminth infection . The mean gestational age at delivery was 38 . 9±1 . 7 [28 . 4–42 . 5] weeks . Mean gestational age and the proportion of premature infants were not significantly different in the presence or absence of geohelminth infection . Birth weight data were available for 87% ( 648/748 ) of live-born , normal , singletons . Mean birth weight was 2900±447 [1100–4400] g . The proportion of LBW newborns was significantly higher among primigravida compared to multigravida ( 25% ( 41/162 ) vs . 10% ( 46/483 ) , P<0 . 001 ) , in women aged <25 years vs . older ( 19% ( 57/301 ) vs . 9% ( 30/345 ) , P<0 . 001 ) , with hookworm infection vs . none ( 17% ( 46/278 ) vs . 11% ( 41/370 ) , P = 0 . 048 ) , and in premature vs . term infants ( 58% ( 34/59 ) vs . 9% ( 53/589 ) , P<0 . 001 ) . Anaemia and malaria were not significant risk factors for LBW . In a logistic regression model excluding prematurity ( the strongest risk factor for LBW ( n = 59 ) ) , the presence of hookworm infection was independently but weakly associated with LBW ( AOR: 1 . 81 , 95% CI: 1 . 02–3 . 23 , P = 0 . 041 ) as was ( more significantly so ) being primigravid ( AOR: 3 . 27 , 95% CI: 1 . 83–5 . 84 , P<0 . 001 ) .
The proportion of intestinal geohelminth infections in pregnant women on the Thai-Burmese border is high and comparable to reports from other parts of South East Asia , including Thailand , Burma and Vietnam [60]–[62] , while malaria transmission is low [50] . The cross-sectional surveys conducted 11 years apart confirmed a declining prevalence of intestinal parasites but prevalence remains higher than the 20–30% WHO criteria for mass deworming [3] , [63] . Anaemia in this population is common but predominantly mild [64] and the association of hookworm infection and anaemia was only significant for the highest intensity of hookworm infection , a finding already reported half a century ago [65] . This site has a unique system of antenatal care established in 1986 in response to a high malaria related-maternal mortality rate ( 1000/100 , 000 live births ) [52] and lack of any drug to offer as chemoprophylaxis due to multidrug resistant strains of P . falciparum . Women are encouraged to attend ANC on a weekly basis . Attendance is high and most women average more than 10 consultations per pregnancy in both the refugee and migrant settings . Women with detectable parasitaemia on screening are treated regardless of symptoms as they are unlikely to clear parasitaemia without becoming symptomatic [66] . The reduction in malaria ( and anaemia ) incidence in pregnancy in this population has been described in detail elsewhere [67] . This is the first time , to our knowledge , that A . lumbricoides infection has been associated with a reduced risk of P . vivax . The novelty of this finding might be influenced by the fact that many studies on interactions between worms and malaria were done in Africa , where P . falciparum is the predominant species . Other investigators in African settings have described higher prevalence of falciparum malaria in presence of A . lumbricoides in pregnant women [49] and in children [29] . These differences could be related to acquired immunity , methodological issues or to interactions ( other helminths could alter the immune response e . g . schistomiasis or Strongyloides stercoralis ) . The density of infection of hookworm and A . lumbricoides in these surveys was low , as is usually reported from Asian settings , and no relationship was found between malaria and geohelminth density . Similar to a study in Kenyan pregnant women A . lumbricoides prevalence increased with gravidity , but whereas they observed the same trend with maternal age this was not observed on the Thai-Burmese border[48] . Hookworm prevalence peaked amongst the lowest age group , and reached a plateau after 25 years of age , which is similar to the pattern reported from Kenyan pregnant women[48] . Co-infection with hookworm was associated in our setting with a significantly higher risk of malaria , but this did not reach statistical significance for the individual plasmodial species . Similar associations have been previously reported for P . falciparum in children [68] , [69] , adults [19] and pregnant women [48] . In regions of high prevalence it is plausible that helminthes might suppress the ability to clear infections ( resulting in a positive association between helminth infection and asymptomatic malaria parasitaemia ) , or suppress the inflammatory responses that result in clinical disease ( resulting in a negative association between helminth infection and clinical malaria disease ) [21] but this seems unlikely in this setting where malaria prevalence is low . Subclinical haematological cues may influence the host attractiveness for the vector [19] . In Thailand hookworm was shown to be associated with increased incidence of P . falciparum but not P . vivax malaria [19] . In our setting , the replenishment of pregnant women's iron and folate reserves may have resulted in reticulocytosis and may have increased P . vivax densities [70] . This effect would be expected to be greatest in hookworm infections , and may well explain why the interaction could be observed in our study whereas it could not be in the study of Nacher [19] where patients did not receive haematinics . P . falciparum and to a much greater extent P . vivax prefer to invade young red cells . This would not account for the negative association seen between malaria and A . lumbricoides . However , an alternative hypothesis proposed by some authors is that residential location and spatial aspects of exposure may explain some of the associations between worms and malaria [71] . NGOs in the refugee camps have provided intermittent deworming ( 6–12 monthly single dose mebendazole ) to school children since late 2001 . There has been no deworming program for pregnant women or adults . It is likely that sporadic deworming of children and improved footwear or sanitation , has led to the decreased proportion of geohelminths in pregnant women observed between the two survey periods . Active weekly screening as part of routine antenatal care has made severe malaria a rare event . No association between disease severity and the prevalence of geohelminths could be demonstrated . The decreased proportion of women with mild anaemia between the two surveys could be related to the decrease of geohelminth infection; it also could be due to the reduction in P . falciparum observed on the Thai-Burmese border [72] , [73] or to the implementation of anaemia prophylaxis for all pregnant women . In Sierra Leone , the administration of iron and folate supplements had a greater effect on haematocrit than the administration of albendazole [11] . This suggests that deworming to prevent anaemia should not be used as sole strategy against anaemia [74] . If A . lumbricoides coinfection does indeed attenuate malaria , then mass deworming may reduce a potential protective benefit . On the other hand hookworm was associated with a higher proportion of malaria , low birth weight and anaemia suggesting that hookworm should be treated in pregnancy . As there is no specie selective antihelminth at hand , deworming policies should be based on local prevalence and intensity of geohelminths , malaria , and anaemia severity . The present paper has limitations: the surveys were cross sectional , pooling data from different periods and the sample size may not have been sufficient to detect quantitative effects of the different worm species on different plasmodial species . No plausible explanation has been provided for the observed associations . No socioeconomic , behavioral or environmental factors were available for analysis , however these tend to be uniformly similar across the population of refugees and migrant workers: These people live in poor conditions and all are economically deprived so that it is unlikely to be a confounder in the analysis . The assumption that worms observed in the stool sample were present at the time of malaria is plausible at the population level given the lifespan of worms but it is not possible to ascertain that was always the case in each individual . This may have reduced the precision . Nevertheless , the present paper presents for the first time in the same data set a range of complex interactions between hookworm , A . lumbricoides and both P . falciparum and P . vivax malaria , during pregnancy . Our findings potentially have considerable practical and evolutionary implications . Future trials to confirm or deny the associations observed here require well designed longitudinal studies to account for the observed complex and conflicting interactions . | Intestinal worms , particularly hookworm and whipworm , can cause anaemia , which is harmful for pregnant women . The WHO recommends deworming in pregnancy in areas where hookworm infections are frequent . Some studies indicate that coinfection with worms and malaria adversely affects pregnancy whereas other studies have shown that coinfection with worms might reduce the severity of malaria . On the Thai-Burmese border malaria in pregnancy has been an important cause of maternal death . We examined the relationship between intestinal helminth infections in pregnant women and their malaria risk in our antenatal care units . In total 70% of pregnant women had worm infections , mostly hookworm , but also roundworm and whipworm; hookworm was associated with mild anaemia although ova counts were not high . Women infected with hookworm had more malaria and their babies had a lower birth weight than women without hookworm . In contrast women with roundworm infections had the lowest rates of malaria in pregnancy . Deworming eliminates all worms . In this area it is unclear whether mass deworming would be beneficial . | [
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"diseases/... | 2010 | Complex Interactions between Soil-Transmitted Helminths and Malaria in Pregnant Women on the Thai-Burmese Border |
Insulin resistance ( IR ) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease . Both are associated with changes in the circulating metabolome , but causal directions have been difficult to disentangle . We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization ( MR ) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion . In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or β-cell responsiveness ( disposition index ) during an oral glucose tolerance test . These implicated bile acid , glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion . In MR analysis in two separate cohorts ( n = 2 , 613 ) followed by replication in three independent studies profiled on different metabolomics platforms ( n = 7 , 824 / 8 , 961 / 8 , 330 ) , we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid . A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis . In one of the largest studies combining “gold standard” measures for insulin responsiveness with non-targeted metabolomics , we found distinct metabolic profiles related to IR or impaired insulin secretion . We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development .
Insulin resistance ( IR ) is a major precursor of type 2 diabetes ( T2D ) [1] , and constitutes an independent risk factor for cardiovascular disease ( CVD ) [2] and for certain cancer types [3 , 4] . In IR , the demands on pancreatic β-cells to produce insulin increase and blood glucose levels rise if β-cell function is impaired . The metabolic effects of IR and declining β-cell function are not fully characterized and causal relationships are difficult to disentangle due to the lack of randomized controlled trials . Associations between the “gold standard” hyperinsulinemic-euglycemic clamp method [5] for measuring whole-body IR and non-targeted metabolomics profiling previously identified α-hydroxybutyrate as a biomarker for IR in 399 non-diabetic persons [6] . Additional insights for causal directions may come from profiling circulating metabolites combined with genotyping , as previously demonstrated in causal investigations of adiposity and the metabolome using a Mendelian Randomization ( MR ) approach [7] and for causal effects of uric acid on IR and T2D risk [8] . Mendelian randomization analysis can test the causal relationship between an exposure and an outcome variable in the absence of randomized controlled trials [9] . Exposure-associated single nucleotide polymorphisms ( SNPs ) can be used as instrumental variables ( IVs ) because allelic variants are randomly allocated at meiosis and therefore independent of bias from confounding and reverse causation . Genotype-based methods like MR can inform drug targeting: For example , the association between genetic variants in PCSK9 , reduced low-density lipoprotein-cholesterol levels and lower coronary heart disease risk [10] predicted the clinical success of pro-protein convertase subtisilin kexin 9-inhibitors [11] . Conversely , MR studies confirmed the lack of a causal association between plasma high-density lipoprotein-cholesterol and cardiovascular events [12] . The causal effects of impaired insulin secretion and IR on blood metabolites have not been assessed before in a large-scale metabolomics framework and could pinpoint key mediators of the risk of adverse health events . We aimed to identify metabolic pathways related to IR and impaired early-phase insulin secretion during an oral glucose tolerance test ( OGTT ) in a large European sample and applied MR methods in additional cohorts to assess potential causal effects of impaired insulin secretion and IR . Using non-targeted metabolomics [13] , we identified 52 circulating metabolites related to either IR or insulin secretion that implicated distinct metabolic pathways and we found evidence for a causal effect of IR on reduced palmitoleic acid ( POA ) and oleic acid ( OA ) levels , as well as on raised tyrosine levels .
The Uppsala Longitudinal Study of Adult Men ( ULSAM ) cohort of community-dwelling 71-year-old men provides the largest human sample combining plasma metabolomics with an OGTT and the hyperinsulinemic-euglycemic clamp method . We previously developed a bioinformatics pipeline for untargeted liquid chromatography/mass spectrometry ( LC/MS ) data [14] and were able to annotate among 10 , 162 spectral features 192 metabolites , on which this study is based . In up to 910 non-diabetic individuals , we used linear regression adjusted for age , sex , and sample quality indicators ( S1 Text ) to identify fasting metabolite levels associated with physiologic measures of insulin secretion and IR . Table 1 provides sample characteristics and Fig 1 illustrates the study flow . Three outcomes were assessed: IR ( clamp M/I ) , the insulinogenic index ( log-IGI30 ) as a measure of glucose-stimulated insulin secretion [15] and the disposition index ( log-DI ) for β-cell responsiveness [16] , all scaled to SD-units . At the 5% false discovery rate ( FDR ) , 47 , 15 , and zero metabolites were associated with clamp M/I , log-DI and log-IGI30 , respectively ( Fig 2 , S1 Table ) . Reduced levels of lysophosphatidylethanolamine ( LysoPE ) 18:2 and hippuric acid were associated with IR and impaired insulin secretion . Shared positive associations were found for deoxycholic acid glycine conjugate , corticosterone , propranolol , piperine , and three unsaturated fatty acids ( FAs; arachidonic , eicosatrienoic , and oleic acid ) . Higher levels of glycerolipids and several acylcarnitines , and lower levels of glycerophospholipids were exclusively associated with IR . Further , increased levels of two bilirubin species were exclusively associated with impaired insulin secretion . To identify metabolite associations independent of adiposity , we additionally adjusted each model for body mass index ( BMI , S1 Table ) . This reduced the strength of associations to a lesser extent for impaired insulin secretion than for IR and preserved the direction of associations for all metabolites still significant at the 5% FDR ( 34 for IR and 8 for impaired insulin secretion ) . Six new associations were detected for IR after BMI adjustment ( increased levels of three acylcarnitines , α-tocopherol , myristic acid and bilirubin , Fig 2 ) . Pathway enrichment analysis carried out with MetaboAnalyst 3 . 0 [17] ( http://www . metaboanalyst . ca/ ) indicated significant enrichment of IR-associated metabolites in primary bile acid synthesis ( p = 0 . 009 , 4 metabolites ) , glycerophospholipid metabolism ( p = 0 . 006 , 4 metabolites ) and caffeine metabolism ( p = 0 . 016 , 2 metabolites ) ( S1 Fig ) . Impaired insulin secretion-associated metabolites were enriched in the FA biosynthesis pathway ( p = 0 . 027 , 3 metabolites ) . We attempted replication for metabolites with suggestive evidence of causation ( p < 0 . 1 ) in up to 7 , 824 European individuals in the KORA F4/TwinsUK cohorts that underwent untargeted metabolomics profiling on a different LC/MS platform [19] . The liberal p-value threshold was chosen as a compromise between limited sample size and the risk of missing true positive associations in the MR discovery set and we adopted the conventional threshold of p < 0 . 05 for significance in all replication analyses . Seven of the nine causally implicated metabolites for IR ( excluding MAG ( 14:0 ) and 3α , 6β , 7β-trihydroxy-5b-cholanoic acid ) and one of two ( bilirubin ) for impaired insulin secretion were available in KORA/TwinsUK . We replicated the causal effect of IR on POA ( βIV = –0 . 48 , 95% CI –0 . 93 to –0 . 03 p = 0 . 038 , all in SD-units ) and the tentative effect on OA ( βIV = –0 . 38 , 95% CI –0 . 81 to 0 . 04 , p = 0 . 078 ) ( Fig 3 ) . We could not replicate the causal effects on tyrosine ( βIV = 0 . 20 , 95% CI –0 . 19 to 0 . 59 , p = 0 . 316 ) and hippuric acid ( βIV = –0 . 18 , 95% CI –0 . 61 to 0 . 24 , p = 0 . 398 ) . A repeat analysis in a subset of 1 , 432 non-diabetic individuals in the KORA F4 cohort replicated the causal effect on POA ( βIV = –1 . 14 , 95% CI –2 . 13 to –0 . 15 , p = 0 . 024 ) and the tentative effect on OA ( βIV = –0 . 82 , 95% CI –1 . 75 to 0 . 10 , p = 0 . 082 ) ( S1 Text ) . In a second attempt to replicate the causal findings for POA and OA , we obtained the publicly available GWAS results for FA fractions in plasma phospholipids from the Cohorts for Heart and Aging Research in Genomic Epidemiology ( CHARGE ) consortium ( http://www . chargeconsortium . com/main/results ) [20] . The CHARGE study combines five independent cohorts of 8 , 961 Europeans in age , sex , and recruitment site-adjusted meta-analysis and we performed MR analysis based on the genetic IR score , as all 10 SNPs had been genotyped ( S1 Text , FA fractions were converted to SD-units ) . The results replicated the causal effect of IR on POA ( βIV = –0 . 29 , 95% CI –0 . 54 to –0 . 05 , p = 0 . 018 ) and OA ( βIV = –0 . 48 , 95% CI –0 . 73 to –0 . 24 , p = 0 . 007 ) . We aimed to replicate the possible causal effect of IR on raised tyrosine using the summary meta-GWAS results from five cohorts of 8 , 330 Finnish individuals who underwent nuclear magnetic resonance metabolomics profiling [21] . In this sample , no significant effect on serum tyrosine levels was found ( β = 0 . 21 , 95% CI –0 . 16 to 0 . 57 , p = 0 . 267 ) . Achieving adequate power in MR analysis requires large sample sizes [22] . However , the differences in analytical methods between studies for the same metabolite make the combination of within-study effects in post-hoc meta-analysis methodologically unsound . To nonetheless explore the effect of increased sample size on causal effects , we combined estimates for each metabolite in inverse variance-weighted fixed-effects meta-analysis and illustrate these exploratory estimates in Fig 3 . In addition to the negative effects on POA and OA , the combined analysis indicated a causal effect of IR on higher tyrosine levels ( p < 0 . 05 ) that was observed as a non-significant trend in each individual study . Given the risk of false positive findings due to multiple testing , these post-hoc findings should be interpreted with caution and require confirmation studies . To assess for violations of MR assumptions due to genetic confounding , horizontal pleiotropy and IV heterogeneity , we examined a ) the association of individual SNPs with the risk factors ( S2B Fig ) ) ; b ) the association of IVs with potential confounders ( S3C Fig ) ) ; c ) scatter plots of IV-outcome v . IV-risk factor associations and funnel plots of IV strength v . IV estimate ( S4D Fig ) ) ; d ) implemented sensitivity analysis for individual SNPs in inverse variance-weighted , log-likelihood and MR Egger regression , including heterogeneity tests ( S4 Table , S1 Text [23–25] ) ; and e ) discuss in S1 Text the likelihood of bias from canalization and other effects . We tested for associations of genetic scores with potential mediator variables in age- and sex-adjusted linear regression in ULSAM , PIVUS and TwinGene ( S3 Fig ) and confirmed the association between worse genetic IR and lower HDL-C as well as smaller waist-hip ratio as reported by Scott et al . [18] ( who included ULSAM ) , and found an association with higher albumin levels . As in [18] , the impaired insulin secretion score was positively associated with plasma glucose and unrelated to other traits apart from lower albumin levels and a trend for increased C-reactive protein . For all metabolites with indication for a causal effect ( p < 0 . 1 ) in the MR discovery sample , we examined individual SNP effects on metabolite levels . The IV-exposure associations were derived from the GWAS results for homeostasis model assessment-IR ( HOMA-IR , n = 46 , 186 ) and corrected insulin response ( CIR , n = 5 , 318 ) in non-diabetic persons from the publicly available results of the Meta-Analyses of Glucose and Insulin-related traits Consortium ( MAGIC; http://www . magicinvestigators . org/downloads/ , converted to SD-units ) . The association between SNPs and risk factors in MAGIC were consistent and did not indicate heterogeneity apart from one variant ( rs11605924 ) that was associated with CIR in the unexpected direction ( S2 Fig ) . As reported in S4 Table , Cochran’s Q tests failed to detect significant heterogeneity between individual SNPs’ causal estimates . The significant effects of IR on OA and POA levels detected in the main analysis were replicated in all sensitivity tests except in the case of MR Egger regression for POA ( slope estimate –0 . 96 , 95% CI –2 . 15 to 0 . 22 , p = 0 . 095 ) . Although the intercept estimate in MR Egger regression for OA differed significantly from zero , it was small in magnitude ( intercept estimate 0 . 02 , 95% CI 0 . 00 to 0 . 04 , p = 0 . 018 ) and the causal effect was reproduced ( slope estimate –0 . 49 , 95% CI –0 . 82 to –0 . 16 , p = 0 . 010 ) . One important assumption of MR Egger regression–that any pleiotropic effects of IVs on the outcome be independent of IV strength [23]–cannot be assessed with currently available methods . Because the IV score had been carefully constructed by Scott et al . [18] to limit the likelihood of pleiotropic interference ( particularly from BMI ) and based on the totality of all MR sensitivity analyses that reproduced the main results in direction and magnitude and failed to indicate significant heterogeneity , we are confident to have excluded pleiotropic effects that could invalidate our main findings as far as possible . However , as discussed in S1 Text , some sources of bias ( e . g . , canalization ) cannot be excluded and the possibility of remaining pleiotropic effects pose limitations that mandate careful interpretation of any MR study . To further assess our findings of a negative causal effect of IR on monounsaturated FA levels , we looked up gene expression data for SCD1 and its rodent equivalent scd1 in the EMBL-EBI Expression Atlas v3 . 0 ( http://www . ebi . ac . uk/gxa/home ) . This gene encodes stearoyl-CoA desaturase 1 ( SCD-1 ) , the rate-limiting enzyme in the biosynthesis of OA and POA [26] . Among 252 uploaded experiments that reported significantly different expression ( 5% FDR ) between experimental and control conditions , we extracted all experiments related to IR . There were eight studies in mice and rats–none in human beings ( S1 Text ) . In all instances , the direction of differential scd1 expression was consistent with our findings—the IR-increasing condition down-regulated scd1 expression .
In the observational part of this multi-cohort study of blood metabolomics profiles , we identified bile acid , glycerophospholipid and caffeine metabolism as associated with IR , and FA biosynthesis as related to impaired insulin secretion . We discovered and replicated causal effects of IR on lower levels of the monounsaturated FAs POA and OA , as well as suggestive evidence for higher levels of the aromatic amino acid tyrosine . Sensitivity analyses did not indicate pleiotropic effects of the genetic instruments . Causal effects were largely unaffected by the exclusion of prevalent diabetes cases in the KORA/TwinsUK replication set . A small collection of publicly available experimental results in rodents supported our causal findings: All IR-increasing conditions were associated with a down-regulation of SCD-1 , implying reduced endogenous production of OA and POA . The liver and adipose tissue are the main sites of de novo lipogenesis and SCD-1 is the rate-limiting enzyme for monounsaturated FA biosynthesis [27] . It introduces double bonds into palmitic and stearic acid to produce POA ( 16:1n-7 ) and OA ( 18:1n-9 ) , respectively–the major precursors for cholesteryl esters and triglycerides ( TGs ) that are packaged into very low-density lipoprotein ( VLDL ) particles and secreted by the liver [28] . In scd1 knockout mice and hypertriglyceridemic persons , plasma FA composition reflects hepatic SCD-1 activity [29 , 30] . Hence , whilst dietary FAs contribute to overall plasma FA levels , the relative lipid composition , as assessed in the present study , is likely to reflect SCD-1 activity . Correspondingly , in ULSAM and PIVUS , we found good agreement between FA quantification by untargeted plasma metabolomics and targeted serum cholesteryl ester analysis ( S1 Text ) . Experimental evidence for the inhibition of SCD-1 by IR stems from liver-specific insulin receptor knockout ( LIRKO ) mice that had ~80% reduced hepatic scd1 expression and ~90% reduced microsomal scd1 transcript levels compared to control mice [31 , 32] . In muscle-specific insulin receptor knockout ( MIRKO ) mice , scd1 expression was downregulated by ~23% compared to controls [33] . Little is known about the causal effects of a genetic predisposition for IR on SCD-1 [34] . Reduced SCD-1 activity in knockout mice has beneficial metabolic consequences , including reduced obesity [35] and improved IR [36] ( reviewed in [28] and [34] ) . Yet , reduced SCD-1 activity has also been associated with adverse vascular outcomes: Inhibition of SCD-1 in hyperlipidemic mice markedly increased aortic atherosclerosis despite protective effects on obesity and IR [37] . Reduced SCD-1 activity caused enrichment of saturated FA in VLDL and LDL , which promoted atherogenesis through macrophage-induced vascular inflammation . Susceptibility to exacerbated inflammation was also demonstrated in scd1 knockout mice with induced colitis [38] or on a very low-fat diet that increased endoplasmic reticulum stress response [39] . Based on these competing effects on vascular and metabolic health , we speculate that IR reduces SCD-1 activity , which counteracts the metabolic consequences of IR by improving insulin signaling but concomitantly increases the risk for CVD through saturated FA-induced proinflammatory changes . Supported by our results and the above evidence , this hypothesis further derives from two facts: IR increases CVD risk independent of other risk factors [2 , 40] and IR predicts CVD risk independent of T2D [41] . A summary of the presumed relationships is displayed in Fig 4 . In our study , we could not evaluate the longitudinal effect on CVD , hence our hypothesis needs to be evaluated in cohorts with the available outcome data . In observational analysis , IR was positively associated with OA ( FDR-adjusted p = 0 . 035 , after adjustment for BMI pBMI > 0 . 05 ) and POA levels ( p = 0 . 091 , pBMI > 0 . 1 ) in contrast to the negative genetic associations in MR studies . This discrepancy is likely due to confounding factors such as dietary FA intake and highlights that MR studies have the power to disentangle causal mechanisms that may be obscured in cross-sectional studies . The hypothesized but untested relationship with cardiovascular disease requires investigation in future studies . The effect of IR on elevated tyrosine levels was observed as a non-significant tendency in all cohorts but reached nominal significance only after combination of estimates in meta-analysis . Although there is a considerable risk that this result is a false positive due to multiple testing , we still consider it an interesting finding worth investigating in larger MR studies , as it has not been reported before . Associations between worse IR/T2D risk and circulating tyrosine levels have been established in observational and longitudinal studies [42 , 43] . Possible molecular mechanisms could involve reduced tyrosine catabolism , either through IR-induced oxidative stress leading to elevations in methionine , cysteine and the antioxidant glutathione that result in tyrosine hydroxylase inhibition [44]; or through inactivation of tyrosine aminotransferase [45] . Hence , we speculate that the previously identified association between tyrosine and increased T2D risk could be caused by concomitant IR but caution that the observed trends in our study need to be verified in larger samples . Limitations of our study include moderate power in MR studies and inherent limitations of the non-targeted metabolomics discovery platform to capture certain metabolites ( e . g . , polar amino acids ) and separate sugars and other non-polar molecules . We used an untargeted metabolomics approach that detected several thousand metabolic features , yet the limited availability of standard compound spectra in in-house and public libraries precluded the quantification of the entire spectrum of the plasma metabolome and may have biased the pathway enrichment analysis . Observational associations were established in an exclusively male , elderly European cohort ( ULSAM ) with unknown generalizability to other age and ethnic groups . The different metabolomics platforms between studies and the expression of FA levels in % of plasma phospholipids rather than absolute levels in CHARGE somewhat limit methodological consistency . However , this diversity also supports the robustness of findings that were replicated on different platforms . Causal relationships were consistent across studies but require validation in physiologic models of IR , particularly as some sources of bias ( e . g . , canalization , unmeasured horizontal pleiotropy ) cannot be excluded . Whilst the unique contribution of our study is to assess causal effects on the plasma metabolome , we could not examine the reverse causality as no genetic instruments were available for the majority of metabolites . As illustrated above , relative levels of circulating POA and OA likely reflect endogenous biosynthesis , however , the exact contributions of SCD-1 , exogenous sources , catabolism and excretion could not be addressed by our study and require experimental investigation . We used proxy outcomes ( HOMA-IR and CIR ) with available large GWAS results in sensitivity analyses rather than the exact same measures as in the main analysis . Strengths include the extensive testing for violations of MR assumptions , the replication of main findings in large independent cohorts that used different methods of quantification , and the use gold standard measures for IR . We detected no evidence of pleiotropy of genetic instruments whose associations with cardiometabolic traits were in the expected directions and causal estimates agreed between different analysis methods . In summary , our study in multiple independent cohorts of community residents indicates a causal effect of IR on circulating OA , POA , and tyrosine levels and provides new insights into the metabolomic signature of IR and impaired insulin secretion . It is to our knowledge the first large-scale attempt to explore the causal effects of genetic IR on a non-selected set of plasma metabolites . The potential implications of the presumed IR-induced inhibition of monounsaturated FA biosynthesis on health outcomes require validation in experimental models but may form part of the explanation for the elevated CVD risk in IR that is independent of T2D development .
Detailed descriptions are available in S1 Text . In brief , ULSAM ( http://www2 . pubcare . uu . se/ULSAM/ ) was started in 1970 and enrolled 81 . 7% ( n = 2 , 322 ) of all male residents of Uppsala county , Sweden , born between 1920 and 1924 . On-going assessments every five to ten years include questionnaire , biochemical , and anthropometric examinations . The current study is based on the assessment at age 70 years , which included an OGTT and hyperinsulinemic-euglycemic clamp measurement . The PIVUS study ( http://www . medsci . uu . se/pivus/ ) enrolled 50% ( n = 1 , 016 ) of a random sample of Uppsala community residents aged 70 years in 2001 and features assessments including health questionnaires , blood sampling and clinical measurements every five years . The current study is based on the assessment at age 70 years . TwinGene ( http://ki . se/en/meb/twingene-and-genomeeutwin ) is a longitudinal study of 12 , 591 twins born before 1958 and registered in the Swedish Twin Registry with questionnaire assessments and blood sampling done between 1998 and 2002 , and again between 2004 and 2008 . The current study used a subcohort from a nested case-cohort design established for an earlier project [13] that was randomly selected within four age and sex strata to match a case group that included incident cases of T2D , coronary heart disease , ischemic stroke , and dementia ( up to 31 Dec 2010 ) ( S1 Text ) . In all three Swedish cohorts , prevalent cases of diabetes were excluded ( criteria in S1 Text ) . The Cooperative Health Research in the Region of Augsburg ( Kooperative Gesundheitsforschung in der Region Augsburg , KORA [46] ) study is a series of epidemiological surveys of the general population in Southern Germany that includes longitudinal health assessment and blood sample collection . The current study is based on the KORA F4 ( 2006–2008 ) survey of 32-to-77-year-old men and women ( n = 1 , 768 , 48 . 5% male , mean age 60 . 8 ± 8 . 8 years , mean BMI 28 . 2 ± 4 . 8 kg/m2 ) . TwinsUK is a predominantly female cohort of adult twins recruited from the general UK population . The current study is based on 17-to-85-year-old twins ( n = 6 , 056 , 7 . 1% male , mean age 53 . 4 ± 14 . 0 years , mean BMI 26 . 1 ± 4 . 9 kg/m2 ) who underwent blood metabolite profiling and health center assessment . The CHARGE consortium GWAS for plasma FA fractions [20] combined 8 , 961 mostly middle-aged to older persons of European ancestry ( 45 . 0% male , mean age 59 . 7 ± 7 . 6 years , mean BMI 27 . 0 ± 4 . 8 kg/m2 ) from five cohorts—the Atherosclerosis Risk in Communities ( ARIC ) study , the Cardiovascular Health Study ( CHS ) , the Coronary Artery Risk Development in Young Adults ( CARDIA ) study , the Invecchiare in Chianti ( InCHIANTI ) study , and the Multi-Ethnic Study of Atherosclerosis ( MESA ) . Details on cohorts and recruitment are reported elsewhere [20] and documented online ( http://chargeconsortium . com/ ) . The Finnish consortium [21] combined five cohorts of 8 , 330 individuals with serum nuclear magnetic resonance metabolomics results from the FINRISK 2007 Dietary , Lifestyle and Genetic determinants of Obesity and Metabolic syndrome ( FINRISK-07/DILGOM ) study , the Helsinki Birth Cohort Study ( HBCS ) , the Health2000 GenMets study , the Northern Finland Birth Cohort 1966 ( NFBC1966 ) study and the Cardiovascular Risk in Young Finns Study ( YF ) . Across all cohorts with 46 . 9% males , mean age was 37 . 8 ± 3 . 2 years and mean was BMI 25 . 4 ± 4 . 3 kg/m2 . All participants provided written informed consent prior to inclusion in the study and the research was approved by the Ethics Committees of Uppsala University ( ULSAM , PIVUS ) and Karolinska Institutet ( TwinGene ) , or the respective Institutional Review Boards for the other cohorts . The study was conducted according to the principles of the Declaration of Helsinki . Untargeted metabolomics profiling of venous blood samples in the three Swedish cohorts was carried out by ultra-performance liquid chromatography ( UPLC ) on a Waters Acquity UPLC system coupled to a Waters Xevo G2-Time-Of-Flight-Mass Spectrometry ( TOFMS ) platform at Colorado State University ( Fort Collins , CO , USA ) . Data acquisition in the positive electrospray ion mode with a mass-to-charge ratio ( m/z ) range of 50–1 , 200 at 5 Hz was alternately performed at collision energies of 6V and 15–30V . Details on sample handling and data processing by XCMS in R [47] are available in S1 Text and in [14] . Parameter selection for feature detection , alignment , grouping , and imputation was optimized in simulations of random sets of 20–40 samples . In total , 10 , 162 ( ULSAM ) , 9 , 755 ( TwinGene ) and 7 , 522 ( PIVUS ) features were detected . Adjustment for factors of unwanted variability ( plate effect , analysis date , retention time drift and sample collection ) by analysis of variance-type standardization was followed by log-transformation and removal of spectra with abnormal intensities and/or low inter-duplicate correlations and/or retention times <35 sec . For each feature , retention time , m/z , and fragmentation pattern were compared to in-house and public database reference libraries and matched according to Metabolomics Standard Initiative guidelines [48] . The current study is based on all 192 metabolites identified in ULSAM . Common features between ULSAM , PIVUS , and TwinGene were identified by matching m/z and retention time , followed by manual inspection of fragmentation spectra . Full metabolomics data are available in the MetaboLights archive ( study identifiers MTBLS90 for PIVUS , MTBLS124 for ULSAM , MTBLS93 for TwinGene; http://www . ebi . ac . uk/metabolights/ ) . Metabolomics analyses in KORA and TwinsUK were carried out by the commercial company Metabolon , Inc . ( Durham , NC , USA ) , which combined positive and negative ion-mode UPLC/tandem-MS with gas chromatography/MS . Following protein precipitation in methanol , samples for analyzed in duplicates and spectral annotation was performed against a standard compound library ( for details , see S1 Text and [19 , 46] ) . Following the removal of outlying ( >3 SD ) features and those with <300 non-missing values , 276 and 258 metabolites in KORA and TwinsUK , respectively , were quantified and identified based on matching to in-house library standards . In CHARGE , a targeted gas chromatography approach was used to quantify plasma phospholipid composition ( except in the InCHIANTI cohort , where total plasma FA were measured ) . As detailed in [20] , fasting plasma phospholipid isolation by thin-layer chromatography was followed by quantification of FA by targeted gas chromatography . The Finnish study processed serum samples from all five sub-cohorts in one central laboratory by three complementary 1H-nuclear magnetic resonance analysis windows optimized for lipoproteins , low molecular weight metabolites and lipid species , respectively [21 , 49] . For quantitative analysis , raw spectral data were pre-processed with baseline zeroing , peak alignment and correction for albumin background and validated against high-performance LC data . We used IV analysis to estimate the causal effect of IR/impaired insulin secretion on metabolite levels in PIVUS and TwinGene . The Wald ratio estimator [50] was obtained as the ratio between the regression coefficients for the effect of the genetic IV on metabolite levels divided by the effect of the genetic IV on IR/insulin secretion ( Eq 1 ) . Standard errors were estimated by the delta method , which we previously validated ( Eq 2 ) [51] . As IV , we used the IR and impaired insulin secretion genetic risk scores validated by Scott et al . [18] ( S2 Table ) . Because the study included ULSAM ( alongside the MRC-Ely , RISC , Fenland , and EPIC-Interact cohorts ) to estimate the association between instrument and risk factor , we excluded the ULSAM cohort from MR analysis . We used proxy SNPs ( linkage disequilibium r2 >0 . 8 ) for variants not directly genotyped . When no proxies were available , SNP scores were imputed as 2*risk allele frequency based on the 1000 Genomes Project phase 1 [52] ( for the insulin secretion score this applied to rs1800574 in all three cohorts , as well as rs7957197 and rs10811661 in TwinGene ) ( S2 Table ) . Quality control included mean-imputation of SNP values for individuals with one missing value and exclusion of individuals with >1 missing values . All SNPs had call rates >95% . Additive non-weighted genetic risk scores were calculated in PLINK1 . 07 ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) . The association between genetic IVs and metabolites was estimated in linear models adjusted for age , sex , the first three genetic principal components and cohort with metabolite levels ( SD-unit ) as outcome . Age- and sex-adjusted associations between genetic IVs and exposure ( SD-unit ) were obtained from Scott et al . [18] . For sensitivity analysis , SNP associations with IR and insulin secretion were obtained from summary GWAS results in MAGIC ( see above ) . MR Egger regression and other sensitivity analyses were carried out in R according to the scripts provided in the data supplement by [23] and in appendix A . 3 by [24] , respectively . We obtained the publicly available GWAS data for serum/plasma metabolite levels in 7 , 824 European adults in the KORA F4 and TwinsUK cohorts [19] , as well as for OA and POA fractions of total plasma phospholipids in 8 , 961 Europeans in CHARGE http://chargeconsortium . com/ ) . We also obtained the summary meta-GWAS statistics for serum tyrosine levels from a Finnish consortium study [21] . Details on genotyping and statistical analyses are available in S1 Text and elsewhere [19 , 46] . We extracted β coefficients and standard errors for SNPs in the non-weighted IR/insulin secretion genetic scores and computed summary effect sizes with the grs . summary ( ) function in the gtx package in R [53] . Regression coefficients expressed SD-unit change in metabolite levels . Causal effects were estimated by MR analysis as described above for all metabolites available in KORA/TwinsUK that passed p < 0 . 1 at the discovery stage , for OA and POA in CHARGE , and for tyrosine in the Finnish consortium . Causal estimate from all cohorts were combined in inverse variance-weighted , fixed effects meta-analysis via metafor in R . R scripts for the full metabolomics pipeline in PIVUS , TwinGene and ULSAM are available online ( https://github . com/andgan/metabolomics_pipeline ) , R scripts used for observational and MR analysis are available as well ( https://github . com/chrnowak/metabolomics ) . | Impaired glucose homeostasis leads to diabetes and cardiovascular disease and has two main components: failure to secrete enough insulin from pancreatic β-cells and reduced insulin-stimulated cellular uptake of glucose and other nutrients in target tissues ( insulin resistance , IR ) . We used metabolomics analysis in non-diabetic persons to measure a non-selective range of small molecules including amino acids , lipids , and sugars . Pathway analysis highlighted distinct metabolic pathways linked to IR ( e . g . bile acid production ) and impaired insulin secretion ( fatty acid biosynthesis ) , but causal directions remained unclear . Mendelian Randomization ( MR ) analysis can test for causal effects in observational studies in the absence of randomized controlled trials . Using MR analysis in up to four large independent studies , we found evidence that IR causes a decrease in levels of the main endogenous monounsaturated fatty acids palmitoleic acid and oleic acid , as well as suggestive evidence for higher levels of the amino acid tyrosine . We provide a possible explanation for parts of the diabetes-independent risk of cardiovascular disease in persons with IR . | [
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"drug... | 2016 | Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study |
In patients with cerebral malaria ( CM ) , higher levels of cell-specific microparticles ( MP ) correlate with the presence of neurological symptoms . MP are submicron plasma membrane-derived vesicles that express antigens of their cell of origin and phosphatidylserine ( PS ) on their surface , facilitating their role in coagulation , inflammation and cell adhesion . In this study , the in vivo production , fate and pathogenicity of cell-specific MP during Plasmodium berghei infection of mice were evaluated . Using annexin V , a PS ligand , and flow cytometry , analysis of platelet-free plasma from infected mice with cerebral involvement showed a peak of MP levels at the time of the neurological onset . Phenotypic analyses showed that MP from infected mice were predominantly of platelet , endothelial and erythrocytic origins . To determine the in vivo fate of MP , we adoptively transferred fluorescently labelled MP from mice with CM into healthy or infected recipient mice . MP were quickly cleared following intravenous injection , but microscopic examination revealed arrested MP lining the endothelium of brain vessels of infected , but not healthy , recipient mice . To determine the pathogenicity of MP , we transferred MP from activated endothelial cells into healthy recipient mice and this induced CM-like brain and lung pathology . This study supports a pathogenic role for MP in the aggravation of the neurological lesion and suggests a causal relationship between MP and the development of CM .
Cell activation by various agonists and apoptosis trigger the vesiculation of microparticles ( MP ) from all cell types [1] , [2] , [3] . During vesiculation , phospholipids are reorganised through the translocation of inward and outward membrane lipids , whereby phosphatidylserine ( PS ) is exposed on the outer leaflet of the membrane [4] , [5] . The budding progeny are small ( 0 . 2–1 µm ) plasma membrane-derived vesicles that express antigens of their cell of origin and PS on their surface , facilitating their role in coagulation , inflammation and cell adhesion [6] , [7] . Once described as inert biological bystanders MP have now emerged as novel therapeutic targets in the treatment of diseases [8] , [9] , [10] . Under normal physiological conditions , baseline levels of circulating MP can be detected in the blood and are thought to be involved in maintaining cellular homeostasis . However , elevated levels of MP have been implicated in several diseases [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , including cerebral malaria ( CM ) , in patients as well as in experimental models [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] . CM is a multisystem multi-organ dysfunction that develops as a syndrome following Plasmodium falciparum infection [27] . It is characterised by the presence of sustained impaired consciousness and those surviving may develop residual neurological sequelae [28] . Despite better campaigns targeted at the eradication of malaria , the global burden persists [29] , [30] . The underlying pathogenesis that drives the manifestation of CM remains incompletely understood . What is known is that the pathogenesis is multifactorial , involving the dynamic interaction between cellular sequestration , a dysregulated inflammatory response , MP production and homeostasis disruption [24] , [31] , [32] . Little is understood about the role of MP in CM pathogenesis , although markedly high plasma levels of circulating platelet , erythrocytic , leucocytic and in particular endothelial cell-derived MP ( PMP , EryMP , LMP , EMP respectively ) have been detected in patients with CM [23] , [26] , [33] . In murine experimental CM ( eCM ) , the overproduction of MP is also observed , and ablation of MP vesiculation via knock-down of the ATP-binding cassette transporter A1 ( ABCA1 ) involved in the distribution of PS , confers protection against the neurological syndrome without interfering with the infection itself [21] . Pharmacological inhibition of MP production by pantethine also confers protection from CM [8] . The above findings indicate that MP may have an active role in the development of the CM lesion and are not merely an epiphenomenon , although the precise mechanisms of action of these MP during CM have not been completely deciphered [20] . Using murine experimental models of CM [34] , [35] and non-cerebral malaria ( NCM ) [36] , [37] we characterised the production of MP over the course of Plasmodium infection in CM-susceptible mice , and compared their cellular origins . We adoptively transferred MP , isolated from mouse blood obtained at the time of the neurological syndrome , into the circulation of recipient mice and followed their blood clearance . Our study dissects in which tissues these MP localise to possibly exert their effects , as little is known about their fate following their initial release . Since the endothelium is an active component of the CM lesion , and EMP have been found to be elevated in human CM ( hCM ) [23] , [38] , we transferred in vitro generated EMP and studied their induction of pathology and clearance kinetics in healthy and infected mice . This study shows MP localised at the neurovascular lesion in vivo and MP transfer elicited CM-like histopathology in the brain and lung of healthy recipients , supporting a role for MP in CM pathogenesis .
This study investigates the levels and cellular origin of MP produced during murine malaria and for the first time describes the clearance and fate of CM+ MP in vivo as well as the pathogenicity of EMP . Using CM-susceptible mice , we showed that P . berghei infection induces a rise in total circulating MP , although distinct differences exist in the cellular origin and production profiles of MP in mice developing CM versus NCM . We found that MP from CM+ , but not healthy donors , adoptively transferred in vivo can be detected at the site of the lesion , sequestered amongst other cells within the cerebral vessels of CM+ recipient mice . We also showed that transferred EMP , induce CM-like pathology in the brain and lung of recipient mice , supporting a role for MP in the exacerbation of CM . Ablating the increase of MP numbers either genetically [21] or pharmacologically [8] confers protection against murine CM . Elevated levels of plasma EryMP [33] and EMP [23] , [38] in malaria patients correlate with severity and are particularly restricted to those with cerebral involvement . This also has been shown in studies on intracerebral haemorrhage , whereby higher MP levels correlate with coma and poor clinical outcome in patients [39][40] . Although suggestive of a role in the pathogenesis of CM , the existing data on MP in CM does not completely support a role for MP in the worsening of CM nor do they substantiate it solely as a predictive marker for CM . Elevated levels of circulating Annexin V+ MP were detected in the plasma of CM+ mice ( i . e . infected with PbA or PbK1/2 ) at the time of neurological development . This finding confirms what has already been observed upon CM onset in experimental and hCM [21] , [38] . Our study extends from this and follows the production of MP over the course of infection , not just at CM onset . Interestingly , a biphasic production of MP was detected in the plasma of PbA infected mice , peaking during the early stages of infection and also at CM onset . These waves of MP coincide with the perpetuated cycle of endothelial activation , the production of cytokines and chemokines , the upregulation of adhesion molecules and the binding of vascular cells to microvessels [41] . Although the first wave of MP was absent in PbK1/2 infection , mice with high MP levels during the neurological phase did develop CM . MP overproduction was absent in mice that did not develop cerebral signs during the time of CM onset in PbA mice ( i . e . PbK infected or 30% of the PbK1/2 infected ) . This suggests that MP may play a role in the development of the neurological syndrome . At the time of CM onset , cell-specific MP numbers were higher in PbA infected mice than in PbK- and PbK1/2 infected animals or healthy controls . The sum of positive MP for single staining of cell markers gave a closer approximation of total circulating MP , as Annexin V staining indicates only PS-positive MP . MP can be PS negative or have low undetectable PS on the surface and remain unbound to Annexin V [20] , [42] . We found that plasma PMP , EMP , EryMP and MMP were most elevated during the neurological phase in PbA infected mice . Previous studies have shown a dramatic increase in EMP , EryMP and PMP in patients presenting with CM [23] , [33] , [38] . In CM , the neurovascular lesion is comprised of sequestered vascular cells such as platelets , erythrocytes and leucocytes within the endothelial lining of microvessels [32] , [43] , [44] . It is not surprising that the sequestered cells also produce the most predominant MP populations detected . The development of CM is attributed to the cascade of events preceding the sequestration of cells and , consequently , the mechanical occlusion [31] , [43] , [44] , [45] . The 70% of PbK1/2 infected mice that developed CM displayed comparable levels of total Annexin V+ MP levels to PbA infected mice , although their origins were predominately from platelets and erythrocytes . No significant differences were detected between the levels or proportions of cell-specific MP detected in PbK infected and healthy mice between days 6 and 14 . Our data show that during the acute stage of PbA-infection , a peak of PMP can be detected , and this is absent in the PbK- and PbK1/2-infections . This finding is consistent with the substantial loss of platelets as MP in the acute phase of the PbA-infection [46] and may correlate with the depth and duration of coma [38] . Thrombocytopenia is associated with poor prognosis in both human and experimental CM [46] , [47] and higher platelet accumulation has been observed in cerebral microvessels in both human [48] and eCM [49] , [50] . In vitro , platelets enhance the binding of infected erythrocytes ( IE ) to the cerebral endothelium [51] and their MP progeny are able to adhere preferentially to IE and also to the cerebral endothelium [19] , [52] . PMP pathogenic potential is attributed to their mobility and their access via blood flow to other vascular cells [53] . Although this finding supports the platelet adhesion hypothesis , the absence of this first wave of PMP in PbK1/2 infection is interesting . Nothing is known about thrombocytopenia or PMP in PbK1/2 infection . The majority of mice develop CM+ despite the absence of the first wave of PMP; nevertheless , at CM onset , higher titres of PMP are detected . EryMP were elevated in CM+ mice , both in PbA- and PbK1/2 infected mice , at CM onset . This finding is supported by human studies , whereby EryMP numbers are increased in patients with P . falciparum infections , even after antimalarial drug treatment [33] and also in patients with severe malarial anaemia [38] . In contrast , EryMP numbers are lower in patients with P . vivax and P . malariae malaria , similar to what is observed with the PbK infected mice in our study [33] . Interestingly , the PbK infected group , with no evidence of cerebral complications , had an overproduction of MP from erythrocytes and leucocytes during the later stage of infection . The evolving hyperparasitaemia in these mice leads to the destruction of IE and the gradual rupture and fragmentation of fragile erythrocytes increases the level of EryMP and cellular debris [33] . Higher levels of LMP could account in that case for the activation of the cells involved in the destruction of erythrocytes and their removal from the circulation . Adoptively transferred CM+ MP are detectable in recipient mice but quickly subsided , indicating clearance from circulation . Some of these MP were found to be arrested in cerebral microvessels of CM+ recipients and also in the spleen , kidney and , to a lesser extent , the lung and liver . Recent studies in humans have shown that parasites induce the loss of endothelial protein C receptors in the cerebral microvessels , leaving them vulnerable to enhanced local thrombin generation and coagulopathy [54] . We know from previous murine studies that MP are procoagulant and proinflammatory [21] , [43] , [55] , thus it is not surprising that MP are found lodged in the cerebral microvessels of infected recipients . No MP were detected in the heart of recipients . MP from both healthy and CM+ donors were also detected within the spleen of recipient hosts , suggesting that this organ could be a site of MP trapping and clearance from the circulation independent of the infection . It is possible that the spleen filters the PS+ MP in a similar way to PS+ cells in malaria [56] and Kupffer cells in the liver could remove EryMP as shown by Willenkens et al . , [57] , although further studies are required to elucidate this in our system . Little is known on the mechanisms underlying the clearance of plasma MP from circulation in vivo . In our study , the disappearance of PKH67-labelled CM+ MP occurred within 5 minutes . In a rabbit model , no PMP were detected in the circulation at 10 , 30 or 60 minutes post injection [58] . In contrast , transferred PMP , isolated from platelet concentrates from the peripheral blood of single donor patients , circulated for markedly longer with a half-life of 5 . 8 hours ( Annexin V+ ) and 5 . 3 hours ( CD61+ ) [59] . The authors attribute the elevated levels and the longer half-life of circulating MP to the infused platelets producing more MP in circulation [59] . Another possible explanation is that MP may clear the circulation and reach their target sites quicker in rabbit models , and also in mice , due to their faster heart rate [58] . The authors also suggest that the half-life is an overestimation , and the observations in the rabbits miss the initial rapid clearance of MP [58] . Furthermore , in vitro assays support the idea that MP can be detected in blood when there is no mechanism to remove them [58] . In pathological states , the continuous production of MP overrides the mechanisms of rapid clearance , hence elevated levels can be detected . All cells are able to produce and release MP , although the emerging progeny of MP are heterogeneous and do not share the same properties . EMP represent the most abundant MP detected in pathologies that arise due to vascular injury or endothelial dysfunction although not all their roles are noxious [60] , [61] . EMP numbers are elevated in patients with conditions in which the endothelium is injured and/or the endothelial barrier is compromised , including sickle cell disease , Alzheimer's disease , metabolic syndrome , hypertension , atherosclerosis and chronic obstructive pulmonary disease [62] , [63] , [64] , [65] , [66] , [67] . EMP were first described in vitro [6] and high numbers were detected in CM patients presenting with coma [23] . Lower numbers of EMP and concentrations of TNF were detected in mice protected against CM during PbA infection [21] . Studies in vitro and in the murine model of CM indicated that EMP have similar procoagulant and pro-inflammatory potentials to , and express the same repertoire of antigens as , their corresponding mother EC [6] , [21] . In CM , the endothelium is both a target and an effector in disease pathogenesis [24] . The direct role of EMP in inducing brain and lung pathology in murine models of CM has not been described . We addressed the hypothesis that EMP may be pathogenic in the CM lesion by transferring TNF-generated EMP into healthy and infected mice . Our findings demonstrate that transferred TNF-EMP can induce histopathological signs that are compatible with endothelial leakage leading to cerebral and pulmonary oedema and haemorrhage in healthy mice [68] . Control inert microsphere beads did not induce any change and remained in circulation for 60 minutes , compared to EMP that were cleared within the first few minutes post transfer , supporting the idea that the clearance of the MP is a physiological phenomenon that is mediated by receptors present at the surface of MP . Exactly how EMP alter endothelial integrity in CM is unclear . Flow cytometry revealed that our EMP express endoglin , CD54 and CD106 . Soluble endoglin ( sCD105 ) overexpression is linked to typical systemic and vascular inflammation states such as pre-eclampsia and HELLP syndrome [69] . It is possible that endoglin-bound MP may have a role in inducing vascular injury . Endoglin is an RGD membrane protein acting as transforming growth factor-β accessory receptor and has been implicated in leucocyte recruitment and extravasation [70] and more recently in septic shock-induced disseminated intravascular coagulopathy [71] . In CM , CD54 and CD106 are established markers of EC injury and enable tethering and sequestration of cells to the endothelium [31] , [43] , [72] . TNF increased the expression of CD54 and CD106 on the EMP , consistent with other studies [25] , suggesting a possible mechanism by which MP interact with the endothelium to induce injury . Transferred EMP induced atelectasis in lungs from healthy recipient mice and increased alveolar cellularity , resembling the pathology seen in CM+ lung . EMP were shown to induce endothelial dysfunction , promote vasodilation , pulmonary oedema and acute lung injury in pathophysiological concentrations [73] . EMP sequester in lung tissue and elicit an immune response in vivo by increasing cytokine production leading to neutrophil recruitment [73] , [74] . It is plausible that EMP initiate a cascade of events , beginning with the production of cytokines , that prime the endothelium thereby ultimately impairing vessel functions and resulting in tissue damage . Taken together , our findings offer new evidence for a causal relationship between MP and the pathogenesis of CM . To our knowledge , this is the first time that MP have been localised at the neurovascular lesion in vivo and that their transfer elicited histopathology in the brain and lung of healthy recipients . We confirm that elevated levels of total MP are present at CM onset , predominantly from activated host cells that are known to participate in CM pathogenesis . Specifically , in CM , the early peak of total MP and PMP differentiated the kinetic production profile from NCM and could potentially be an indicator of prognosis . We showed that MP are rapidly cleared from circulation , and that some remain sequestered in organs , such as the brain and spleen . The EMP in this study carry VCAM-1 and ICAM-1 on their surface , which are upregulated following cytokine stimulation , potentially mediating their role in the worsening of CM . During CM , activation of cells by parasite moieties , toxins , cytokines and/or cell death , delivers MP into the circulation . We propose that the interactions between MP , endothelium , circulating host vascular cells and their released circulating soluble factors influence the course of infection leading to the development of CM . Since the first human studies on MP in CM [23] , [38] , there has been growing interest in exploring the potential of MP as biomarkers for both diagnosis and follow-up and therapeutic targets since they represent both a consequence of , and contributor to , CM . The plasma membrane acts as the primary sensor to its external environment , thus , discriminating the cellular origin of MP may indicate what tissues or cells are undergoing activation or damage . Besides detection of malarial retinopathy , which is yet to be included as standard assessment for severe malaria , a clinical challenge still exists to distinguish CM from other encephalopathies [75] . Diagnosis relies on P . falciparum parasitaemia and impaired consciousness with the exclusion of other potential causes of severe disease , which in malaria-endemic areas is difficult to achieve due to the prevalence of asymptomatic parasitaemia and the lack of high-level diagnostic testing . Misdiagnosis is common and there is a need for reliable CM specific diagnostic and prognostic biomarkers [76] . This would support the promise of MP as clinical probes for CM and help provide targeted care of malaria patients at imminent risk of organ damage or cerebral complications as indicated by their detected MP [11] . In addition , molecules such as Pantethine that inhibit MP release have conferred protection in vivo and may be suitable candidates for an adjunct neuroprotective treatment of CM [8] . If the delay of CM onset in vivo is observed in human patients , this could potentially increase the therapeutic window available for treatment decreasing CM-associated mortality and neurological sequelae . In combination with anti-parasite chemotherapy , molecules that stabilize plasma membranes and reduce overproduction of deleterious MP and shedding may be protective or minimise the cerebral complications associated with CM .
Data were analysed using GraphPad Prism version 5 . 00 for Windows , GraphPad Software , San Diego California USA . Survival curves were analysed using the Log-rank ( Mantel-Cox ) Test and the Gehan-Breslow-Wilcoxon Test . To compare several groups , we used non-parametric analysis of variance ( ANOVA , Kruskall-Wallis ) with a Dunn's post-test . To compare mean total and cell-specific MP levels between two groups the Wilcoxon test was used; *p<0 . 05 , **p<0 . 001 , ***p<0 . 0001 . | Cerebral malaria ( CM ) is a potentially fatal neurological syndrome characterised by unrousable coma . Since the detection of high levels of plasma microparticles ( MP ) in patients with CM , it has been demonstrated that inhibition of MP production confers protection from murine CM . However , the precise mechanisms of action of these MP during CM have not been completely deciphered . In this study , we used experimental models of CM to measure the production and origins of MP over the course of infection . We found low baseline circulating MP in healthy mice and these were subsequently raised at the time of the neurological syndrome . Phenotypic analyses showed that circulating MP were predominantly from activated host cells that have previously been established to participate in CM pathogenesis . We show for the first time transferred MP impairing endothelial integrity and inducing CM-like pathology in the brain and lung of healthy animals . Our study dissects what tissues these MP localise to exert their effects , as little is known about their fate following the initial release . These data suggest a causal relationship between MP and the development of CM and also warrant further investigation into the representation of MP as a marker of CM risk . | [
"Abstract",
"Introduction",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases"
] | 2014 | Production, Fate and Pathogenicity of Plasma Microparticles in Murine Cerebral Malaria |
Repetitive DNA sequences within eukaryotic heterochromatin are poorly transcribed and replicate late in S-phase . In Saccharomyces cerevisiae , the histone deacetylase Sir2 is required for both transcriptional silencing and late replication at the repetitive ribosomal DNA arrays ( rDNA ) . Despite the widespread association between transcription and replication timing , it remains unclear how transcription might impinge on replication , or vice versa . Here we show that , when silencing of an RNA polymerase II ( RNA Pol II ) -transcribed non-coding RNA at the rDNA is disrupted by SIR2 deletion , RNA polymerase pushes and thereby relocalizes replicative Mcm2-7 helicases away from their loading sites to an adjacent region with low nucleosome occupancy , and this relocalization is associated with increased rDNA origin efficiency . Our results suggest a model in which two of the major defining features of heterochromatin , transcriptional silencing and late replication , are mechanistically linked through suppression of polymerase-mediated displacement of replication initiation complexes .
Approximately half of the human genome consists of repetitive DNA sequences organized as heterochromatin . These regions are largely devoid of genes and are characterized by both low levels of transcription and late DNA replication [1–4] . The association between low levels of transcription and late replication is well established and extends to regions of the genome that are transcriptionally active only during specific stages of development . In stages of development when genes within these regions are transcribed , they replicate early , and when these genes are no longer expressed , their replication is delayed [5 , 6] . In contrast , so-called "housekeeping" genes , which are constitutively transcribed , replicate early during all stages of development . Replication timing has important evolutionary implications for genome stability , with late replicating regions being more prone to mutation and rearrangement [7 , 8] . Despite the prevalence and evolutionary significance of the association between transcription and replication timing , its mechanistic underpinnings remain elusive . It has been proposed that differences in histone modifications or nuclear localization between heavily transcribed and silenced genome regions may affect their replication timing , but it has proved difficult to establish mechanistic foundation for these associations [9 , 10] . In S . cerevisiae , repetitive regions within the rDNA locus and at telomeres are subject to regional , gene-independent transcriptional silencing and are considered simple models of the heterochromatin found in higher eukaryotes ( reviewed in [11 , 12] ) . In a further parallel to metazoan heterochromatin , both of these repetitive regions replicate late in the cell cycle [13–15] . The NAD-dependent histone deacetylase Sir2 is required for heterochromatin formation at both sites , but with different partners at the two locations , forming RENT ( REgulator of Nucleolar silencing and Telophase exit ) and SIR ( Silent Information Regulator ) complexes at the rDNA and telomeres , respectively [16 , 17] . The rDNA locus in yeast consists of 150 tandemly arranged 9 . 1 kb repeats that occupy half of chromosome XII and comprise 10% of total genomic DNA . Each repeat contains ribosomal rRNA genes for the Pol I-transcribed 35S precursor RNA and for the Pol III-transcribed 5S RNA , separated by two intergenic spacer regions ( IGS1 and IGS2 ) ( Fig 1A ) . The spacer regions are subject to Sir2-dependent silencing and harbor two Pol II promoters , c-pro and e-pro , that drive transcription of non-coding RNAs . Two hundred base pairs downstream from c-pro , within IGS2 , is the ribosomal Autonomously Replicating Sequence ( "rARS" ) . The rARS serves as a replication origin , whose activation is also suppressed by Sir2 . Thus , SIR2 imposes both transcriptional silencing and replication origin repression at the rDNA . This feature , together with the homogeneous nature of the repeats , make the yeast rDNA a highly suitable model for examining the relationship between transcriptional silencing and replication in heterochromatin . The rARS serves as a binding site for the origin recognition complex ( ORC ) , which assists with loading the Mcm2-7 complex ( Mcm2-7 ) at rDNA origins . Because the rARS is located immediately downstream of the initiation site for a Sir2-regulated ncRNA , we reasoned that the transcription machinery may alter the deposition of replication initiation factors at the rDNA origins , which is referred to as origin licensing . Specifically , we hypothesized that RNA Pol II passage through the rDNA origin might either promote loading of Mcm2-7 replicative helicases or , in light of a previous reports [18 , 19] , induce their sliding along DNA and re-localization away from their loading site . Further supporting the possibility that Sir2 affects origin licensing is our observation that excessive origin activation in sir2 mutants can be suppressed by a point mutation in the origin recognition complex ( Orc ) -binding site within the rARS [13 , 20] . Here we tested the idea that transcription alters deposition of the pre-replicative complex ( pre-RC ) at rDNA origins by using sequencing-based methods to obtain and compare high-resolution footprints of nucleosomes and replication initiation factors at the rDNA origins in wild type ( WT ) and sir2 mutant cells . This analysis revealed that disruption of transcriptional silencing upon SIR2 deletion leads to RNA Pol II-mediated displacement of pre-RCs away from their loading site at the rDNA origins , which effectively repositions them from an area with high nucleosome occupancy to one with low . While our studies do not prove causality in the association between pre-RC repositioning and advanced replication timing , given that the overall abundance of pre-RCs at the rDNA is reduced in sir2 cells , we propose a model in which repositioning of pre-RCs to regions with low nucleosome occupancy in sir2 cells facilitates their subsequent activation .
To determine whether the absence of SIR2-mediated transcriptional repression alters chromatin architecture at the rARS , we sought to profile the chromatin organization of this locus at nucleotide resolution both before and after pre-RC assembly at the rARS . Chromatin perturbations dependent on pre-RC assembly should be ( 1 ) G1-specific; ( 2 ) CDC6- and ORC1-dependent; and thus reflective of binding of the Mcm2-7 helicase complex . We first used a micrococcal nuclease ( MNase ) -based high-resolution epigenome profiling technique called MNase-Seq ( Fig 1B ) to assess the chromatin architecture in a factor-agnostic manner . This technique reveals DNA footprints protected by the histone octamer ( nucleosomes ) as well as smaller footprints protected by DNA-binding factors such as transcription and replication factors [21–23] . Briefly , total chromatin is digested with MNase and the resulting fragments are subjected to paired-end sequencing using a protocol that retains DNA fragments as small as 50 base pairs . The length of each fragment is plotted as a function of its chromosomal position; thus , the size of the fragment is indicative of the protein 'footprint' ( Fig 1D ) . For example , DNA fragments protected by a histone octamer will be approximately ~150 bp and smaller fragments ( 50–100 bp ) represent footprints of site-specific DNA-binding factors ( Fig 1D ) . To gain insight into chromatin organization at the rDNA origins before the pre-RC is formed , we carried out MNase-seq on chromatin from cells arrested in the G2 phase of the cell cycle and plotted the reads that map to the 1 . 25 kb of rDNA sequence marked by a dashed line in Fig 1A that includes the replication origin . From left to right , we first observed a 50–100 bp footprint corresponding to the transcription factor Reb1 ( gray rectangle in Fig 2A and S1 Fig ) , followed by the footprints of six nucleosomes in the 150–200 size range; the first three are particularly well positioned , and the last maps to the 5S RNA sequence . The first nucleosome , which coincides with the initiation site of the Sir2-repressed c-pro transcript exhibited highest occupancy , followed by the third one , whereas the occupancy of the last three nucleosomes averaged only 10% of the first one . Overall , these results underscore the highly organized nature of chromatin at the rDNA origins of replication , which is remarkably uniform considering that the footprints are a composite of many cells with a multitude of rDNA repeats . We next used the same assay to analyze chromatin from cells arrested in G1 , when the pre-RC forms . We observed a striking 50–100 bp footprint adjacent to the rARS ( dashed rectangle in Fig 2A , right panel ) suggestive of the pre-RC . To determine whether the formation of this G1-specific footprint is ORC-dependent , we analyzed a previously published MNase-seq data set for the orc1-161 temperature-sensitive mutant [23] . We observed a loss of the footprint in the orc1-161 mutant at the restrictive temperature ( Fig 2B ) , as expected if the footprint constitutes the pre-RC . Because loading of the pre-RC requires Cdc6 [24] , we used the temperature-sensitive allele cdc6-1 [25] to determine whether the G1-specific signal we saw was Cdc6-dependent . Consistent with it reflecting pre-RC binding , the footprint was present in G1 at the permissive temperature of 23° but not at the restrictive temperature of 37° ( dashed rectangle in Fig 2C ) . The G1-specificity and ORC- and Cdc6-dependence of this footprint , coupled with its localization adjacent to the rARS demonstrate that this footprint reflects pre-RC formation at the rDNA origins , and that MNase-seq constitutes an accurate and robust assay for examining chromatin changes associated with pre-RC formation at this location . Our MNase-seq results reveal binding dynamics of both the pre-RC and nucleosomes averaged across many cells and many rDNA repeats in each cell . Replication origins normally occur in "nucleosome-free regions" ( NFRs ) but , to our surprise , this was not the case at the rARS: Instead , our data showed a nucleosome precisely at the site of pre-RC formation ( marked by dashed lines in Fig 3A ) [23 , 26 , 27] . Furthermore , the pre-RC and this nucleosome compete for binding: In G1 the pre-RC signal is prominent while the nucleosome signal is faint , whereas the converse is true in G2 ( Fig 3A ) . The coupling of the eviction of the nucleosomes with loading of the pre-RC in G1 is evident by changes in fragment size distribution at the sites of pre-RC loading ( Fig 3B ) , with the size shifting from that corresponding to nucleosomes in G2 , to the smaller size corresponding to pre-RC in G1 . Finally , the two nucleosomes that flank the rARS do not appear to shift away from the origin to make space for binding of the pre-RC in G1 . These results demonstrate that binding of the pre-RC and binding of the nucleosome at the rARS are mutually exclusive events , and that licensing of rDNA origins requires , or causes , eviction of this nucleosome . We next used MNase-seq to determine whether SIR2 deletion increases pre-RC binding or changes the distribution of pre-RC and nucleosome occupancy at the rDNA . When we compared pre-RC footprints in WT and sir2 cells arrested in G1 , rather than seeing increased pre-RC binding and decreased nucleosome occupancy at the rARS in sir2 compared to WT , as might be expected from the observation that SIR2 deletion promotes rDNA replication , we instead saw the opposite: SIR2 deletion led to decreased pre-RC binding ( dashed rectangle in Fig 4A ) and increased nucleosome occupancy ( Fig 3A and 3C ) . Notably , however , the area to the right of the rARS , which is devoid of pre-RC binding in the WT , showed obvious binding in the 50–100 bp DNA fragment size range in the sir2 mutant ( Fig 4A ) . These 50–100 bp footprints in sir2 cells ( dashed rectangle in Fig 4A ) , including both those on the left and on the right of rARS , were G1-specific , consistent with pre-RC binding pattern we have previously observed in WT cells . These results suggest that the pre-RC might be displaced rightward in sir2 mutant , perhaps thereby allowing a nucleosome to occupy the vacated space at the rARS . The Mcm2-7 helicase is a major component of the pre-RC . To determine whether the pre-RC footprint we observed by MNase-seq contains Mcm2-7 , and to confirm its redistribution in the sir2 mutant , we tagged the endogenous copy of Mcm2 at its C-terminus with MNase and carried out the "Chromatin Endogenous Cleavage" assay ( ChEC ) ( Fig 1C ) [28 , 29] . Mcm2 function was not altered by the presence of the tag as judged by comparable growth of WT and Mcm2-tagged strain ( S2 Fig ) . In ChEC , a short burst of calcium-activated MNase activity induces in situ cleavage of DNA adjacent to the fusion protein . We prepared sequencing libraries from the cleaved DNA , again using a protocol that retains small DNA fragments and presented the sequencing data in two dimensions as for the MNase-seq results ( Fig 4B ) . The footprint we obtained coincided with the G1-specific , CDC6- and ORC1- dependent footprint observed by MNase-seq ( Fig 4B and 4C ) . Furthermore , we observed that the Mcm2-7 footprints in sir2 cells can be also found right of the rARS , whereas they are confined left of the rARS in the WT cells ( Fig 4B ) . The Mcm2-7 footprints to the right of the rARS in the sir2 mutant were not suppressed by deletion of FOB1 ( S4 Fig ) , indicating that they do not originate from extrachromosomal rDNA circles that accumulate in a sir2 mutant in a FOB1-dependent manner [30] . These results suggested that the bound Mcm2-7 , assuming it is loaded by ORC at the same site in both WT and sir2 , is pushed rightward in the sir2 mutant . This movement effectively repositions Mcm2-7 from an area of high nucleosome occupancy to one with low . We next quantified abundance of Mcm2-7 in WT and sir2 mutant using both our MNase-seq and ChEC-seq datasets . In the MNase-seq dataset we integrated read depths in the 50–100 base fragment size range in the region that encompasses both displaced and non-displaced Mcm2-7 , and normalized it to read depths corresponding the footprint of the transcription factor Reb1 ( gray rectangle in Fig 4A ) . The sir2 mutant exhibited a 37% reduction in Mcm2-7 binding relative to WT ( Fig 4D ) . To calculate Mcm2-7 abundance using ChEC-seq , we compared the fraction of footprints that map to rDNA , among all Mcm2-7 footprints , in WT and sir2 cells . We observed a 47% reduction in Mcm2-7 binding in this region in sir2 vs WT in our Mcm2-MNase ChEC dataset , which is similar to what we have observed using MNase-seq dataset . We conclude that the overall abundance Mcm2-7 complexes at the rDNA is markedly reduced in sir2 mutants . A combination of the findings that Mcm2-7 complexes are repositioned in a sir2 mutant to an area with low nucleosome occupancy and that overall Mcm2-7 binding at the rDNA is lower in a sir2 mutant compared to WT suggests that accelerated replication of the rDNA in the absence of SIR2 is a consequence of pre-RC repositioning rather than increased origin licensing . To assess whether the increased rDNA replication in a sir2 mutant still originates at approximately the same location , despite the decreased Mcm2-7 footprint , we used two-dimensional gel electrophoresis ( 2D gel ) to compare replication intermediates at the rDNA origins in WT and sir2 mutant cells at several time points after release from G1 arrest , with an early genomic origin ( ARS305 ) serving as a control . 2D-gels allow assessment of the activity of specific origins because they can distinguish "bubble- shaped " replication intermediates that are formed when an origin is active from "Y-shaped" intermediates , which reflect passive replication [31] . We observed bubble signals at the rDNA in both WT and sir2 cells , and their timing and intensity differed as expected [15 , 32]: In WT cells , the bubble signal at the rDNA appeared after the control origin had fired , whereas the reverse was true in the sir2 mutant; furthermore , maximal activation of rDNA origins was greater in sir2 mutants compared to WT , consistent with prior reports that Sir2 suppresses activation or DNA origins ( Fig 5 ) . Excessive activation of rDNA origins in the sir2 mutant and resulting sequestration of limiting replication factors was accompanied by reduced and delayed activation of ARS305 due to competition , consistent with previous reports [15 , 32] . Although the resolution of this technique is not sufficient to distinguish initiation from Mcm2-7 complexes at their normal location at the rARS from those displaced to the right , these results demonstrate that the advanced replication at the rDNA in a sir2 mutant initiates in the same general area as in WT . Consistent with previous reports , we found that the level of c-pro transcript is elevated more than 20-fold in the absence of Sir2 ( S3 Fig ) [33–36] . Initiating approximately 200 base pairs to the left of the rARS and proceeding rightward through the origin , c-pro transcription would be expected to generate the observed rightward displacement of Mcm2-7 from their loading site ( S3 Fig ) . If so , blocking c-pro transcription upstream of the rARS should prevent rightward displacement of Mcm2-7 complexes . To test this idea , we used a system developed by Kobayashi et al . [37] to generate a strain with transcription terminators in the rDNA array inserted between the start of c-pro transcription and the rARS , at a site marked by asterisk in Fig 1A , and we used Mcm2-MNase ChEC to monitor the location of the pre-RC ( Fig 6 ) . This system features two ribosomal repeats at the rDNA locus and the rRNA genes transcribed by RNA Pol II from a plasmid , which enables repeat alterations followed by their Fob1-mediated expansion . Our results demonstrate that premature termination of c-pro transcription ( S5A Fig ) with a terminator placed downstream of the c-pro transcription initiation site but upstream of the rARS prevents the rightward displacement of the pre-RC in a sir2 mutant , thus providing strong support for our hypothesis that the passage of RNA Pol II across the replication origin , as happens in the absence of Sir2 , displaces the pre-RC . Because the terminator sequence in a sir2 mutant prevented Mcm2-7 displacement , this system presented an opportunity to examine the effect of the terminator on rDNA replication timing . We reasoned that if displacement of Mcm2-7 is the proximal cause of early replication of the rDNA in a sir2 mutant , and if the terminator suppresses this displacement , then the terminator should also suppress the early replication phenotype . Unfortunately , we could not test this prediction because we found that in our experimental system , the rDNA replicated early even in WT cells , regardless of the presence or absence of SIR2 ( S1 Table ) . The rDNA arrays in our experiment were generated by repeat expansion in a strain that contained only two repeats and no nucleolus . We suggest that the process of expansion failed to recreate an intact nucleolus , which perturbed rDNA replication timing . In support of this idea , we noted that the nucleolar staining with Nop58-GFP marker is diffuse and fragmented in the strain subjected to rDNA expansion compared with the WT strain with intact nucleolus ( S5B Fig ) .
Although it has long been clear that heterochromatin is both transcriptionally silent and late replicating , the mechanism linking these two remains a mystery . On one hand , the lack of transcription could cause late replication , or vice versa; on the other hand , both phenomena could share a single cause [10] . Here we show that SIR2 deletion repositions replication machinery by virtue of derepressing a Pol II-transcribed non-coding RNA adjacent to the replication origin . The Mcm2-7 is pushed ahead by advancing RNA Pol2 in the absence of SIR2 , and this movement is blocked by a transcriptional terminator placed between the promoter and the rARS . Pre-RCs displaced by transcription have been shown to retain their activity [18] but our results suggest that their displacement at the rDNA , as observed in sir2 mutants , may be associated with their excessive and premature activation . How could the displacement of pre-RCs at rDNA origins increase their activation ? One possibility is that displacement of pre-RCs from their initial loading site allows repeated rounds of Mcm2-7 loading , and that this in turn advances replication timing . Such a model has been proposed and is consistent with the observations that more than one Mcm2-7 complex can be loaded at a single origin , and that Mcm2-7 ChIP-seq signals are stronger at early compared to late replicating yeast origins [38] . However , quantitation of our Mcm2-7 binding data clearly demonstrate decreased , rather than increased , levels of Mcm2-7 binding at the rDNA in sir2 mutants ( Fig 3D ) , refuting this model . Instead , we suggest that displacement of Mcm2-7 complexes from their site of loading promotes firing by liberating them from local chromatin features that restrain origin activation . In support of this idea , at its loading site at the rARS , each Mcm2-7 complex is flanked by a well-positioned nucleosome on both sides . In contrast , most of the repositioned Mcm2-7 complexes in sir2 cells will not have a flanking nucleosome , given the low nucleosome occupancy in that region . The absence of flanking nucleosomes could facilitate a step in origin activation subsequent to Mcm2-7 loading in sir2 cells . It is also possible , that Mcm2-7 displacement is not the causal event , but that transcription through the origins promote their activation by another mechanism . The majority of replication origins in budding yeast are found in intergenic regions [39 , 40] between convergently transcribed genes , which is expected to reduce transcription-mediated displacement of pre-RCs . The replication origins at the CUP1 locus , however , exhibit a striking similarity to the rDNA locus: This locus contains two or more copies of a tandem repeat , each of which contains a gene that confers copper-resistance ( CUP1 ) , an origin of replication , and a non-coding RNA that spans the origin ( RUF5; RNA of Unknown Function ) . In contrast to the situation at the rDNA , where SIR2 is known to repress transcription from c-pro , regulators of transcription of RUF5 have not been identified . If such regulators are identified , it will be interesting to learn whether they advance replication timing of the CUP1 origin and whether such organization of replication origins and non-coding RNAs constitute a recurring theme in repetitive genomic regions . Our discovery of subtle displacement of replicative helicases by RNA Pol II in heterochromatin relied on mapping of replicative helicase at a much higher level of resolution than is attainable with more widely used approaches to chromatin analysis , such as chromatin immunoprecipitation , and provides an experimental framework for examining associations between transcription and replication during development , aging and carcinogenesis in metazoans .
All yeast strains except for those with rDNA repeat expansion were in S288C background . Yeast experiments were carried out using standard YPD ( yeast peptone dextrose ) medium [2% ( wt/vol ) glucose , 1% yeast extract , 2% ( wt/vol ) peptone] . The full list of strains is provided in the S2 Table . To generate MCM2-MNase strains , sequences containing MNase and triple FLAG tags along with KanMX selectable marker were amplified from pGZ108 tagging plasmid [29] ( Addgene #70231 ) and inserted directly immediately upstream of the stop codon of MCM2 . To generate a strain with a transcriptional terminator we first introduced a marker ( HygR ) at the left border of the repeats in the strain TAK201 [37] by direct integration using the following primers: TAGGACATCTGCGTTATCGTTTAACAGATGTGCCGCCCCAGCCAAACTCCagattgtactgagagtgcac and AGCTTAACTACAGTTGATCGGACGGGAAACGGTGCTTTCTGGTAGATATGctgtgcggtatttcacaccg . Next , we introduced the CYC1 terminator between the site of c-pro transcription initiation and the rARS , between coordinates 458 , 950 and 458 , 951 , using a "pop-in/pop-out" strategy . The strain was first transformed with the PCR product amplified from plasmid pJH105 , which contains URA3 markers flanked by CYC1 terminator sequence on both sides , using the following primers: TCAGAGACCCTAAAGGGAAATCCATGCCATAACAGGAAAGTAACATCCCAgccccttttcctttgtcgatat and GAATAGTTACCGTTATTGGTAGGAGTGTGGTGGGGTGGTATAGTCCGCAT-ttacatgcgtacacgcgtttg . A resulting transformant with terminator-URA3-terminator fragment was used to generate a strain containing a single terminator by selecting URA3 pop-out events with 5-FOA . We next introduced FOB1 using a plasmid pTAK101 to enable repeat expansion . Upon the repeat expansion , we selected a strain that has lost both plasmid pTAK101 with FOB1 and plasmid pNOY353 with Pol-II transcribed 35S and 5S rDNA genes . A control strain , without terminator was also generated by repeat expansion . MNase-seq was carried out as described [23] . Cells grown to log phase from an overnight 25 mL culture were arrested in G1 with 3 μM alpha-factor for 1 . 5 hrs . or G2 with 20 mg/mL nocodazole for 2 . 5 hrs . at 30° C . Incubations with cdc6 temperature sensitive mutant were carried at out 23° C; log phase cells from an overnight culture were synchronized in G2 with 15 μg/mL nocodazole followed by an additional spike at 1 . 5 hrs . and 3 hrs . for a total 60 mg/mL . Temperature-sensitive cells were harvested after 3 . 5 hrs . incubation , washed twice with cold YEPD and released in 23° C or 37° C pre-warmed media with alpha-factor . Cells were arrested in G1 with 3 μM alpha-factor for 4 hrs . or 1 . 5 hrs . in permissive and non-permissive temperature , respectively . Arrested cells were crosslinked with 1% formaldehyde for 30 min at room temperature water bath with shaking . Formaldehyde was quenched with 125 mM glycine and cells were centrifuged at 3000 rpm for 5 min . Cells were washed twice with water and resuspended in 1 . 5 mL Buffer Z ( 1 M sorbitol , 50 mM Tris-HCl pH 7 . 4 ) with 1 mM beta-mercaptoethanol ( 1 . 1 μL of 14 . 3 M beta-mercaptoethanol diluted 1:10 in Buffer Z ) per 25 mL culture . Cells were treated with 100 μL 20 mg/mL zymolyase at 30° C for 20–30 min depending on cell density . Spheroplasts were centrifuged at 5000 rpm for 10 min and resuspended in 5 mL NP buffer ( 1 M sorbitol , 50 mM NaCl , 10 mM Tris pH 7 . 4 , 5 mM MgCl2 , 1 mM CaCl2 ) supplemented with 500 μM spermidine , 1 mM beta-mercaptoethanol and 0 . 075% NP-40 . Nuclei were aliquoted in tubes with varying concentrations of micrococcal nuclease ( Worthington ) , mixed via tube inversion , and incubated at room temperature for 20 mins . Chromatin digested with 1 . 9 U– 7 . 5 U micrococcal nuclease per 1/5th of spheroplasts from a 25 mL culture yielded appropriate mono- , di- , tri- nucleosome protected fragments for next-generation sequencing . Digestion was stopped with freshly made 5x stop buffer ( 5% SDS , 50 mM EDTA ) and proteinase K was added ( 0 . 2 mg/ml final concentration ) for an overnight incubation at 65° C to reverse crosslinking . DNA was extracted with phenol/chloroform and precipitated with ethanol . Micrococcal nuclease digestion was analyzed via gel electrophoresis prior to proceeding to library preparation . Sequencing libraries for both MNase-seq and ChEC-seq were prepared as described [21] . Mcm2-MNase ChEC was carried out as described [29] . Briefly , cells were first permeabilized with digitonin and then treated with calcium to activate MNase; DNA was extracted with phenol/chloroform and precipitated with NaCl and ethanol . MNase-tagged cells were grown at 30° C overnight in 50 mL cultures to 8 x 106 cells/mL , arrested in G1 with 3 μM alpha-factor for 1 . 5 hrs , cooled on ice for 3 mins , centrifuged at 1 , 500 x g for 2 mins , and washed twice in cold Buffer A ( 15 mM Tris pH 7 . 5 , 80 mM KCl , 0 . 1 mM EGTA ) without additives . Washed cells were carefully resuspended in 570 μL Buffer A with additives ( 0 . 2 mM spermidine , 0 . 5 mM spermine , 1 mM PMSF , ½ cOmplete ULTRA protease inhibitors tablet , Roche , per 5 mL Buffer A ) and permeabilized with 0 . 1% digitonin in 30° C water bath for 5 min . Permeabilized cells were cooled at room temperature for 1 min and 1/5th of cells were transferred in a tube with freshly made 2x stop buffer ( 400 mM NaCl , 20 mM EDTA , 4 mM EGTA ) /1% SDS solution for undigested control . Micrococcal nuclease was activated with 5 . 5 μL of 200 mM CaCl2 at various times ( 30 sec , 1 min , 5 mins , and 10 mins ) and the reaction stopped with 2x stop buffer/1% SDS . Once all time points were collected , proteinase K was added to each collected time points and incubated at 55° C water bath for 30 mins . DNA was extracted using phenol/chloroform and precipitated with ethanol . Micrococcal nuclease digestion was analyzed via gel electrophoresis prior to proceeding to library preparation . Library was prepared using total DNA , without any fragment size selection . DNA replication analysis was carried out as described [13] . Briefly , cells with G1 and S-phase DNA content were isolated from logarithmically growing cells using FACS . DNA from these cells was fragmented by sonication and sequenced . rDNA replication kinetics was determined by calculating the ratio of rDNA read depths from S-phase cells and G1 cells . We calculated relative abundance of Mcm2-7 at the rDNA as the fraction of reads that map to the rDNA over the total number of reads for each strain . The relative rDNA Mcm2-7 abundances among different strains are directly compared . RNA was extracted from logarithmically growing cells after spheroplasting using Trizol reagent and chloroform and purified after extraction with an RNA-Easy column . The sequences of the primers used in qRT-PCR for c-pro and PDA1 mRNA are provided in S3 Table . rDNA size was measured by qPCR using DNA that was extracted by phenol-choroform extraction and ethanol precipitation using the primers listed in S3 Table . As an internal standard qPCR was done in parallel on the DNA extracted from strains with 150 , 90 and 30 rDNA copies whose rDNA size had been verified by Pulsed Field Gel Electrophoresis . Sequencing was performed using an Illumina HiSeq 2500 in Rapid mode employing a paired-end , 50 base read length ( PE50 ) sequencing strategy . Image analysis and base calling was performed using Illumina's Real Time Analysis v1 . 18 software , followed by 'demultiplexing' of indexed reads and generation of FASTQ files , using Illumina's bcl2fastq Conversion Software v1 . 8 . 4 . Sequences were aligned using the gsnap alignment software suite and data were plotted using the ggplot R package . 2D gel electrophoresis was carried out as described [20] . Briefly , genomic DNA was prepared from 0 . 5% agar-embedded , G1-arrested cell or cells taken 30 , 60 and 90 minutes after release into medium with 200 mmol hydroxyurea , digested with NheI ( for rARS ) or EcoRV ( for ARS305 ) , and subjected to 2 dimensional agarose gel electrophoresis , followed by Southern blotting . | Eukaryotic genomes typically contain large regions of repetitive DNA , referred to as heterochromatin , that are both transcriptionally silent and late replicating . We provide a possible explanation for the association between transcriptional silencing and late replication . Budding yeast contains a histone deacetylase called SIR2 that was originally identified as a transcriptional repressor , but was later also found to ensure late replication of repetitive ribosomal DNA ( rDNA ) sequences . We show that the transcription that occurs in the absence of SIR2 directly displaces the helicase required for replication initiation at the rDNA . This work represents an important advance in understanding the interplay between transcription and replication at repetitive sequences by directly linking transcription with replication machinery in heterochromatin . | [
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... | 2019 | Sir2 suppresses transcription-mediated displacement of Mcm2-7 replicative helicases at the ribosomal DNA repeats |
Many tissues are sustained by adult stem cells , which replace lost cells by differentiation and maintain their own population through self-renewal . The mechanisms through which adult stem cells maintain their identity are thus important for tissue homeostasis and repair throughout life . Here , we show that a histone variant , His2Av , is required cell autonomously for maintenance of germline and cyst stem cells in the Drosophila testis . The ATP-dependent chromatin-remodeling factor Domino is also required in this tissue for adult stem cell maintenance possibly by regulating the incorporation of His2Av into chromatin . Interestingly , although expression of His2Av was ubiquitous , its function was dispensable for germline and cyst cell differentiation , suggesting a specific role for this non-canonical histone in maintaining the stem cell state in these lineages .
Many adult tissues with short-lived , highly differentiated cells such as blood and skin replace cells lost to turnover through the proliferation and differentiation of adult stem cells . Adult stem cells must also self-renew to maintain a source of differentiating cells in the long term . The mechanisms that control the balance between self-renewal and differentiation need to be tightly regulated to maintain homeostasis of adult tissues . Although recent work has focused on signals from the local microenvironment of the stem cell niche , responses to these signals take place in the context of cell autonomous properties of the stem cell state that influence the ability of adult stem cells to maintain their identity . Likely candidates for such cell autonomous properties include the state of chromatin at key regulatory genes that influence stem cell maintenance . The basic unit of eukaryotic chromatin , the nucleosome , is formed by DNA wrapped around an octamer containing two copies each of histones H2A , H2B , H3 , and H4 . Access to DNA by transcription factors and RNA polymerase is achieved by factors that control the post-translational modifications of core histones [1] and/or remodel nucleosomes [2] . The replacement of canonical histones with histone variants has recently emerged as an additional mechanism regulating chromatin accessibility [3] . Variants of the canonical histone H2A are highly conserved across species and play roles in transcriptional control , formation of heterochromatin boundaries , lineage commitment , and DNA repair . In yeast and mammals , H2AX is involved in recruiting factors to the sites of DNA damage [4] and H2A . Z is implicated in transcriptional regulation [5] , [6] . In Drosophila , His2Av , the only known variant of H2A , assumes functions of both H2AX and H2A . Z [7] . Drosophila His2Av and His2A share 55% of their amino acid sequences , with the C-terminal region of His2Av considerably longer than that of His2A [8] . Here , we show that the histone variant His2Av is required cell autonomously for the maintenance of two adult stem cell populations in the Drosophila testis . The stem cell-niche microenvironment at the apical tip of the Drosophila testis consists of the germline stem cells ( GSCs ) , which give rise to sperm [9]; the cyst stem cells ( CySCs ) , which give rise to the cyst cells that enclose germ cells as they differentiate [10] , [11]; and the post-mitotic somatic hub cells , to which GSCs and CySCs attach [12] , [13] . His2Av function is required for both GSC and CySC maintenance; however , its function was dispensable for the differentiation program in the germ and cyst cell lineages . Our results suggest that in the absence of DNA damaging agents , the transcriptional role of His2Av may be required to regulate the delicate balance between self-renewal and differentiation states in adult stem cells .
Immunostaining of wild-type adult testes revealed His2Av protein expression in many cell types in the adult testis of Drosophila . At the apical tip of the testis , His2Av localized to the nuclei of somatic cells of the hub , GSCs ( Fig . 1A ) and CySCs ( Fig . 1C ) . In differentiating spermatocytes , His2Av was concentrated on the autosomal and sex bivalent chromosomes within the nucleus ( Fig . 1B , inset ) . His2Av also localized to the nuclei of differentiating somatic cyst cells associated with spermatocyte cysts ( Fig . 1D ) . Clonal analysis revealed that His2Av function is required cell autonomously for stem cell maintenance in the Drosophila male germline . Negatively marked GSCs lacking His2Av function were generated in adult fly testes by mitotic recombination using the FLP/FRT system in a His2Av810/+ background [14] . GSCs at day 3 post clonal induction ( PCI ) ( Fig . 1E ) and later germline clones at day 8 PCI ( Fig . S1A ) homozygous mutant for His2Av810 did not exhibit His2Av staining , indicating specificity of the antibody towards His2Av protein and a sharp decline in protein levels in His2Av mutant GSCs by at least day 3 PCI . At day 2 PCI , GSCs homozygous mutant for His2Av810 were detected in 75% of the testes scored , similar to the 81 . 4% observed in controls ( Fig . 1F ) . By day 8 PCI , the percentage of testes with at least one marked GSC clone dropped to 2% for the His2Av810 mutant ( Fig . 1F ) , suggesting a defect in GSC maintenance upon loss of His2Av function , while 64 . 8% of control testes had at least one marked GSC . Consistent with the loss of mutant GSCs , His2Av810 mutant spermatocytes were not maintained over time after clone induction . At day 4 PCI , His2Av810 spermatocytes were observed in 86% of testes . However , by day 12 PCI , His2Av810 mutant spermatocytes were no longer observed ( Fig . 1G ) . A genomic transgene carrying the His2Av coding sequence under control of its endogenous promoter and fused to the mRFP coding sequence ( His2Av-mRFP ) [15] rescued the loss of spermatocytes , indicating that the failure to maintain GSCs and their differentiating progeny was due to loss of His2Av function ( Fig . 1G , Fig . S1B , C ) . Knockdown of His2Av function specifically in GSCs and early germ cells by expression of a RNAi hairpin for His2Av using the nanos-GAL4-VP16 ( NGVP16 ) driver also indicated a cell autonomous role for His2Av in GSC maintenance . By day 3 after RNAi expression , induced by shifting flies from 18°C to 30°C , His2Av protein levels in GSCs dropped considerably compared to controls ( Fig . S2A , B ) . At day 4 after RNAi induction , visualization of testes by phase contrast microscopy revealed the presence of spermatocytes and elongated spermatids in testes expressing His2Av RNAi and in controls ( Fig . 2A , B ) . By day 12 , however , testes expressing His2Av RNAi exhibited germ cell loss and did not contain spermatocytes or elongated spermatids ( Fig . 2D ) , while control testes at day 15 still had both cell types ( Fig . 2C ) . Quantitation of GSC number revealed that the loss of germ cells observed 12 days after RNAi induction was due to a failure to maintain GSCs . At day 0 , His2Av RNAi expressing and control testes had an average of 7 and 8 . 2 GSCs , respectively ( Fig . 2E , F , I ) . By day 12 , the number of GSCs adjacent to the hub in testes expressing His2Av RNAi had dropped to 0 , while control testes contained an average of 7 . 8 GSCs per testis hub ( Fig . 2G , H , I ) . In contrast to its role in GSCs , His2Av was not required cell autonomously for germ cell differentiation . Germline clones homozygous mutant for His2Av810 differentiated into spermatocytes ( Fig . 3A and Fig . S1A ) and round and elongating spermatids ( Fig . 3B , C ) , as observed 8 days after clone induction . Mutant onion stage round spermatids had the normal size and 1∶1 ratio of nuclei to mitochondrial derivatives , indicating successful progression through meiotic divisions ( Fig . 3B ) . Knockdown of His2Av in late spermatogonial cysts by RNAi expressed under the control of the bam-Gal4 driver confirmed that His2Av function is dispensable for the differentiation program of germ cells at the later stages . His2Av protein levels were greatly reduced in spermatocytes upon expression of RNAi ( Fig . S2C , D ) , yet spermatocytes lacking His2Av protein for 8 days after RNAi induction were still able to differentiate , undergo meiosis , and give rise to elongated spermatids ( Fig . 3D , E ) . Although His2Av mutant GSCs were lost to differentiation , they did not appear to do so by accumulating Bam protein earlier than their heterozygous counterparts . The accumulation of Bam protein in transit-amplifying spermatogonial cells stops proliferation and initiates differentiation to spermatocytes [16] . Immunostaining for Bam protein 5 days PCI revealed that neither heterozygous His2Av810/+ nor homozygous His2Av810 mutant GSCs or gonialblasts expressed Bam protein ( Fig . 3F ) . Bam protein did accumulate at the correct time during the differentiation program in His2Av mutant cells , at the 4-cell spermatogonial stage ( Fig . 3F ) , similar to in wild-type spermatogonial cysts . Consistent with the correct temporal accumulation of Bam protein , germ cells lacking His2Av function underwent 4 rounds of spermatogonial divisions , producing cysts with 16 spermatocytes ( Fig . 3G ) . Consistent with its expression in the cyst cell lineage , His2Av function was also required cell autonomously for CySC maintenance . Although both His2Av810 mutant and control CySCs were present at comparable frequencies at day 2 PCI , by day 8 PCI almost all testes lacked His2Av810 mutant CySCs , while control CySCs were maintained ( Fig . 4A ) . Consistent with the loss of mutant CySCs , His2Av810 mutant cyst cells expressing the differentiation marker Eya were also lost over time . His2Av810 mutant cyst cells were observed in 100% of testes at day 4 PCI , but by day 12 PCI His2Av810 mutant Eya-positive cyst cells were almost entirely absent ( Fig . 4B ) . The His2Av-mRFP transgene rescued the loss of His2Av810 homozygous mutant CySCs , suggesting that the failure to maintain CySCs was due to loss of His2Av function . At day 2 PCI , an average of 43 . 2% ( n = 32 ) of testes contained His2Av810 mutant CySCs , while under the same conditions , 67 . 5% ( n = 23 ) testes from sibling males carrying the His2Av-mRFP transgene contained His2Av mutant CySCs ( data not shown ) . The percentage of testes containing His2Av810 mutant CySCs at day 8 PCI dropped to 3% ( n = 26 ) , while 42 . 1% ( n = 31 ) of testes from males carrying the His2Av-mRFP transgene contained marked CySCs . The failure to maintain CySCs was not due to downregulation of the transcriptional repressor Zinc-finger homology-1 ( Zfh-1 ) , which is expressed in CySCs and is required for CySC maintenance [17] . At day 3 PCI , when only 20% of testes scored had homozygous mutant CySCs , Zfh-1 expression in His2Av810 homozygous mutant CySCs was comparable to that in neighboring wild-type CySCs ( Fig . 4C ) . As in the germ line , His2Av function was not required for cyst cell differentiation . Cyst cells lacking His2Av function differentiated successfully at least to the stage at which they express the differentiation marker Eya and are associated with differentiating germ cells ( Fig . 4D ) . In addition to the survival and differentiation of germ cells and cyst cells lacking His2Av , the classic eye test revealed that His2Av function might be dispensable for cell survival in the eye tissue . Eyes composed exclusively of cells lacking His2Av function were generated using the EGUF/hid system [18] . When mitotic recombination was not induced , eye precursor cells expressed the GMR-hid transgene and failed to develop , resulting in adult flies with tiny eyes ( Fig . 4E ) . In contrast , when clones were induced , cells lacking His2Av function produced eyes ( Fig . 4F ) , although they appeared slightly smaller and rougher compared to controls ( Fig . 4G ) , suggesting that His2Av might contribute to proper cell proliferation and/or differentiation in this tissue . Together , the results from clonal and RNAi analysis in the germline , somatic cyst , and eye cell lineages suggest that in the absence of DNA damaging agents , Drosophila His2Av function is required for adult stem cell maintenance but not for cell survival or differentiation . Analysis of His2Av mutant GSCs revealed that His2Av function was not required to maintain three previously defined STAT-dependent characteristics of GSCs: 1 ) attachment to the hub through E-cadherin mediated adherens junctions , 2 ) oriented cell division [19] , and 3 ) upregulation of STAT92E protein in response to Unpaired ( Upd ) signaling from the hub . His2Av mutant GSCs localized E-Cadherin-GFP ( E-Cad-GFP ) , expressed in GSCs by the nanos-Gal4 driver and detected 5 days PCI , to the hub-GSC interface similar to neighboring heterozygous GSCs ( Fig . 5A ) and as previously shown [12] . The expression of E-Cad-GFP in GSCs did not result in an increase in His2Av mutant GSC maintenance; His2Av810 mutant GSCs in testes from sibling males either expressing or lacking the expression of E-Cad-GFP were lost at the same rate ( Fig . 5B ) . His2Av also did not appear to be required for the stereotypical orientation of centrosomes in GSCs that sets up the mitotic spindle orientation and the subsequent asymmetric outcome of GSC division [12] . Analysis of testes 3 days after induction of His2Av810 clones revealed that in GSCs with two centrosomes , one centrosome was found adjacent to the hub-GSC interface in 86 . 3% of His2Av810 mutant GSCs , similar to neighboring heterozygous His2Av810/+ GSCs ( 86 . 42% ) and FRT control GSCs ( 84 . 62% ) ( Fig . 5C–E ) . Loss of His2Av function did not substantially alter the accumulation of STAT92E , an indicator of JAK-STAT activity [20] , in GSCs . His2Av810 mutant GSCs remaining adjacent to the hub 5 days PCI had STAT92E protein levels comparable to neighboring heterozygous GSCs ( Fig . 5F ) , suggesting that loss of GSCs in His2Av mutants is not due to failure to express STAT92E . Conversely , GSCs homozygous mutant for either Stat92E06346 ( Fig . 5G ) or Stat92Ejc46 ( data not shown ) expressed His2Av protein at levels comparable to neighboring heterozygous GSCs , suggesting that His2Av expression in GSCs was not dependent on STAT92E function . Loss of His2Av function did not suppress the overproliferation of CySC-like and GSC-like cells in testes with forced activation of the JAK-STAT pathway . When the Upd ligand was expressed ectopically in early germ cells under the control of the nanos-Gal4 driver , larval testes heterozygous for His2Av had an abundance of small Vasa-positive GSC-like cells with dot spectrosomes and Zfh-1 positive CySC-like cells ( Fig . 5H ) . Under the same conditions , testes from sibling nos-Gal4/UAS-Upd; His2Av810/Df ( 3R ) BSC524 larvae also exhibited an abundance of GSC-like and CySC-like cells ( Fig . 5I ) , although there were subtle signs of differentiating germ cells . In the absence of His2Av function , 16 out of 37 ( 43 . 24% ) nos-Gal4; UAS-Upd larval testes had a few germ cell cysts containing branched fusomes ( Fig . 5I″″ ) . In the same experiment , only 1 out of 37 ( 2 . 7% ) testes from nos-Gal4/UAS-Upd; His2Av/+ larvae exhibited branched fusomes . Thus , in the absence of His2Av function , a small population of His2Av mutant germ cells appears to initiate the differentiation program even under conditions of high JAK-STAT activation . Clonal analysis suggested that the chromatin remodeling factor Domino , the homolog of yeast Swr1 [21] , which exchanges His2A variant for His2A in yeast [22] , [23] , is required for stem cell maintenance . In the Drosophila testis , when negatively marked clones of domk08108 were generated using the FLP/FRT system , the percentage of testes carrying marked domk08108 homozygous GSCs or CySCs was indistinguishable from the control at day 2 PCI ( Fig . 6A , B ) . However , the percentage of testes carrying marked domk08108 homozygous GSCs steadily decreased over time after clonal induction and dropped to zero by day 8 ( Fig . 6A ) . Similarly , the percentage of testes with domk08108 mutant CySCs dropped from 74% at day 2 , to 14 . 5% at day 4 , to 0% at day 15 ( Fig . 6B ) . Immunostaining analysis revealed that Domino function might be required for the localization of His2Av to chromatin in GSCs , similar to the function of the corresponding Swr1 complex in yeast . At day 6 PCI , nuclei in GSCs lacking Domino function had reduced levels of His2Av protein compared to control GSCs ( Fig . 6C–C′″ ) . Quantification of His2Av immunofluorescence intensity revealed that the loss of domino function reduced His2Av protein levels in GSCs by an average of 2-fold . The average ratio of His2Av immunostaining per unit area in GSCs that were homozygous for domk08108 compared to His2Av immunostaining per unit area in GSCs heterozygous for domk08108 within a testis ( n = 28 testes ) was 0 . 56 . In contrast , in FRT 42D control ( n = 18 testes ) , the ratio of His2Av immunostaining per unit area in GFP negative to GFP positive GSCs was 1 . 12 ( Fig . 6D ) . Consistent with a role for Domino in His2Av incorporation and function in GSC maintenance , the loss of His2Av810 mutant GSC clones increased in a domk08108/+ genetic background ( Fig . 6E ) . At day 2 PCI , 65 . 5% of testes contained His2Av810 homozygous mutant GSC clones , while under the same conditions , only 51 . 1% of testes from sibling males carrying the dom allele had marked GSC clones , possibly due to reduced incorporation of His2Av into chromatin before clonal induction . Under the same conditions at day 2 PCI , an average of 92% of testes from both domk08108/+; His2Av810 and sibling His2Av810 males had spermatocyte clones , suggesting that clonal induction occurred at the same rate in both genetic backgrounds ( data not shown ) . The percentage of testes with marked His2Av mutant GSCs was also lower at days 3 and 5 PCI in males with the domk08108/+ allele compared to sibling males without the dom allele . In contrast to the function of Domino , the chromatin remodeling factor ISWI , which also functions in GSC and CySC maintenance [24] , did not appear to be required for the localization of His2Av to chromatin in GSCs . Immunostaining for His2Av protein in testes containing ISWI2 homozygous mutant GSCs 6 days PCI revealed that the levels of His2Av protein in ISWI mutant GSCs were comparable to neighboring ISWI2/+ GSCs ( Fig . 6F ) . The average ratio of His2Av immunostaining intensity per unit area for GSCs homozygous mutant for ISWI2 to neighboring ISWI2/+ GSCs within the same testis ( n = 24 testes ) was 0 . 96 ( Fig . 6D ) . Similarly , ISWI protein levels in the nuclei of His2Av810 mutant GSCs were comparable to that in heterozygous GSCs ( Fig . 6G ) , suggesting that His2Av might not be required to recruit or maintain ISWI on chromatin . ISWI did not exhibit a strong genetic interaction with His2Av to maintain GSCs in the adult testes . The percentage of testes with marked His2Av810 mutant GSC clones in ISIW2/+; His2Av810 testes was comparable to that in testes lacking the ISWI allele at days 2 , 3 , and 8 PCI , only falling slightly at day 5 PCI ( Fig . 6H ) . Loss of His2Av function did not globally alter levels of the epigenetic marks associated with transcriptional state in GSCs . Immunostaining with antibodies that recognize H3K4 tri-methyl ( H3K4me3 ) ( Fig . 7A ) , mostly associated with transcriptionally active/poised chromatin regions , and H3K27 tri-methyl ( H3K27me3 ) ( Fig . 7B ) , mostly associated with transcriptionally inactive regions of chromatin [1] on testes with His2Av810 mutant GSCs 5 days PCI revealed that the levels of these epigenetic marks were comparable in mutant and heterozygous GSCs . Likewise , the protein levels of Scrawny ( Scny ) , a histone H2B deubiquitinase required to prevent premature expression of differentiation genes in adult stem cells [25] , were also not altered in His2Av mutant GSCs ( data not shown ) . Furthermore , His2Av protein levels scored 6 days PCI were unaltered in GSCs homozygous mutant for scny02331 ( Fig . 7C ) or scnye00340 ( data not shown ) compared to neighboring scny/+ heterozygous GSCs . Although scny mutant follicle cells in the Drosophila ovary exhibit elevated levels of H3K4me3 [25] , male GSCs homozygous mutant for either scny02331 ( Fig . 7D ) or scnye00340 ( data not shown ) did not exhibit changes in H3K4me3 levels compared to neighboring heterozygous GSCs . These data suggest that loss of His2Av or Scny function was not associated with dramatic changes in transcription in the testis , at least when assayed at a global level by immunostaining for histone marks .
Our results reveal that the histone variant His2Av is required cell autonomously for maintenance of two different adult stem cell types , GSCs and CySCs , in the Drosophila male gonad , but not for the differentiation of the progeny in these two stem cell lineages . The specific requirement for His2Av for adult stem cell maintenance suggests that His2Av may play critical role ( s ) in the mechanisms that maintain the ability of adult stem cells to self-renew rather than differentiate . His2Av function has been implicated in both transcriptional repression and transcriptional activation . His2Av could maintain adult stem cells by either favoring repression of pro-differentiation genes and/or activation of genes necessary for stem cell identity and function . In yeast , H2A . Z occupies transcriptionally inactive genes and intergenic regions [26] , while in Drosophila , His2Av is required for the establishment of heterochromatin and transcriptional repression [27] . Conversely , studies indicate that in Drosophila , yeast , and chicken , His2Av is enriched at nucleosomes downstream of the transcription start site of active or poised genes [28] , [29] , [30] . Nucleosomes and histone dimers containing H2A . Z appear to be less stable than nucleosomes containing the canonical histone H2A [31] , [32] , [33] . This lower stability may favor a more open chromatin , giving transcriptional activators or repressors better access to the DNA . Consistent with this model , a recent study showed that H2A . Z promotes self-renewal and pluripotency of murine embryonic stem cells ( ESCs ) by facilitating the binding of Oct4 to its target genes and the Polycomb repressive complex 2 to differentiation genes [34] . However , in ESCs , unlike in Drosophila male GSCs and CySCs , His2A . Z function was also required for the expression of differentiation genes when ESCs were grown under conditions that induce differentiation [34] , [35] . We propose that the requirement of His2Av for adult stem cell maintenance , but not for differentiation , may reflect a subtle role for His2Av in maintaining expression of genes required for self-renewal versus differentiation . Adult stem cells lie at the cusp of two alternate fate choices , self-renewal and differentiation; the progeny of stem cell division are maintained in a state where they can execute either self-renewal or differentiation programs depending on local cues . The requirement for this balanced , bi-potential state may make adult stem cells more sensitive to the small alterations in the relative levels of key transcripts associated with the loss of His2Av function , tilting the balance from stem cell maintenance to onset of differentiation . Consistent with the model that His2Av may alter transcriptional levels subtly , H2A . Z was shown to be required to fine-tune the transcriptional state of hsp70 and a wide variety of other genes in response to temperature changes in Arabidopsis [36] , [37] . The ATP-dependent chromatin-remodeling factor Domino is required for GSC and CySC maintenance in the male germline , as previously shown for somatic follicle stem cells in the female gonad [38] . The yeast Swr1 complex containing the homolog of Drosophila Domino exchanges His2A with Htz1 ( the yeast His2A variant ) [22] , [23] , [39] and in Drosophila , Domino- containing Tip60 chromatin remodeling complex has been shown to exchange phospho-His2Av with unmodified His2Av in in vitro assays [40] . Our studies indicate that Domino function is required in vivo in GSCs for the incorporation of His2Av into chromatin . Nuclei of domino mutant GSCs had lowered but still detectable levels of His2Av protein , possibly due to the weak domino allele used in this study . Alternatively , incorporation of His2Av in some regions of the chromatin may occur independently of Domino function , as has been reported in yeast , in which stress-responsive genes exhibit Swr1-independent incorporation of Htz in the coding region [41] . Although ISWI , like His2Av , is required for GSC and CySC maintenance in the male germline [24] , these proteins may function in parallel pathways to maintain adult stem cells in the testis . The ISWI containing nucleosome-remodeling factor ( NURF ) was shown to maintain GSCs and CySCs in the Drosophila testis by positively regulating the JAK-STAT signaling pathway; GSCs mutant for components of the NURF complex exhibited low levels of STAT92E protein [24] . In contrast , as discussed below , His2Av may function independently of the JAK-STAT signaling pathway . Our results indicate that His2Av may function independently of the JAK-STAT signaling pathway to provide a chromatin environment that allows for stem cell maintenance . Expression of the His2Av and STAT92E proteins in GSCs was not dependent on each other . Our studies indicate that His2Av may not be required for expression of at least one other key STAT-dependent gene in CySCs . Activation of the JAK-STAT signaling pathway in response to the Upd signal from the hub is important for CySC maintenance , possibly in part through STAT-dependent transcription of Zfh-1 [17] . However , CySCs lacking His2Av function still expressed Zfh-1 . In GSCs , activation of the JAK-STAT pathway is important for maintaining hub-GSC adhesion and for centrosome orientation [19] , both of which appeared unaffected in His2Av mutant GSCs . Loss of His2Av function did not strongly suppress the phenotype associated with ectopic overexpression of Upd in the testis , although a few His2Av mutant germ cells were able to initiate differentiation , possibly due to relatively lower levels of JAK-STAT activation in these cells . Even though loss of His2Av normally resulted in differentiation of GSCs and CySCs , the requirement for His2Av function can be overridden by high levels of activation of the JAK-STAT pathway , possibly maintaining somatic CySCs in a stem cell like state , which may fail to provide a microenvironment for germ cells to initiate differentiation [19] , [42] .
Fly stocks were raised on cornmeal/molasses medium at 25°C unless stated otherwise . Stocks are from the Bloomington Stock Center unless specified otherwise . Mutant alleles used in this study include 1 ) w;FRT82B , His2Av810/TM6B , Tb , carrying a 311 base pair deletion that removes the second exon of the His2Av gene [43] , 2 ) w;FRT82B , His2Av05146/TM3 , 3 ) w;; Df ( 3R ) BSC524/TM6b , Tb , a deletion that encompasses the His2Av gene , 4 ) the Stat92E alleles , FRT82B , Stat92E06346/TM3 and FRT82B , Stat92EJ6C8/TM3 ( gift from E . Matunis ) , 5 ) y , w , ey-Flp , GMR-lacZ; FRT42D , domk08108/CyO , y+ , a loss of function allele ( also known as dom1 ) with a P-element inserted at the 3′ boundary of the first exon [21] obtained from DGRC , 7 ) y w;FRT 42D , ISWI2 , sp/SM5 , Cy , sp , a null allele carrying a nonsense mutation [44] , 8 ) the scrawny alleles: FRT 80B , scnyl ( 3 ) 02331 and FRT 80B , scnye00340 [25] . w; His2Av-mRFP; FRT82B , His2Av810/TM6B , Tb flies were used to rescue GSC and CySC loss . The His2Av-mRFP construct rescues the lethality of His2Av05146 mutant [15] . The following flies 1 ) hs-FLP122;;FRT82B , ubi-nGFP , 2 ) hs-FLP122;nos-GAL4;FRT82B , tub-LacZ ( gift from D . Kalderon ) , 3 ) hs-FLP122;;FRT80B , ubi-nGFP , 4 ) hs-FLP122;FRT42D , ubi-nGFP were used to induce marked clones in the testes . y , w;ey-GAL4 , UAS-FLP;FRT82B , GMR-hid/TM2 flies were used to induce marked clones in adult eyes . FRT82B , FRT80B and FRT42D were used as wild-type controls for clone induction . Other stocks used include w , sa-GFP [45] , UAS-Upd [46] , UAS-DEFL #6-1 ( UAS-E-Cad-GFP ) [47] from DGRC , nanos-GAL4 , UAS-Dicer2;; nanos-GAL4VP16 ( NG4VP16 ) and ;; Bam-GAL4 . RNAi flies against His2Av ( Transformant ID #110598 ) were obtained from the Vienna Drosophila RNAi Center . A heteroallelic combination of His2Av810 and Df ( 3R ) BSC524 survives until the third instar larval stage when grown at 25°C for 2 days and then shifted to 29°C . The effects of loss of His2Av function in testes ectopically expressing Upd ligand was analysed in the third-instar larval progeny of nanos-GAL4; Df ( 3R ) BSC524/TM6B , Tb and UAS-Upd; His2Av810/TM6B , Tb . Tb-positive larvae ( heterozygous for either His2Av810 or Df ( 3R ) BSC524 ) expressing UAS-Upd under the nanos-Gal4 driver were used as controls Testes were dissected in 1× phosphate-buffered saline ( PBS ) and fixed in 4% formaldehyde in PBS for 20 minutes at room temperature , washed twice for 30 minutes each in PBS with 0 . 3% Triton X-100 and 0 . 6% sodium deoxycholate . Testes were incubated overnight at 4°C in primary antibodies against Armadillo ( Arm , mouse 1∶10; Developmental Studies Hybridoma Bank ( DSHB ) ) [48] , Fas3 ( mouse 1∶10; DSHB ) [49] , α-spectrin ( mouse 1∶10; DSHB ) [50] , Eyes absent ( Eya , mouse 1∶10; DSHB ) [51] , E-cadherin ( mouse 1∶10 , DSHB ) [52] , Green Fluorescent protein ( GFP , rabbit 1∶400–1∶1000; Invitrogen and Sheep 1∶1000 , Abd-Serotec ) , β-Galactosidase ( rabbit 1∶1000; Cappel ) , Histone H3 lysine 4 trimethyl ( H3K4me3 , rabbit 1∶200: Cell Signaling ) , Histone H3 lysine 27 trimethyl ( H3K27me3 , rabbit 1∶200: Cell Signaling ) , His2Av ( rabbit 1∶1000; gift from R . Glaser ) [53] , Traffic-jam ( Tj , guinea pig 1∶5000; gift from D . Godt ) [54] , Vasa ( goat 1∶50; Santa Cruz Biotechnology ) , ©-tubulin ( mouse 1∶50; Sigma ) , Zfh-1 ( rabbit 1∶5000; gift from R . Lehman ) , STAT92E ( rabbit 1∶1000; gift from E . Bach ) [55] , Scrawny ( guinea pig 1∶200; gift from M . Buszczak ) [25] and ISWI ( rabbit 1∶100; gift from J . Tamkun ) [56] . Secondary antibodies used were from the Alexa Fluor-conjugated series ( 1∶500; Molecular Probes ) . Samples were mounted in VECTASHIELD medium containing DAPI to visualize DNA ( Vector Labs H-1200 ) . Immunofluorescence images were obtained with a Leica SP2 Confocal Laser Scanning microscope . Phase and clonal analysis images were obtained using a Zeiss Axioskop microscope and SPOT RT3 camera by Diagnostic Instruments or CoolSNAPez camera by Photometrics . Images were processed using Adobe CS4 Photoshop and Illustrator . Comparison of intensity of His2Av staining in GSCs was performed using the ImageJ program [57] . The nuclear area in GSCs was selected based on the DAPI staining and the average intensity of His2Av immunostaining within the nucleus was measured using ImageJ . An average of immunofluorescence intensity per unit area for all GSCs homozygous ( identified as GFP negative ) or heterozygous ( identified as GFP positive ) for a given genotype was calculated for each testis . The relative level of His2Av protein was calculated as a ratio of the average immunofluorescence intensity per unit area for homozygous GSC to heterozygous GSC within each testis . Similar results were obtained when anti-His2Av intensity was normalized to the intensity for DAPI staining for each GSC . Homozygous His2Av mutant clones in a heterozygous background were generated by crossing either 1 ) hs-FLP122;;FRT82B , ubi-nGFP , 2 ) hs-FLP122; FRT42D , domk08108/CyO;FRT82B , ubi-nGFP , 3 ) hs-FLP122; FRT 42D , ISWI2 , sp/CyO;FRT82B , ubi-nGFP , or 4 ) hs-FLP122;nos-GAL4;FRT82B , tub-LacZ virgin females to w;;FRT82B , w;;FRT82B , His2Av810/TM6B , Tb or w;; UAS-DEFL #6-1 , FRT82B , His2Av810/TM6B , Tb [The UAS-DEFL #6-1 ( UAS-E-Cad-GFP ) containing chromosome was recombined to the FRT82B , His2Av810 chromosome] males . Homozygous domk08108 or ISWI2 mutant clones were obtained by crossing males of the alleles to hs-FLP122;FRT42D , ubi-nGFP virgin females , while males of scny alleles were crossed to hs-FLP122;;FRT80B , ubi-nGFP males . The progeny were raised at 25°C and heat-shocked at 37°C for two hours each on two consecutive days at the pupal stage . GSCs homozygous mutant for His2Av810 or other alleles were identified by their lack of GFP ( or β-Galactosidase ) , presence of the germ cell marker Vasa , and contact with the hub . Homozygous clones of CySCs generated by heat shock induced mitotic recombination were identified by their lack of GFP ( or β-Galactosidase ) and the germ cell marker Vasa , by the presence of Tj , a marker of the cyst cell lineage , and by their proximity to the hub . Homozygous mutant germline clones generated in His2Av05146/+ resulted in the loss of mutant GSCs ( Fig . S3A ) and spermatocytes ( Fig . S3B ) over time after clone induction . However , this loss of marked cells was not associated with a loss of anti-His2Av staining ( Fig . S3C′ ) , and the loss of homozygous mutant germ cells was not rescued by the presence of His2Av-mRFP transgene ( Fig . S3B ) , suggesting that a mutation other than His2Av on the chromosome might be responsible for GSC loss in this line . FLP-medicated mitotic recombination was induced in eye precursor cells by crossing y , w;ey-GAL4 , UAS-FLP;FRT82B , GMR-hid/TM2 virgins to males carrying FRT 82B , His2Av810 ( or FRT control ) . Eye precursor cells carrying one copy of the dominant cell lethal transgene GMR-hid fail to survive , thereby generating eyes composed entirely of cells homozygous for His2Av810 ( or the FRT control ) . RNAi knockdown experiments were carried out by crossing flies carrying His2Av RNAi hairpin under the UAS regulatory sequence to either UAS-Dicer2;;NG4VP16 females or Bam-GAL4 . The progeny were raised at 18°C until eclosion and transferred to and held at 30°C . | Many tissues in the body are maintained by adult stem cells , which are dedicated but undifferentiated precursors that both maintain their population throughout life and produce daughter cells that differentiate to replace cells lost to turnover or damage . Here we show that the histone variant His2Av is required cell autonomously for maintenance of both germline and somatic cyst stem cells in the Drosophila testis . Although His2Av is expressed ubiquitously , under normal conditions , function of this histone variant was not required for correct differentiation of stem cell progeny in testes or for the survival of cells in the developing eye . We propose that adult stem cells maintain a plastic , bipotential state able to switch between self-renewal and differentiation and that His2Av may provide a chromatin state that helps bias transcription programs towards the stem cell fate . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | The Histone Variant His2Av is Required for Adult Stem Cell Maintenance in the Drosophila Testis |
Complex tissues , such as the brain , are composed of multiple different cell types , each of which have distinct and important roles , for example in neural function . Moreover , it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity , in particular at the level of gene expression . The ability to study this heterogeneity has been revolutionised by advances in experimental technology , such as Wholemount in Situ Hybridizations ( WiSH ) and single-cell RNA-sequencing . Consequently , it is now possible to study gene expression levels in thousands of cells from the same tissue type . After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types . Whilst many clustering algorithms exist , they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study . When such information exists it provides important insights that should be directly included in the clustering scheme . To this end we have developed a clustering method that uses a Hidden Markov Random Field ( HMRF ) model to exploit both quantitative measures of expression and spatial information . To accurately reflect the underlying biology , we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters . We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii . Our approach allows known cell types to be identified as well as revealing new , previously unexplored cell types within the brain of this important model system .
Complex organisms are heterogeneous at several levels . For example , one can divide the body into functional organs: the skin , the brain , the liver and so on . This anatomical and functional classification implies that distinct organs are composed of different cell types . Interestingly , these functional building blocks are also not composed of homogeneous cell types . Indeed , they are composed of several tissues that together make up a complex organ . For example , the skin of mammals can be described as the superposition of the Epidermis , Dermis and Hypodermis [1] . However , even with this more precise description , each of these tissues will be heterogeneous . For instance in the Dermis , the cells making up the sweat glands will not be the same as the cells in the hair follicles . Additionally , this heterogeneity does not stop at this sub-sub classification: heterogeneity is still present and , with fine enough measurements methods , this remains true to the single cell level [2] . When reducing the scale of study , the classification of cells into distinct groups ceases to be anatomical . Instead , molecular biology has allowed scientists to define molecular characteristics that distinguish individual cells . The most widely used characteristic is mRNA expression , and gene expression signatures are now commonly used to define cell types [3] , [4] . Conceptually , if a set of cells have similar expression profiles , this information can be used to gather these cells into a specific cell type; we focus on this , molecular , definition of a cell type in the remainder of this manuscript . To do this , gene expression measurements at the single cell level within the tissues under study are necessary . Recent technological developments have facilitated this shift from tissue to single cell resolution: in-situ hybridization [5] in a few organisms including P . dumerilii and single cell RNA sequencing assays [6] are amongst a number of methods that allow gene expression to be measured at the single cell level [7] . Given this , one key challenge is to develop computational methods that use the expression data to cluster single cells into robust groups , which can then be examined to determine their likely functional roles . Many popular clustering methods ( e . g . , hierarchical clustering , k-means and independent mixture models ) exist and can be applied to address this problem [8]–[10] . However , these methods fail to take into account the spatial location of each cell within the tissue under study — when such information is available [3] , [11] , [12] , it is extremely useful and should be incorporated into the downstream analysis . Specifically , we can hypothesise that cells that are close together are more likely to belong to the same cell type . In other words , if a cell has a "slightly" more "similar" expression profile to a typical cell in cell type b than in cell type a but all the surrounding cells have been classified as belonging to cell type a it seems sensible to assign this cell to cell type a . However , it is also important to note that cell migration , which takes place during the development of complex tissues , can lead to isolated cells with very different expression profiles than their neighbours , which also needs to be accounted for . To address these problems and , in particular , to utilise both the spatial and the quantitative information , we extended a graph theoretical approach developed for image segmentation to reconstruct noisy or blurred images [13] , a method that finds its roots in the field of statistical mechanics as the Ising model [14] and its generalization , the Potts model [15] . The core concept of this method ( Figure 1 ) is to estimate the parameters of a Markov Random Field based model using mean-field approximations to estimate intractable values as described in [16] . We use an Expectation-Maximization ( EM ) procedure to maximize the parameters as described in [13] , [16] . To the best of our knowledge , such methods have not previously been applied to 3-Dimensional gene expression data . Additionally , from a theoretical perspective , we extended current models by allowing the degree of spatial cohesion per cluster to differ , thus allowing for the possibility that some cell types are more spatially coherent than others . After validating our approach using simulated data , we demonstrated its utility by applying it to data generated using methods described in Tomer et al . [3] who were interested in studying the ancestral bilaterian brain .
Tomer et al . [3] used Wholemount in Situ Hybridisation ( WiSH ) to study the spatial expression pattern of a subset of genes , at single cell resolution , in the brain of the marine annelid Platynereis dumerilii 48 hours post fertilization ( hpf ) . P . dumerilii , is an interesting biological model , sometimes considered a "living fossil" as it is a slow-evolving protostome that has been shown to possess ancestral cell types , and thus may provide a better comparison with vertebrates than fast evolving species like Drosophila and nematodes where derived features can obscure evolutionary signal [17] , [18] . Wholemount in-Situ hybridization ( WiSH ) is an experimental technique where the practitioner uses labelled probes designed to be specific to a given mRNA to determine in which cells of the tissue under study that message is expressed . For a small organism like Platynereis , the staining can be applied to the whole animal and a 3-Dimensional representation of the expression pattern of a gene can be deduced using confocal microscopy to study the patterns of gene expression slice by slice . In practice , following the staining , imaging and alignment , the brain volume was partitioned into 32 , 203 3 voxels . The 3 volume was chosen to be slightly smaller than the average cell in Platynereis's brain but it is possible to consider this grid as a simple cellular model where each voxel roughly corresponds to a cell in the brain . Within each voxel , the light emission ( assumed to be correlated to the gene's expression level ) was measured ( Figure 2 ) . Theoretically , this luminescence data is quantitative but , on such a small scale , light contamination between voxels means that the quantitative measurements have to be interpreted with caution ( Figure 3 ) . Additionally , the light efficiency of probes can differ leading to a high experiment-to-experiment variability . Consequently , we binarized the dataset by setting the value of expression within a voxel to 1 or 0 , depending upon whether the gene was or was not expressed , respectively ( see Discussion ) . By repeating this process with different probes , expression patterns for 86 genes of interest were mapped . Importantly , due to the stereotypic nature of early Platynereis development [17] , the expression patterns can be overlaid , meaning that for each 3 voxel it is possible to determine which subset of the 86 genes is expressed . We can represent this information in a matrix of binary gene expression , where the location of each voxel , roughly representing a cell within the brain , is referenced in a 3D coordinate system . Given this coordinate system , we can create a neighbouring graph representation , where each node in the equivalent undirected graph corresponds to a voxel in the in-situ data . The edges of the graph were computed following a simple neighbouring system taking only the 6 closest neighbours , one in each direction of the 3D space . Markov random fields ( MRF ) are statistical models that provide a way of modeling entities composed of multiple discrete sites such as images where each site is a pixel or , in our case , a biological tissue where each site is a single voxel roughly corresponding to a cell , in a context-dependent manner [19] . MRF based methods find their roots in the field of statistical mechanics as the Ising model [14] and its generalization , the Potts model [15] . Since then , they have been and are still mainly used in the field of image analysis , and the literature about them is ever growing [20]–[22] . More specifically , MRF methods are found in a wide range of applications such as image restoration and segmentation [23] , surface reconstruction [24] , edge detection [25] , texture analysis [26] , optical flow [27] , active contours [28] , deformable templates [29] , data fusion [30] and perceptual grouping [31] . MRFs have also been used in a variety of biological applications from analysing medical imaging data [23] , [32] , [33] to analysing networks of genomic data [34] . Additionally , the Cellular Potts Model [35] has been used to model tissue development at a sub-cellular resolution . Mathematically , MRF models are built around two complementary sub-models . The field represents the sites and their spatial structure . The Hammersley-Clifford ( 1971 ) theorem states that the probability distribution of the Markov field can be represented as a Gibbs measure , which incorporates an energy function into which the spatial coherency parameters of the model are incorporated . Some critical choices in terms of the modeling framework are the structure of the neighbourhood system and the energy function . The emission model is used to describe the underlying data ( gene expression measurements in our case ) and it is necessary to make some assumptions about its form depending upon the underlying data . In our study the goal is to allocate the voxels described above into clusters , where is unknown , using the binarised matrix of gene expression measurements , . To incorporate spatial information into our clustering scheme , we assume that , the ( latent ) vector of length that describes the allocation of voxels to clusters , satisfies a first-order Markov Random Field ( MRF ) , where the probability that a voxel is allocated to a given state depends only upon the states of its immediate neighbours . Additionally , within cluster , we assume that the expression of gene follows a Bernoulli distribution with parameter ; we denote the full set of Bernoulli parameters using the matrix . In a typical MRF , the degree of spatial cohesion is determined by a single parameter , which is assumed to be constant for all clusters [36] , [37] . However , in the context of tissue organisation , it is reasonable to expect that the degree of spatial cohesion will differ between clusters; consequently , we estimate a separate value of for each of the clusters ( Methods ) . To estimate the parameters we use a fully-factorized variational expectation maximization ( EM ) approach in conjunction with mean-field approximations to infer intractable values [16] . To choose the optimal number of clusters , , we use the Bayesian Information Criterion ( BIC ) . Simulating data with a spatial component is a non-trivial problem . Existing methods rely on MCMC approaches as described in [38] . However , in our case with a relatively large number of nodes in the graph ( ) , this is computationally expensive . To overcome this problem , we exploited the fact that the Platynereis dataset already possesses a spatial structure , and use this as a synthetic example on which to base our simulations . As outlined in Figure 4 , we start by clustering the gene expression data using different values of and store the corresponding parameter estimates . Subsequently , the estimated Bernoulli parameters , , were used to simulate binarised gene expression data from clusters where , for each voxel contained within cluster , the expression of gene is simulated from a Bernoulli distribution with parameter ( Figure 4 ) . Next , each simulated voxel was assigned to the same spatial location as the corresponding voxel in the biological dataset . As a result , the simulated and the biological datasets have the same neighbouring graph . We can then cluster these simulated datasets using the method outlined above and determine how accurately we can estimate the parameters ( ) and choose the correct number of clusters , . The most important criterion for assessing the efficacy of our approach is the similarity between the inferred and true clusters . This also implicitly assesses the accuracy of the estimation of : if the inferred and true clusters are identical , the estimates of must be equal to the true values . In practice , we used the Jaccard coefficient to compare the inferred and the true clusters ( Methods ) , where a Jaccard coefficient of 1 implies perfect agreement . To benchmark our approach's performance , we also assessed the ability of two other models to cluster the simulated data: hierarchical clustering ( hClust ) , a very widely used approach in genomics and elsewhere , and an independent mixture model , which allows the relative improvement in performance added by the spatial component to be studied . Additionally , the likelihood function that needs to be maximised possesses many stationary points of different natures . Thus , convergence to the global maximum with the Expectation-maximisation algorithm ( see Methods section ) , depends strongly on the parameter initialisation . To overcome this problem , different initialisation strategies have been proposed and investigated ( see for instance [39]–[41] ) . Herein , we compare a random initialisation scheme with an initialisation based upon the solution obtained by applying hClust . The results of these experiments are shown in Figure 5 for . Our method , when used with a random initialization scheme ( Methods ) , has an average Jaccard coefficient of , and clearly demonstrates better performance than the other methods . The second best performing method is the independent mixture model with a random initialization , which has an average Jaccard coefficient of 0 . 7 . Since the independent mixture approach is equivalent to the MRF with all the parameters set equal to 0 ( i . e . , without a spatial component ) this suggests that accounting for the spatial aspect yields improved results . Given this , it is perhaps unsurprising that hClust also performs relatively poorly . Additionally , we note that initializing the MRF with the hClust output yields results that are superior to those generated by hClust but that are still poorer than either the randomly initialized independent mixture model or the MRF approach . This is likely explained by noting that , depending upon the initialization , the EM algorithm might converge to a local maximum . Consequently , for the rest of this study we use the random initialization strategy to initialize the EM algorithm . As well as directly comparing the clusters , we can also determine how accurately the parameters are estimated . To this end , in Figure 6 we compare the true and inferred mean values of for different values of . The values of increase with , which is to be expected since more clusters implies the existence of more transition areas , thus making an increase of necessary to maintain the optimal spatial coherency of the model . Figure 6 also shows a slight but consistent underestimation of . This can be explained by noting that the simulation scheme used may reduce the spatial coherency within clusters . Specifically , as illustrated in Figure 7 , clusters may not display homogeneous expression of a given gene: instead , depending upon the value of , a gene will be expressed only in a fraction of voxels . In reality , the voxels in which such genes are expressed may have a coherent spatial structure within the cluster that is lost in the simulation , thus explaining the consistently smaller values for that are estimated . To confirm this , we performed a second simulation using the parameter values estimated from the first simulation as a reference . In this context we did not expect any further loss of spatial coherency , which was indeed confirmed as shown by the blue curve in Figure 6 . To validate further our estimation of , we randomized the coordinates of the voxels to lose any spatial component before re-clustering the data . As expected , we observed that the estimates of were very close to for all clusters ( Figure 6 ) , as well as there being very similar Jaccard coefficient values ( relative to the true values ) for the independent mixture and the MRF model . Both of these observations provide confidence in our assertion that the spatial component plays an important role in the fit . Finally , we assessed the ability of the model to choose the correct number of clusters , . To do this , we noted the "true" number of clusters underlying the simulated data and compared this with the chosen value , . The results for two representative choices of are shown in Figure 8 and demonstrate that our clustering approach , in conjunction with the BIC , is able to accurately determine the optimal number of clusters . After validating our method using simulated data , we next studied the biological meaning of each of the clusters generated by applying the HMRF model to the real data . To do this , we combined each cluster's spatial location with its corresponding expression parameter . The latter parameter allows a stereotypical expression "fingerprint" to be associated with every cluster . In practice , not all of the 86 genes will provide insight into the biological function of a given cluster . For instance , in the case of a ubiquitously expressed gene , , the value of will be high for all clusters . To overcome this problem , we developed a score , , for each gene , and each cluster , where: For each gene , , and cluster , , is large if gene is specific to cluster . Consequently , the top scoring 3 or 4 genes for each cluster will represent a specific stereotypical expression pattern that will help us infer or confirm the identity of the functional tissue represented by each cluster . To provide confidence in our approach , we first considered well characterised regions within the Platynereis brain . Arguably the best-studied regions of the brain in Platynereis are the eyes: the brain has 4 eyes , two larval and two adult , and their locations and expression fingerprints are well known . As shown in Figure 9A , our approach generates two spatially coherent clusters that correspond to each of these regions . Importantly , the genes that best characterise these clusters are biologically meaningful: rOpsin and rOpsin3 , both members of the well-described opsin family of photosensitive molecules [42] , [43] , best distinguish the adult eye and larval eyes respectively , consistent with the in-situ data images shown in Figure 10 . As well as the eyes , a second region of the Platynereis brain , the mushroom bodies ( which corresponds to the pallium , layers of neurons that cover the upper surface of the cerebrum in vertebrates [3] ) , are also clearly identified by our approach ( Figure 9B ) . As well as identifying clusters corresponding to known cell types , we also identified clusters that might correspond to less well studied subtypes with specific biological functions . In Figure 11 , the green cluster defines a region on the basal side of the larvae that can be associated both by its localization and by its most representative genes ( MyoD [44] , [45] and LDB3 [46] , [47] ) with the starting point of the developing muscles of the adult animal . Indeed , MyoD has been shown to play a key role in the differentiation of muscles during development in vertebrates and invertebrates [44] , [45] and LDB3 codes for the protein LDB3 , which interacts with the myozenin gene family that has been implicated in muscle development in vertebrates [47] . Given the location of the eyes and the developing muscles , the location of the pink cluster in Figure 11 is interesting . This cluster surrounds the larval eyes , the adult eyes and reaches the hypothetically developing muscles described above . Looking at the most representative genes for this pink cluster , it is interesting to note the presence of Phox2 , a homeodomain protein that has been shown to be necessary for the generation of visceral motor-neurons ( neurons of the central nervous system that project their axons to directly or indirectly control muscles ) as described generally in [48] and in Drosophila [49] . The second most representative gene , COE , has also been shown to play a role in Platynereis and Drosophila neural tissue development [50] . In this context , although we lack biological validation , we can hypothesise that the cells within this particular cluster could be developing neurons that link the eyes to the muscles of Platynereis . Although this hypothesis remains purely speculative and would need validation in the laboratory , we believe this example is an interesting proof-of-concept that our clustering method can prove useful for hypothesis generation . Indeed , the analysis of the parameter values and the spatial localization attached to the clusters has allowed us to place with a reasonable level of confidence a functional hypothesis about a tissue that was not clearly defined either spatially or functionally . It is also interesting to note that hClust does not separate either putative region when clustering the same data with the same number of clusters . When we used an independent mixture model approach ( i . e . , with no spatial component ) to cluster the data the results were more comparable to those obtained when using the HMRF strategy . However , as can be observed when comparing Figures 12 and 11 , the clusters generated via the independent EM approach are considerably noisier and , as expected , less spatially coherent than those generated by the HMRF model . Further , for the developing muscle region , this noise is linked to biological imprecisions . When compared to in situ data generated by Fischer et al . [17] , who used a phalloidin in situ stain to investigate the location of the muscles at this developmental stage , it can be observed that the muscles are restricted to regions located away from the axes of symmetry , more consistent with the HMRF clustering output . Similarly , the independent mixture model method associates to the hypothesized region of developing neurons around the eyes , some ventral areas that seem unlikely to belong to the same sub tissue . Consequently , it seems likely that the HMRF not only performs better than the independent mixture model on simulated data but also better reflects the underlying biology .
As shown in Figure 3 , we overcame problems linked to light contamination by binarizing the "quantitative" luminiscence information . To do this , it is necessary to specify a threshold above which a gene is considered expressed . Ideally the same threshold would be applied to all genes — however , when we examined the density plots of light intensities for each gene we observed significant differences that rendered such an approach impossible . Specifically , for some genes , the density of intensities clearly separated the voxels into two groups , corresponding to those where the gene is expressed and unexpressed , respectively ( Figure 13 ( left ) ) . For the remaining genes , however , the density plot was diffuse , with no clear separation of the voxels into expressed and unexpressed clusters ( Figure 13 ( right ) ) . Consequently , we binarized each gene manually by choosing an optimal threshold based upon inspection of the raw fluorescent microscopy images . This is possible since the number of genes under study is relatively small . However , as the number of genes for which data is available increases ( as will be the case , for example , with single-cell RNA-sequencing studies ) , an automated method , perhaps based upon mixtures of Gaussians in the context of the WiSH data , will be required . Importantly , if the noise level in single cell expression datasets decreases to the extent where we can safely consider the results as quantitative , our method can easily be transformed to take this feature into account . The general outline of the model will stay exactly the same , the change will occur in the emission distribution . Instead of representing a Bernoulli parameter for each gene and each cluster , each could instead represent the parameter of a Poisson distribution . In our model we assume that , conditional upon the allocation of a voxels to a cluster , the gene expression levels can be described by independent Bernoulli distributions . This is a reasonable assumption in the context of the 86 genes chosen by Tomer et al . [3] , since they were selected to have largely orthogonal expression profiles . In other words , they were chosen since they were known to correspond to distinct and potentially interesting regions of the Platynereis brain . However , in many other settings a larger number of genes , many with correlated expression profiles ( i . e . , genes in the same regulatory network ) will be profiled and this assumption will be invalid . Consequently , extending the model to allow for dependence structure in the emission distributions will be a critical challenge . Additionally , as the number of genes increases , our approach for choosing the most specific genes will become less practical . Instead , entropy based approaches , such as the Kullback-Leibler divergence , might be more suitable . In summary , we have illustrated , using both simulations and real data , that accounting for spatial information significantly improves our ability to cluster voxels roughly representing brain cells into coherent and biologically relevant sets . While our approach converges very quickly ( on the order of minutes ) for the motivating dataset described herein , as the volume of data increases ( i . e . , by assaying the expression levels of thousands of genes in each cell using single-cell RNA-sequencing ) it will be important to carefully investigate how easily our model scales . Nevertheless , we anticipate that our method will play an important part in facilitating interpretation of single-cell resolution data , which will be an increasingly important challenge over the next few years .
To select the optimal number of clusters we used the BIC [45] , which finds the optimal number of clusters , , by selecting the value of that minimises its value . However , due to the symmetry of the brain we used a slightly different approach . As shown in Figure 14 ( blue dots ) , the BIC does not reach a clear minimum when applied to all voxels in the brain but instead reaches a plateau after a given number of clusters . This is most likely due to the highly , but not perfectly symmetrical nature of the brain: with a small , the same "tissue" on both the left and the right hand side of the brain will belong to the same cluster . However , because the two sides of the brain are not perfectly symmetrical , as increases the left and right part of the same "tissue" will be clustered separately . As a result , the likelihood continues to increase sufficiently to explain the flattened BIC curve . Moreover , this hypothesis seems to be confirmed by the fact that when computing the BIC on the right and left side of the brain separately , the curve has in both cases a clear minimum as shown in Figure 14 ( red and green dots ) . Given this , we opted to choose as the point where the BIC curve reaches a plateau . The data are available as a binarized datset of single cell gene expression data for the 86 genes in the brain of Platynereis dumerilii . An implementation of the EM algorithm in the C programming language is also available on the Github page of the project [52] . | Tissues within complex multi-cellular organisms have historically been defined in terms of their anatomy and function . More recently , experimental approaches have shown that different tissues express distinct batteries of genes , thus providing an additional metric for characterising them . These experiments have been performed at the whole tissue level , with gene expression measurements being "averaged" over millions of cells within a tissue . However , it is becoming apparent that even within putatively homogeneous tissues there exists significant variation in gene expression levels between cells , suggesting that additional cell subtypes , defined by distinct expression profiles , might be obscured by "bulk" experimental approaches . Herein , we develop a computational approach , based upon Markov Random Field models , for clustering cells into cell types by exploiting their gene expression profiles and location within the tissue under study . We demonstrate the efficacy of our approach using simulations , before applying it to identify known and putatively novel cell types within the brain of the ragworm , Platynereis dumerilii , an important model for understanding how the Bilaterian brain evolved . | [
"Abstract",
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"Methods"
] | [
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] | 2014 | Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets |
Most transcriptional activity of exponentially growing cells is carried out by the RNA Polymerase I ( Pol I ) , which produces a ribosomal RNA ( rRNA ) precursor . In budding yeast , Pol I is a multimeric enzyme with 14 subunits . Among them , Rpa49 forms with Rpa34 a Pol I-specific heterodimer ( homologous to PAF53/CAST heterodimer in human Pol I ) , which might be responsible for the specific functions of the Pol I . Previous studies provided insight in the involvement of Rpa49 in initiation , elongation , docking and releasing of Rrn3 , an essential Pol I transcription factor . Here , we took advantage of the spontaneous occurrence of extragenic suppressors of the growth defect of the rpa49 null mutant to better understand the activity of Pol I . Combining genetic approaches , biochemical analysis of rRNA synthesis and investigation of the transcription rate at the individual gene scale , we characterized mutated residues of the Pol I as novel extragenic suppressors of the growth defect caused by the absence of Rpa49 . When mapped on the Pol I structure , most of these mutations cluster within the jaw-lobe module , at an interface formed by the lobe in Rpa135 and the jaw made up of regions of Rpa190 and Rpa12 . In vivo , the suppressor allele RPA135-F301S restores normal rRNA synthesis and increases Pol I density on rDNA genes when Rpa49 is absent . Growth of the Rpa135-F301S mutant is impaired when combined with exosome mutation rrp6Δ and it massively accumulates pre-rRNA . Moreover , Pol I bearing Rpa135-F301S is a hyper-active RNA polymerase in an in vitro tailed-template assay . We conclude that RNA polymerase I can be engineered to produce more rRNA in vivo and in vitro . We propose that the mutated area undergoes a conformational change that supports the DNA insertion into the cleft of the enzyme resulting in a super-active form of Pol I .
The nuclear genome of eukaryotic cells is transcribed by three RNA polymerases [1] . RNA polymerase II ( Pol II ) transcribes most of the genome and is responsible for all messenger RNA production . RNA polymerases III and I are specialized in the synthesis of a limited number of transcripts . RNA polymerase III ( Pol III ) produces small structured RNAs , including tRNAs and the 5S ribosomal RNA . RNA polymerase I ( Pol I ) produces a single transcript , the large polycistronic precursor ( 35S pre-rRNA in yeast; 47S pre-RNA in human ) , which constitutes the first step of ribosome biogenesis . Pre-rRNA is then processed by multiple successive steps into the mature rRNAs ( 25S , 18S , and 5 . 8S in yeast; 28S , 18S and 5 . 8S in human ) . Despite producing a single transcript , Pol I is by far the most active eukaryotic RNA polymerase , responsible for up to 60% of the total transcriptional activity in exponentially growing cells [2] . The strongly transcribed rRNA genes can be visualized using the DNA spread method developed by Miller et al , 1969 , in which rRNA genes ( rDNA ) exhibit a “Christmas tree” configuration , with up to 120 polymerases per transcribed gene [3] . The full subunit composition and structural data are now available for the three nuclear RNA polymerases of the budding yeast Saccharomyces cerevisiae [4 , 5][6 , 7] . Pol I contains a core of shared or homologous subunits that are largely conserved in eukaryotes and archaea , as for the other two nuclear RNA polymerases [8] . The two largest subunits ( Rpa190 and Rpa135 ) form the DNA-binding cleft that carries the catalytic center . Rpb5 , Rpb6 , Rpb8 , Rpb10 , and Rpb12 are shared with Pol II and Pol III , whereas Rpc40 and Rpc19 are only shared with Pol III . This nine-subunit core is associated with the stalk , a structure formed in Pol I by the heterodimeric complex Rpa43/Rpa14 , which is involved in docking the essential Rrn3 initiation transcription factor to the enzyme [9–12] . The Pol I-Rrn3 complex interacts with promoter bound factors , the core factor ( CF ) , forming the initially transcribing complex ( ITC ) [13–16] . Additionally , Pol I and Pol III contain subunits that are functionally and structurally related to Pol II-specific basal transcription factors , called the "Built-in Transcription Factors" [17–19] . Their presence in Pol I and Pol III results in a higher number of subunits , from 12 subunits in Pol II , to 14 and 17 for Pol I and Pol III respectively , and correlates with substantial transcript production from a few genes [8] . The heterodimer formed by Rpa34 and the N-terminal domain of Rpa49 ( Rpa49Nt ) in Pol I ( equivalent to Rpc53 and Rpc37 in Pol III ) is related to the basal transcription factor TFIIF , and stimulates endogenous transcript cleavage activity [18 , 20 , 21] . Rpc34 in Pol III and the Rpa49 C-terminal domain ( Rpa49Ct ) bear a tandem winged helix domain similar to TFIIE , also named A49tWH [18 , 20] . Rpa49Ct binds upstream DNA [15 , 22] and is involved in initiation and elongation [18 , 23] . Finally , Rpa12 in Pol I and Rpc11 in Pol III harbour a C-terminal domain involved in stimulating endogenous transcript cleavage activity , similar to that of TFIIS for Pol II [17 , 24] . Yeast genetic studies of Pol III and Pol I “Built-in Transcription Factors” have revealed striking differences , despite their clear similarities . Each Pol III subunit is essential , but none of the Pol I “Built-in Transcription Factors" is required for cell growth . Lack of Rpa34 or invalidation of Rpa49Nt , by removing the TFIIF-like heterodimer , has no growth effect in vivo [18 , 25 , 26] . In contrast , full or C-terminal deletion of RPA49 leads to a strong growth defect at all temperatures , which is more severe below 25°C [18 , 26 , 27] . Full deletion of RPA12 leads to a strong growth defect at 25 and 30°C , and is lethal at higher temperatures [28] . Lack of the C-terminal extension of Rpa12 abolishes stimulation of intrinsic cleavage , without any detectable growth defect [17 , 24] . Finally , yeast strains carrying the triple deletion of RPA49 , RPA34 and RPA12 are viable , but accumulate the growth defects associated with each of the single mutants [25] . Pol I is functional in the absence of Rpa49 , but shows well-documented initiation and elongation defects , both in vivo and in vitro [23 , 26 , 27 , 29–31] . Restoration of active rRNA synthesis in the absence of Rpa49 , has been used to identify factors involved in initiation and elongation , such as Hmo1 and Spt5 [26 , 30 , 32] . Here , we made use of the spontaneous occurrence of extragenic suppressors of the growth defect of the rpa49 null mutant [27] to better understand the activity of Pol I . We showed that the suppressing phenotype was caused by specific point mutations in the two largest Pol I subunits , Rpa190 and Rpa135 . We identified a small area around phenylalanine in position 301 in subunit Rpa135 , at an interface formed by the lobe in Rpa135 and the jaw made up of regions of Rpa190 and Rpa12 , where most mutations cluster . Characterizing the Rpa135-F301S allele , we showed in an in vitro assay , that such Pol I mutant is more active than the wild-type enzyme . In vivo , overproduction of rRNA by Pol I bearing the Rpa135-F301S mutation was observed in backgrounds where the nuclear exosome activity is impaired by RRP6 deletion .
We characterized extragenic suppressors of the RPA49 deletion to better understand how cell growth is restored in the absence of Rpa49 . RPA49 full-deletion mutants show a strong growth defect at 30°C and are unable to grow at 25°C . However , spontaneous suppressors have been previously observed [27] . We quantified the frequency of occurrence of individual clones able to grow at 25°C . There was a low frequency of colony occurrence , comparable with the spontaneous mutation rate of a single control gene ( CAN1; < 5 . 10−6 ) . We isolated more suppressors after irradiating the cells with UV light . UV irradiation , resulting in a survival rate of about 50% , increased the frequency of suppressor mutations by approximately 10-fold . We identified clones that grew at 25°C after three days and selected individual colonies , called SGR for Suppressor of Growth Defect of RPA49 deletion , with various growth rates . We ranked SGR from 1 to 186 based on their growth rates at 25°C; SGR1 had a growth rate comparable to the wild-type ( WT ) condition ( Fig 1A ) . We crossed the 186 SGR clones with a strain of the opposite mating type bearing the deletion of RPA49 to obtain diploid cells homozygous for the RPA49 deletion and heterozygous for each suppressor . The restoration of growth of the diploids at 25°C showed that all suppressor phenotypes obtained were fully or partially dominant . We focused on the most efficient suppressor clones , SGR1 and SGR2 , and performed tetrad analysis to follow segregation of the observed suppression phenotype . Each suppressor phenotype was linked to a single locus in the genome and SGR mutants had no strong growth defect ( SGR1 in Fig 1A ) . We used global genomic mapping of SGR1 and SGR2 , derived from "genetic interaction mapping" ( GIM ) methods [33] ( Materials and Methods; S1 Fig ) , and found a genomic linkage with genes encoding the two largest Pol I subunits: RPA135 for SGR1 and RPA190 for SGR2 ( S1 Fig ) . Sequencing of the genomic DNA revealed that SGR1 bears a double mutation , whereas SGR2 bears a single one ( RPA135-I218T/R379K and RPA190-A1557V alleles , respectively ) . Furthermore , we identified an additional mutant , SGR3 , in RPA135 ( RPA135-R305L ) . The heterogeneity of the growth induced by strong UV mutagenesis prevented suppressor cloning from the 183 other SGR clones . We next used the dominant phenotype of these suppressors to isolate more alleles of RPA190 and RPA135 , which suppress the deletion phenotype of RPA49 . We constructed a library of randomly generated mutants ( see Materials and Methods ) by propagating plasmids bearing WT RPA135 or RPA190 in a mutagenic E . coli strain . After phenotypic selection of rpa49Δ mutants bearing a mutagenized Rpa190 or Rpa135 subunit at 25°C , each plasmid bearing a suppressor allele was extracted , sequenced , and re-transformed into yeast to confirm the suppressor phenotype . We thus isolated nine novel alleles of RPA190 and thirteen of RPA135 that were able to restore growth of rpa49 deletion mutant at 25°C ( S1 Table ) . We evaluated the suppression strength based on growth restoration relative to WT at 25°C , as for the SGR strains . Suppressor alleles obtained by mutagenesis of RPA190 or RPA135 , more effective than SGR1 , 2 , or 3 were identified ( S1 Table ) . In conclusion , we identified 22 novel alleles of genes coding for the two largest Pol I subunits as extragenic suppressors of the rpa49Δ-associated growth defect . The growth of the strains bearing one of six suppressor alleles ( RPA190-E1274K , RPA190-C1493R , RPA190-L1262P , RPA135-R379G , RPA135-Y252H , and RPA135-F301S ) was evaluated by a 10-fold dilution test ( Fig 1B ) , showing significant suppression by all in the absence of Rpa49 . In previous genetic studies , other genetic backgrounds that alleviate the growth defect of rpa49Δ at 25°C were isolated: rpa43-35 , 326 [26] , decreased rDNA copy number [29] , Hmo1 over-expression [30] , or Spt5 truncations [32] . For all these mutants , rRNA synthesis was only partially restored in the absence of Rpa49 and significant transcription defects remained . Here , we focused on the RPA135-F301S allele , the most effective growth suppressor of the RPA49 deletion: the rpa49Δ RPA135-F301S double mutant grew almost as well at 25°C as the WT strain ( Fig 1B ) . We sought further insight into the effect of the suppressors by integrating the RPA135-F301S point mutation into the endogenous gene in three genetic backgrounds: WT , rpa49Δ ( full deletion ) , or rpa49ΔCt . Note that yeast bearing rpa49ΔCt or rpa49Δ full-deletion have a similar growth defect , but have different Pol I subunits composition [26 , 27] . In the absence of Rpa49 , Rpa34 does not associate with transcribing Pol I while in strains bearing the rpa49ΔCt allele , Rpa34 and Rpa49Nt remain associated with the polymerase [26 , 27] . The growth rate was determined in each of these yeast strains at 30°C , in the presence or absence of RPA135-F301S . The suppressor allele RPA135-F301S had no effect on growth in the WT strain ( doubling time of 102 min ) . The doubling time was 180 min for the rpa49Δ strain and RPA135-F301S restored growth to a doubling time of 135 min . We observed similar suppression in the rpa49ΔCt background . Structural data are now available for Pol I in an inactive form [4 , 5] , in complex with Rrn3 [12 , 23 , 34] , associated with other initiation factors [13–15] , in elongating forms [22 , 35] , and in the paused state [36] ( Fig 2A ) . We mapped Rpa135 and Rpa190 residues that suppress the growth defect of the mutant strain rpa49Δ onto the structure of WT Pol I in which the full structure of Rpa49 was determined [15] ( Fig 2B ) . Most of the suppressor mutations , which provided growth recovery ( S1 Table ) , appeared to be clustered at a specific interface between the two largest subunits , Rpa190 and Rpa135 ( Fig 2B ) , between the lobe ( Rpa135—salmon ) and the jaw ( Rpa190—blue ) . In RPA135 , we found five suppressor mutations which modify a small region of 60 residues within the lobe domain ( S2 Fig ) . Note that in this region , three amino acids "DSF" ( D299 , S300 , F301 ) , which are conserved among eukaryotic species , are all mutated in suppressors ( S2 Fig ) . Substituted residues likely result in destabilization of this interface , suggesting a specific rearrangement of the interface lobe/jaw in each mutant . The jaw is also characterized by the presence of a β-strand in the structure of Rpa12 ( Rpa12—yellow: residues 46–51 , Fig 2B ) that , along with four β-strands in Rpa190 , forms a five-stranded anti-parallel β-sheet . The Rpa12 β-strand faces the Rpa135 lobe domain ( residue 252 to 315 of Rpa135—salmon ) , in which six independent mutations were found , including RPA135-F301S . To evaluate the implication of Rpa12 in suppression , we then tested whether mutated alleles of RPA12 could behave as suppressors . We generated a library of randomly mutagenized RPA12 , and screened for RPA12 alleles able to correct rpa49Δ growth defect at 25°C . Two dominant alleles ( RPA12-S6L and RPA12-T49A ) indeed efficiently suppressed the growth defect of rpa49Δ and of rpa49ΔCt to a similar extend ( Fig 2D , just shown for rpa49Δ ) . RPA12-S6L and RPA12-T49A obtained by random mutagenesis are specifically located in the "hotspot" at the jaw/lobe interface . Threonine 49 of Rpa12 is located on the β-strand ( Fig 2C ) , facing residues D299 , S300 , and F301 of Rpa135 , and Rpa190-E1274 ( Fig 2B ) . The second mutation , RPA12-S6L , is located in the N-terminal domain of Rpa12 ( Fig 2C ) . In conclusion , all point mutations in Rpa190 , Rpa135 , and Rpa12 detected in the hotspot domain of the jaw/lobe interface alleviate the need for RPA49 in vivo . We used yeast mutant cells with a low ( about 25 copies , +/- 3 copies ) and stabilized ( fob1Δ ) number of rDNA repeats to better associate the growth phenotype of RPA135-F301S or RPA12-S6L allele to rRNA synthesis activity and Pol I density on transcribed genes in vivo . This genetic background is the best suited to study variations in the number of polymerase molecules per rRNA gene because it has a low number of rDNA copies , almost all in the active state with a very high Pol I loading rate [29 , 37 , 38] . We generated five strains in this low copy background ( bearing single mutations; rpa49ΔCt , RPA135-F301S , RPA12-S6L and double mutants combining rpa49ΔCt with RPA135-F301S or RPA12-S6L alleles ) and determined their doubling time ( Fig 3A ) and de novo synthesis of rRNA ( Fig 3B ) . Note that the rDNA copies number was similar between strains , as indicated by chromosome XII size in pulse-field gel electrophoresis ( S3 Fig ) . The presence of the RPA135-F301S allele in Pol I effectively compensated the growth defect caused by the absence of C-terminal part of Rpa49 in this background . Labelling of the nascent rRNA was performed using a 2-min pulse with 3H adenine . We performed the labelling in three independent cultures because of heterogeneity due to random occurrence of suppressors in cell cultures of the rpa49ΔCt mutant . When compared to a WT strain , RNA precursors synthesis was reduced approximately five-fold for rpa49ΔCter , at 30°C ( compare Fig 3B , lane 1 to lanes 2 to 4 ) . Pol I activity in the presence of RPA135-F301S , with or without Rpa49Ct , was similar to that of the WT enzyme ( Fig 3B , lane 1 , 5 and 6 ) . Similarly , RPA12-S6L partially restored Pol I activity in the absence of Rpa49Ct ( compare Fig 3B , lanes 1 , 7 and 8 ) . Thus , RPA135-F301S and RPA12-S6L appeared to largely restore rRNA production in the absence of C-terminal part of Rpa49 . To get insight in Pol I activity in suppressor strains , we combined the rRNA synthesis quantification with the analysis of the Pol I distribution along the rRNA genes . We evaluated Pol I density on transcribed genes by performing Miller’s spreads , the only technique that currently allows the counting of individual Pol I molecules on single rRNA genes [3 , 29] . Using Miller spreads , we previously showed that full deletion of rpa49 resulted in a three-fold decrease of Pol I density per gene [3 , 29] . We show here that strain expressing the rpa49ΔCt allele resulted in a four-fold decrease of Pol I density per gene , with about 21 Pol I detected per gene , as compared to about 91 detectable in WT condition ( Fig 2C ) . Expression of the RPA135-F301S , WT for RPA49 , had no detectable influence on Pol I density ( Fig 3C , RPA135-F301S ) . In contrast to strain rpa49ΔCt , double mutant rpa49ΔCt RPA135-F301S or rpa49ΔCt RPA12-S6L showed significantly higher Pol I occupancy ( 46 and 43 respectively instead of 21 Pol I molecules per gene ) . Using ChIP , we reproduced the result showing that Pol I occupancy in absence of Rpa49Ct is drastically reduced ( Fig 3D; [26] ) . This experiment confirmed Miller’s spread quantification , in which RPA135-F301S significantly increased Pol I occupancy in absence of Rpa49Ct , although not to WT level ( Fig 3D ) . Overall , these results show that the presence of the RPA135-F301S , or to a lesser extend RPA12-S6L allele , in a strain lacking C-terminal part of Rpa49 restores rRNA synthesis to WT levels . However , Pol I density on rRNA genes is only partly restored , indicative of an improved transcription initiation , or increased stability of elongating Pol I in absence of Rpa49Ct . Extensive genetic characterization of Pol I subunits together with recent structural analysis have provided insight in their involvement in catalytic steps ( initiation , pause release or termination ) . To investigate suppression mechanism , we then decided to explore which domains or subunits of Pol I are required for the suppression to occur . We tested deletion of RPA190 alleles ( rpa190Δloop ) coding for Rpa190 lacking a specific domain . The structure of Rpa190 revealed the presence of an extended loop inside the DNA-binding cleft folded in an "expander/DNA mimicking loop" conformation when Pol I is in an inactive , dimeric form [4 , 5] . This loop is inserted in the jaw domain of Rpa190 ( Fig 2B ) , in the vicinity of the mutation hotspot . A small deletion of this Rpa190 domain ( 1361–1390 ) resulted in a slight slow-growth phenotype [4] . We generated a larger deletion allele , rpa190Δloop ( deletion of residues 1342–1411 of Rpa190 ) , and observed no associated growth defect ( Fig 4A ) . We were unable to generate a viable double mutant when combining this mutation with the rpa49 full deletion . Thus , the DNA-mimicking loop is required for Pol I activity in the absence of Rpa49 . We next tested whether deletion of this loop influences the suppression by the RPA135-F301S allele . Note that the rpa190Δloop combined with RPA135-F301S had no growth phenotype . There was no difference in the growth of the rpa49Δ RPA135-F301S double mutant and that of the triple mutant rpa49Δ RPA135-F301S rpa190Δloop ( Fig 4A ) . Thus , the expander/DNA mimicking loop of Rpa190 is not required for suppression , but is required for the viability of the rpa49 deletion mutant . Rpa34 forms a heterodimer with Rpa49Nt , and Rpa14 is essential in absence of Rpa49 . We then introduced RPA135-F301S in yeast strains lacking either Rpa34 or Rpa14 ( Fig 4B and 4C ) . Growth of RPA135-F301S/rpa34Δ and RPA135-F301S/rpa14Δ double mutants were not different from that of the single mutants . However , RPA135-F301S suppressed the growth defect of the viable double mutant , rpa34Δ rpa49Δ ( Fig 4B ) . The double deletion mutant lacking both Rpa49 and Rpa14 was not viable [25] . Introduction of the suppressor RPA135-F301S , by genetic crossing , resulted in a triple mutant ( rpa14Δ rpa49Δ RPA135-F301S ) that could grow , but slower than WT ( Fig 4C ) . We conclude that RPA135-F301S does not require Rpa14 or Rpa34 for the suppression to occur . We next evaluated which part of Rpa12 subunit was required for the suppression to occur ( Fig 5 ) . First , we evaluated growth when rpa12 alleles were expressed in combination with rpa49 deletion . The C-terminal region of Rpa12 ( TFIIS-like ) is inserted towards the active center of Pol I to stimulate intrinsic cleavage activity but is displaced during productive initiation and elongation steps . C-terminal deletion of Rpa12 resulted in normal growth [24] ( Fig 5A- lane 2 ) , although the rpa12ΔCt allele is unable to stimulate cleavage activity in vitro [17] . Full deletion of RPA12 led to a heterogeneous growth phenotype when propagated at 30°C . To overcome this heterogeneity , we constructed a strain with RPA12 under the control of the regulatable pGAL promoter . Depletion of Rpa12 on glucose containing medium , like full RPA12 deletion , resulted in a slight growth defect at 25°C , which was stronger at 30°C [28] ( Fig 5A-lane 3 ) . In contrast , RPA49 deletion resulted in a growth defect at 30°C , which was stronger at 25°C [27] ( Fig 5A-lane 4 ) . Combining rpa12ΔCt with rpa49Δ resulted in a mild synergistic phenotype , with a stronger growth defect at both 25°C and 30°C ( Fig 5A—lane 5 ) . The double mutant lacking both full Rpa12 and Rpa49 subunits was viable but had a major growth defect [25] ( Fig 5A—lane 6 ) . Secondly , in double rpa12/rpa49 mutants , we tested the expression of the suppressors alleles , which were isolated ( RPA12-S6L , RPA12-T49A or RPA135-F301S ) . We explored whether the C-terminal extension of Rpa12 was necessary for suppression of the rpa49Δ phenotype . We introduced the Rpa12 C-terminal truncation into the strain bearing both rpa49Δ and suppressor allele RPA12-S6L . RPA12-S6L resulted in an efficient suppression of the rpa49Δ growth defect ( Fig 5B ) . We then introduced the RPA135-F301S allele in strains lacking Rpa49 with or without the entire Rpa12 subunit and assessed the suppression phenotype at 25°C ( Fig 5C ) . The growth defect of rpa49Δ was completely suppressed by the RPA135-F301S allele when Rpa12 was expressed ( Fig 5C , left panel ) , whereas suppression mediated by RPA135-F301S was not detected in the absence of Rpa12 ( Fig 5C , middle panel ) . After 10 days , rpa49Δ rpa12Δ double mutant behaved exactly the same with or without the RPA135-F301S allele , demonstrating that suppressor allele has no effect in absence of Rpa12 ( Fig 5C , right panel ) . In conclusion , we show that RPA135-F301S suppression of rpa49Δ-associated growth defect does not require Rpa190 DNA mimicking loop , Rpa34 , or Rpa14 . Rpa12 C-terminal portion involved in stimulating cleavage activity is also not required for suppression . In contrast , Rpa12 N-terminal domain is required for the suppression to occur . In vitro , C-terminal part of Rpa49 is essential in promoter-dependent transcription assay [23] . Our in vivo analysis suggests that in RPA135-F301S mutant background , C-terminal part of Rpa49 is not required for rRNA synthesis . Our hypothesis is that Rpa135-F301S partly compensates the requirement for C-terminal part of Rpa49 in initiation . We used the promoter-dependent in vitro transcription system and tailed-template system to assess this hypothesis ( Fig 6A and 6B ) . Results were strikingly different when using promoter-dependent or tailed-template systems . After depletion of Rpa49 , purified Pol I lacks both Rpa34 and Rpa49 subunits and is the so-called Pol A* complex [23 , 25 , 31] . Pol I lacking subunits Rpa49/Rpa34 ( Pol A* , Fig 6A lane 2 ) was almost inactive in promoter-dependent assay when compared to wild-type Pol I ( WT ) or Pol I bearing Rpa135-F301S ( Fig 6A , lane 1 and 3 ) . Note that addition of recombinant Rpa34/Rpa49 , Rpa49Ct alone or Rpa34/Rpa49N-ter stimulated transcription by Pol A* [23]; similarly , recombinant Rpa34/Rpa49 stimulated transcription of Pol A* bearing Rpa135-F301S ( S4 Fig ) . Ruling out our hypothesis , Pol I bearing Rpa135-F301S did not restore promoter-dependent activity of RNA Pol I lacking Rpa49 ( Fig 6A lane 4 ) . We next tested RNA synthesis in a tailed-template system . Tailed-template system being very sensitive to experimental conditions , RNA synthesis assay presented here was reproduced at least three time with various polymerase concentrations . Pol I lacking Rpa34/Rpa49 was partly deficient in tailed template assay ( Fig 6B , lane 2 ) [23] . Interestingly , in this promoter-independent assay , we observed that the RNA synthesis by the polymerase bearing Rpa135-F301S was increased compared to the one with the WT polymerase ( Fig 6B compare lane 1 to 3 ) . Moreover , Pol I bearing Rpa135-F301S fully restored tailed template production of RNA Pol I lacking Rpa49 ( Fig 6B , lane 4 ) . We conclude that in the in vitro promoter-dependent transcription assay , RPA135-F301S suppressor does not correct initiation defect due to the absence of Rpa49 . However , as suggested from promoter-independent assay , in presence of Rpa135-F301S a more efficient polymerase is engineered , able to produce more ribosomal RNAs from DNA tailed template . In vitro , Pol I bearing Rpa135-F301S is over-producing RNA compared to WT . However in vivo , we could not reveal increased production of rRNA ( 2 min pulse labelling , see Fig 3B ) . Pre-rRNAs which are not properly folded into pre-ribosome are targeted to degradation by the 3' to 5' exoribonucleolytic activity of the exosome [39] . We hypothesized that overproduced rRNAs in RPA135-F301S mutant background could be targeted by the nuclear exosome . Rrp6 , part of the nuclear exosome complex , was deleted in a WT strain and in a strain bearing RPA135-F301S mutation . We observed a strong synergistic growth defect in strain bearing both RPA135-F301S and the deletion of RRP6 ( S5 Fig ) . Northern blot analysis ( Fig 7A ) showed that accumulation in RPA135-F301S single mutant was indistinguishable from the WT for all RNA probed . rrp6Δ single mutant accumulates 23S and 35S ( pre- ) rRNA [40 , 41] . In correlation with its growth defect , we could observe a 2-fold increase in 35S and 23S ( pre- ) rRNA accumulation in the double mutant RPA135-F301S rrp6Δ as compared to rrp6Δ . Accumulation of 35S and 23S could indicate an increase of RNA production , or a defect in early maturation steps . We decided to directly assess over-expression of ( pre- ) rRNA using short in vivo labelling experiments ( 40 seconds ) . As previously reported with very short pulse labelling , accumulation of 20S rRNA is barely detectable , while 27SA and 35S are already accumulated [42] . We could detect a strong accumulation of newly synthesized ( pre- ) rRNA in the double mutant RPA135-F301S rrp6Δ compared to WT , RPA135-F301S or rrp6Δ strains ( Fig 7B ) . Note that increased background signal could indicate an accumulation of partially degraded , abortive transcripts or elongating rRNA transcript of various sizes . These results suggested that rRNA are over-expressed in strain bearing RPA135-F301S , but are quickly decayed by Rrp6 . We confirmed this observation by evaluating ongoing transcription independently of decay machinery using high-resolution transcriptional run-on ( TRO ) analysis ( Fig 7C ) . TRO measures occupancy of rRNA genes by actively-elongating polymerases . Indeed , TRO assay make use of 10% sarkosyl , which permeabilizes cell membranes , reversibly blocks elongating polymerases and inhibits RNAse activity [43–46] . Permeabilized cells are then incubated with [α32P]-UTP to resume transcription . Neosynthesized radiolabeled RNAs are extracted , and used to probe slot-blots loaded with single strand DNA fragments complementary to rDNA locus . Using incorporation of [α32P]-UTP in the 5S rRNA transcribed by RNA polymerase III as internal control , TRO revealed a three-fold increase of rRNA transcription in cells bearing RPA135-F301S allele when compared to WT control , irrespective of Rrp6 presence ( Fig 7C ) . These results confirmed that Pol I bearing Rpa135-F301S is over-producing RNA compared to WT and that over-produced RNAs are targeted for degradation by the exosome . All together , we concluded that Pol I bearing Rpa135-F301S is a hyper-active RNA polymerase in vivo .
Our previous studies suggested the specific involvement of Rpa49 in the association and dissociation of initiation factor Rrn3 from the Pol I stalk [26 , 29] ) . Here , we show that genetically modified polymerases lacking Rpa49 or Rpa49Ct , with a single modified residue in Rpa190 , Rpa135 , or Rpa12 , at a position diametrically opposed to the position that binds to Rrn3 , can initiate transcription and that strains harbouring them grow normally . Moreover , mutant Pol I with Rpa135-F301S does not restore promoter dependent activity in absence of Rpa49 . We propose that , independently of the important interplay between Rpa49 and Rrn3 during initiation , Rpa135-F301S can stimulate Pol I activity . The Rpa12 subunit is involved in stimulating the intrinsic cleavage activity of Pol I through a TFIIS-like domain at its C-terminus . Purified Pol I with Rpa12 lacking the C-terminal domain has no cleavage activity [17] . Furthermore , the C-terminal domain of Rpa12 can contact the active site of the polymerase in the inactive conformation and is retrieved in both initiation competent and elongating forms of the polymerase . However , the cleavage activity is re-activated when Pol I is paused [36] . Direct evidence that cleavage is not involved in suppression of the growth defect came from the experiments showing a fully functional suppressor phenotype for RPA12ΔCt-S6L which lacks the domain required for stimulating cleavage . The N-terminal domain of Rpa12 , at the surface of Pol I , is involved in the recruitment of the largest subunit , Rpa190 [24] and is required for docking this subunit to the enzyme . A linker region of Rpa12 connects its N-terminal module ( equivalent to the N-terminal domain in the Pol II subunit Rpb9 ) at the surface of Pol I to its mobile C-terminal region ( TFIIS-like ) and is therefore indirectly required for cleavage . In vitro , purified Pol I that lacks Rpa12 has less activity than WT Pol I in promoter-dependent transcription assays ( S6 Fig ) . Mutations in other Pol I domains , such as deletions in the Rpa190-DNA mimicking loop , Rpa34 or Rpa14 , did not influence suppression of the rpa49 deletion growth defect by the RPA135-F301S allele . In contrast , the Rpa12 N-terminal domain was absolutely required for efficient suppression . Accordingly , RPA135-F301S allele was unable to restore efficient growth when Rpa12 was absent . Thus , Rpa12 and RPA135-F301S likely cooperate in the super-active Pol I enzyme . Note that rearrangement of Rpa12 have recently be shown to correlate with dissociation of Rpa49/Rpa34 heterodimer from Pol I , confirming the tight interplay between those subunits [47] . Pol I undergoes major conformational changes during the transcription cycle , mainly affecting the width of the DNA-binding cleft [48] . During the initiation of transcription , the cleft aperture narrows from a semi-open configuration , as seen in cryo-EM structures of the enzyme bound to Rrn3 [12 , 23 , 34] , to a fully closed conformation observed in transcribing complexes [13 , 15] ( Fig 8 ) . This allows gripping of the transcription bubble inside the cleft ( Fig 8A and 8B ) . Following Rrn3 release , DNA binding is further secured , by the Rpa49-linker which crosses the cleft from the lobe to the clamp , passing over the downstream DNA , and by the Rpa49Ct , which binds the upstream DNA in the vicinity of the clamp [15 , 22] . Therefore , Rpa49Ct is anchored in a position securing cleft closure ( Fig 8B ) . Cleft closure is achieved by the relative movement of two structural units , located on opposite sides of the cleft that pivot with respect to each other using five hinges [4] . The unit consisting of the shelf and clamp modules appears to be rigid , whereas the unit comprising the core and lobe modules , which is in the vicinity of the mutated residues involved in suppression of the rpa49Δgrowth defect , undergoes internal rearrangements ( S1 Movie ) . The most prominent reorganization within this latter unit affects the Rpa190 jaw domain , the outer rim of which shifts away from the DNA by approximately 3 . 7 Å , using the lobe/jaw interface as a hinge ( Fig 8B; blue arrow ) . This movement also involves the linker region of Rpa12 , which contains a β-strand ( residues 46–50 ) that completes a four-stranded β-sheet in the Rpa190 jaw domain . As a result , a short α-helix within the Rpa12 linker region shifts by approximately 3 . 0 Å ( Fig 8B; yellow arrow ) . Rearrangements in the jaw made up of regions of Rpa190 and Rpa12 are likely essential to allow pivoting of the shelf-clamp unit against the core-lobe unit . Without such motion , cleft closure would be impossible ( S1 Movie ) . Structural analysis suggests that the C-terminal domain of Rpa49 and its linker domain are involved in securing the closed cleft conformation [15 , 22] . Cleft closure is likely destabilized in the rpa49Δ mutant ( Fig 8C ) . We propose that Rpa135-F301S or Rpa12-S6L favors DNA capture by increasing the flexibility of the lobe/jaw/Rpa12 interface of Pol I relative to that of the WT polymerase , facilitating cleft closure in the absence of Rpa49 ( Fig 8D , red arrow ) . As shown in vitro , mutant Pol I with Rpa135-F301S is super-active . In vivo , the increased accumulation of pre-rRNA , detectable in absence of the nuclear exosome ( rrp6Δ ) , is in agreement with the in vitro super-activity . We propose that Pol I bearing Rpa135-F301S might facilitate cleft closure , in addition to secure cleft closure by Rpa49 . Such mutant enzyme could capture DNA more efficiently than WT polymerase ( Fig 8E ) . Alternatively , the mutations could also favour a “closed cleft” conformation in the presence of DNA with little impact on flexibility . At this stage , a precise mechanistic understanding of how Pol I super-activity is achieved requires further characterization . Of note , increased rRNA produced by Pol I bearing Rpa135-F301S ( in a background with a WT copies number of rDNA ) , measured by TRO is not correlated to an increased Pol I loading rate on rRNA genes measured by Miller’s spreads and ChIP ( in a background with low copies number of rDNA ) . This paradox needs further clarification . Here , we show that Pol I can be engineered to synthesize more rRNA . A super-active mutant of Pol II was already characterized: point mutation in trigger loop ( Rpb1-E1103G ) leads to increase RNA polymerization rate , affecting pausing and transcriptional fidelity [49 , 50] . Despite a very similar active site organization , an analogous mutation in the Pol I trigger loop resulted in a very different outcome , as it reduces the elongation rate [51] . Such observations led to the conclusion that Pol I catalytic cycle is very different than the one of Pol II , with different rate limiting steps [51–53] . We show here that mutations away from the active center can lead to a super-active form of Pol I . This difference can stem from wide-open configuration of the DNA-binding cleft in Pol I compared to other polymerases . We propose that Pol I cleft opening and closure is a limiting step in the Pol I catalytic cycle .
The oligonucleotides used in this study are listed in S4 Table . Plasmids and details of the cloning steps are described in S3 Table . Randomly mutagenized RPA190 and RPA135 libraries were obtained by transformation and amplification of pVV190 and pNOY80 , respectively , into XL1-red strains according to the manufacturer’s guidelines ( XL1-Red Competent Cells , from Agilent Technologies ) . Yeast strains are listed in S2 Table , and were constructed by meiotic crossing and DNA transformation [54][55] . The yeast media and genetic techniques were described previously [56] . yCNOD226-1a was obtained from yCNOD223-2a by switching the KAN-MX to the NAT-MX marker under the control of the MF ( ALPHA ) 2/YGL089C promoter ( alphaNAT-MX4 ) , which allowed selection of MATα haploid cells [33] . OGT9-6a is an offspring of yCNOD226-1a crossed with BY4741 . OGT8-11a is an offspring of BY4742 crossed with Y1196 . Strains OGT9-6a and OGT8-11a were plated on rich media and UV irradiated ( 5W/m2 during 5 second ) , resulting in 50% survival . LH514D and LH11D are suppressor clones of the growth defect selected from UV-irradiated OGT9-6a grown at 25°C . AH29R is a suppressor clone of the growth defect selected from UV-irradiated OGT8-11a grown at 25°C . Genetic interaction mapping ( GIM ) analysis of the RPA49 deletion mutant was performed as described previously [33] . Microarray data were normalized using MATLAB ( MathWorks , Inc . , Natick , MA ) as previously described [29] . OGT15-7b is an offspring of LH514D with BY4741 , followed by homologous recombination using PCR-amplified fragments generated with oligos 1716 and 1717 and pCR4-HIS3 as template . yTD16-1a was first transformed with plasmid pCJPF4-GAL49-1 . Then , RPA135 was tagged by homologous recombination using PCR-amplified fragments generated with oligos 835 and 836 and genomic DNA of strain RPA135-TAP or yTD6-6c , generating yTD27-1 and yTD28-1a , respectively . Strain yTD25-1a bears a C-terminal deletion of RPA49 generated by homologous recombination using PCR-amplified fragments generated with oligos 208 and 1515 and pFA6-KAN-MX6 as template . yTD11-1a was derived from strain yTD25-1a after switching to HPH-MX by homologous recombination using pUC19-HPH cut by BamHI . C-terminal deletion of RPA49 in yTD29-1a and yTD30-1a were generated by homologous recombination using PCR-amplified fragments generated with oligos 649 and 650 and yTD11-1 genomic DNA as template , transformed into yTD27-1 and yTD28-1a , respectively . Genomic allelic insertion of RPA12-S6L in yTD31-1a and yTD23-1a was performed by homologous recombination using PCR-amplified fragments generated with oligos 1556 and 1557 and pRS316-A12-S6L-KAN as template , transformed into yTD27-1 and yTD29-1a , respectively . TGT135-3b was obtained after sporulation of y27138 transformed by pNOY80 . TGT135-3b and OGT15-9d were mated to generate TGT12 . yTD2-3b and yTD2-3d are offspring of TGT12 transformed with pGL135_33 . yTD6-6c and yTD6-6b were generated by homologous recombination using pTD2_6c_135TAP cut with XhoI-NsiI , transformed into yTD2-3b and yTD2-3d , respectively . yTD48-1a was generated by deletion of the Rpa190 DNA-mimicking loop using homologous recombination with PCR-amplified fragments generated using oligos 1189 and 1194 and genomic DNA of strain SCOC2260 as template , transformed into BY4741 . yTD51-2c , yTD51-8a , and yTD51-5a are offspring of yTD48-1a mated with yTD37-3a . yTD36-2b is an offspring of yCN224-1a mated with yTD40-1a . yTD37-3d and yTD37-7d are offspring of yCN224-1a mated with yTD41-1a . yTD38-3d is an offspring of yCN225-1a mated with yTD40-1a and yTD39-8a is an offspring of yCN225-1a mated with yTD41-1a . Strain yTD53-1a was constructed by homologous recombination using a PCR-amplified fragment generated with oligos 1634 and 1635 and pFA6a-KanMX6-GAL::3HA as template . OGT30-1a and OGT30-3a are offspring of yTD53-1a mated with yTD6-6b . yTD40-1a and yTD41-1a were generated by homologous recombination using PCR-amplified fragments generated with oligos 700 and 1679 and pFA6a-HA-KlURA3 as template , transformed into OGT30-3a and OGT30-1a respectively , switching RPA135-TAP-tag to untagged RPA135 . Strain yMKS8-1a was constructed by homologous recombination using a PCR-amplified fragment generated with oligos 1711 and 1713 and pFA6a-KanMX6-GAL::3HA as template , transformed into strain yCD2-2a . yMKS9-9d is offspring of yMKS8-1a mated with yTD6-6c . Extragenic suppressors allele SGR were mapped using GIM methods , in which a strain is crossed with the entire pool of haploid deletion strains [57] . Here , deletions are used to evaluate linkage to SGR locus . Due to the genetic suppression , Individual deletions genetically linked to SGR are counter-selected in rpa49Δ background . Genetic mapping of SGR locus is based on strong genetic linkage of counter-selected deletion alleles ( see S1 Fig ) , evaluated using micro-array of deletion bar-code ( ArrayExpress accession E-MTAB-7831 ) [58] . Metabolic labelling of pre-rRNA was performed as previously described [59] with the following modifications . Strains were pre-grown in synthetic glucose-containing medium lacking adenine at 30°C to an OD600 of 0 . 8 at . One-milliliter cultures were labeled with 50 μCi [8-3H] adenine ( NET06300 PerkinElmer ) for 2 min . Cells were collected by centrifugation and the pellets were frozen in liquid nitrogen . RNA was then extracted as previously described [60] and precipitated with ethanol . For high molecular weight RNA analysis , 20% of the RNA was glyoxal denatured and resolved on a 1 . 2% agarose gel . Low molecular weight RNAs were resolved on 8% polyacrylamide/8 . 3 M urea gels . Chromatin spreading was mainly performed as described previously with minor modifications [61] . Carbon-coated grids were rendered hydrophilic by glow discharge instead of ethanol treatment . Images were obtained using a JEOL JEM-1400 HC electron microscope ( 40 to 120 kV ) with an Orius camera ( 11Mpixels ) . The position of the RNA polymerase I molecules and the rDNA fiber were determined by visual inspection of micrographs using Image J ( http://rsb . info . nih . gov/ij/ ) . Digital images were processed by software programs Image J and Adobe Photoshop ( v . CS6 ) . In vitro promoter-dependent transcription reactions were performed as previously described [23 , 62] with some modifications . Briefly , 1 . 5 ml reaction tubes ( Sarstedt safety seal ) were placed on ice . Template ( 0 . 5–1 μl; 50–100 ng DNA ) was added , corresponding to a final concentration of 5–10 nM per transcription reaction ( 25-μl reaction volume ) . Core factor ( 1–2 μl; 0 . 5 to 1 pmol/μl; final concentration 20–40 nM ) and 1–3 μl Pol I ( final concentration 4–12 nM ) were added to each tube . Then , 20 mM HEPES/KOH pH 7 . 8 was added to a final volume of 12 . 5 μl . Transcription was started by adding 12 . 5 μl 2X transcription buffer . The samples were incubated at 24°C for 30 min at 400 rpm in a thermomixer . 25 nM of tailed templates were used in a total volume of 25 μl . The transcription was performed as described in promoter-dependent transcription reactions: Tailed templates are PCR-amplified fragments generated with oligos 1834 and 1835 and “pUC19tail_g-_601_elongated” as PCR-template , treated with Nb . BsmI ( NEB ) to generate a nick . Nicked site allows non-specific initiation of Pol I without the addition of initiation factors . Transcription was stopped by adding 200 μl Proteinase K buffer ( 0 . 5 mg/ml Proteinase K in 0 . 3 M NaCl , 10 mM Tris/HCl pH 7 . 5 , 5 mM EDTA , and 0 . 6% SDS ) to the supernatant . The samples were incubated at 30°C for 15 min at 400 rpm in a thermomixer . Ethanol ( 700 μl ) p . a . was added and the tubes mixed . Nucleic acids were precipitated at -20°C overnight or for 30 min at -80°C . The samples were centrifuged for 10 min at 12 , 000g and the supernatant removed . The precipitate was washed with 0 . 15 ml 70% ethanol . After centrifugation , the supernatant was removed and the pellets dried at 95°C for 2 min . RNA in the pellet was dissolved in 12 μl 80% formamide , 0 . 1 M TRIS-Borate-EDTA ( TBE ) , 0 . 02% bromophenol blue , and 0 . 02% xylene cyanol . Samples were heated for 2 min with vigorous shaking at 95°C and briefly centrifuged . After loading on a 6% polyacrylamide gel containing 7M urea and 1X TBE , RNAs were separated by applying 25 watts for 30–40 min . The gel was rinsed in water for 10 min and dried for 30 min at 80°C using a vacuum dryer . Radiolabelled transcripts were visualised using a PhosphoImager . RNA extractions and Northern hybridizations were performed as previously described [60] . For high molecular weight RNA analysis , 3μg of total RNA were glyoxal denatured , resolved on a 1 . 2% agarose gel and transferred to a nylon membrane . The sequences of oligonucleotides used to detect the RNA species: 35S rRNA , 25S rRNA , 20S rRNA , 18S rRNA , 27S rRNA , PGK1 mRNA and SCR1 ncRNA are respectively 774 , 1829 , 1833 , 892 , 1830 , 1831 and 1832 are reported in S4 Table . TRO was performed as previously described [39 , 43] . Slot blots were loaded with single-stranded 80-mers DNA oligonucleotides: 1855 ( NTS2 ) , 1857 ( 5’ETS ) , 1859 ( 18S . 2 ) , 1860 ( 25S . 1 ) , 1861 ( 3’ETS ) , 1862 ( NTS1 ) , 1863 ( 5S US ) and 1864 ( 5S DS ) . | The nuclear genome of eukaryotic cells is transcribed by three RNA polymerases . RNA polymerase I ( Pol I ) is a multimeric enzyme specialized in the synthesis of ribosomal RNA . Deregulation of the Pol I function is linked to the etiology of a broad range of human diseases . Understanding the Pol I activity and regulation represents therefore a major challenge . We chose the budding yeast Saccharomyces cerevisiae as a model , because Pol I transcription apparatus is genetically amenable in this organism . Analyses of phenotypic consequences of deletion/truncation of Pol I subunits-coding genes in yeast indeed provided insights into the activity and regulation of the enzyme . Here , we characterized mutations in Pol I that can alleviate the growth defect caused by the absence of Rpa49 , one of the subunits composing this multi-protein enzyme . We mapped these mutations on the Pol I structure and found that they all cluster in a well-described structural element , the jaw-lobe module . Combining genetic and biochemical approaches , we showed that Pol I bearing one of these mutations in the Rpa135 subunit is able to produce more ribosomal RNA in vivo and in vitro . We propose that this super-activity is explained by structural rearrangement of the Pol I jaw/lobe interface . | [
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"ribosomes"... | 2019 | Genetic analyses led to the discovery of a super-active mutant of the RNA polymerase I |
Tumour cells show a varying susceptibility to radiation damage as a function of the current cell cycle phase . While this sensitivity is averaged out in an unperturbed tumour due to unsynchronised cell cycle progression , external stimuli such as radiation or drug doses can induce a resynchronisation of the cell cycle and consequently induce a collective development of radiosensitivity in tumours . Although this effect has been regularly described in experiments it is currently not exploited in clinical practice and thus a large potential for optimisation is missed . We present an agent-based model for three-dimensional tumour spheroid growth which has been combined with an irradiation damage and kinetics model . We predict the dynamic response of the overall tumour radiosensitivity to delivered radiation doses and describe corresponding time windows of increased or decreased radiation sensitivity . The degree of cell cycle resynchronisation in response to radiation delivery was identified as a main determinant of the transient periods of low and high radiosensitivity enhancement . A range of selected clinical fractionation schemes is examined and new triggered schedules are tested which aim to maximise the effect of the radiation-induced sensitivity enhancement . We find that the cell cycle resynchronisation can yield a strong increase in therapy effectiveness , if employed correctly . While the individual timing of sensitive periods will depend on the exact cell and radiation types , enhancement is a universal effect which is present in every tumour and accordingly should be the target of experimental investigation . Experimental observables which can be assessed non-invasively and with high spatio-temporal resolution have to be connected to the radiosensitivity enhancement in order to allow for a possible tumour-specific design of highly efficient treatment schedules based on induced cell cycle synchronisation .
Tumours are complex dynamic objects which can adapt to changes in their environmental conditions and accordingly react to treatments such as radiotherapy . Withers was one of the first to note that the now common scheduling of radiotherapy in fractions is efficient , because it exploits these dynamic intra-tumoural effects . He identified and described the four “R”s of radiotherapy which today form the basis of clinical practice: redistribution , re-oxygenation , repair and regrowth . After the use of fractionation schemes became common in clinical treatment , further investigation led to the conclusion that standardised protocols might not be the optimal solution for each patient , but rather that altered individual fractionation schemes should be considered [1] . In particular the cell cycle redistribution during radiotherapy has been studied early [2] , [3] and regularly ever since in a variety of experimental systems [4] . Nevertheless , today cell cycle effects are not routinely included in treatment planning and are disregarded as “unusable” even though the advent of modern imaging technologies has delivered a variety of suitable tools which could assess not only oxygenation but also cell cycle status in vivo [5] , [6] . Cancer therapy is clearly advancing in the direction of highly individualised , tailored treatment protocols as a result of a range of new technological developments in radiation delivery [7] and monitoring [8] , [9] . In order to find optimal protocols , a detailed understanding of the treatment effects on the target system is necessary . This is where mathematical and computational models are needed in order to describe and understand the complex interdependencies of the tumour . They open up the possibility to also screen unusual treatment approaches for efficient strategies . Accordingly , over the last decade , a variety of models have been designed for the purpose of treatment planning , be it for radiotherapy [10] , [11] , chemotherapy [12] , combined treatment approaches or others aspects of tumour growth and therapy [13]–[15] . One particularly successful example of therapy optimisation is the description and use of circadian timings in cancer therapy [16] , [17] . Especially for chemotherapy the careful timing of drug delivery in conjunction with the natural cell cycle dynamics has led to interesting predictions [15] , [18] , [19] and an measurable increase in clinical efficiency both in cancer-therapy and in the treatment of non-cancer diseases [20]–[22] . Also with respect to DNA repair and gene expression , circadian cell cycle timings are of interest for cancer therapy [23] . However few models have specifically addressed the effect of cell cycle redistribution in conjunction with cell-cycle specific radiosensitivity [24] and most of these rely on an abstract representation of the tumour cell population . In comparison to a previous single cell-based model by the authors [25] the new model relies exclusively on measurable cell parameters in order to allow for a more direct comparison to experiments . It has been based on the linear-quadratic model for radiation survival and introduces a range of observables to quantitatively describe the synchronisation and sensitivity changes within the tumour spheroid . These qualitative changes and extensions were necessary in order to allow for the study of realistic fractionation schemes as well as alternative radiation delivery timings . Tumour spheroids have been chosen as model system for radiation reactions as they allow for a straightforward testing of predictions in vitro , while retaining a considerable degree of realism when compared to flask cultures [26] . It is to be expected that the effects of synchronisation observed in tumour spheroids are not completely lost in in vivo tumours and are worth being a target of further research for that reason . Within the investigation the focus rests on the redistribution of cells within the cycle phases which occurs as a result of irradiation during treatment . Using a three-dimensional , agent-based model of microtumour growth , we will show its implications for the fractionation of irradiation during clinical treatment schedules . It allows us to demonstrate that an individualised treatment plan , which incorporates cell cycle redistribution effects , can yield a better outcome than typical standardised treatment schedules . The predictions of our model system can thus be used as a guideline for subsequent in vitro experiments and , after in vivo study and validation , ultimately be incorporated into clinical trial settings .
A three-dimensional single-cell based model is developed in order to study the growth of tumour nodules and their reaction to therapeutic approaches . The main parameters are listed in table 1 . It has to be stressed that all parameters used within the simulation are physically accessible and thus can be obtained from experimental measurements . Accordingly the simulation can be tailored to model a specific cell line in conjunction with joint experimental investigations . However the observed effects are of a universal nature , meaning that they are largely insensitive to variation of parameters , as has been tested in the simulation . Hence the choice of parameters is exemplary for a wide physiological range of cells and does not aim to reflect one specific cell line . Technically the present model is developed in C++ code on the framework of the Voronoi-tessellation of biological tissue [27] , [28] . A validation of the employed tumour growth model is provided in reference [25] and in the supporting figure S3 . The use of a three-dimensional spheroid model is of importance in order to obtain a system which comprises a range of features that are present in real tissues and which cannot be adequately described using two-dimensional models [29] , [30] . Accordingly it has been demonstrated experimentally that the treatment reaction of cells in three dimensional structures such as multilayers , spheroids or xenograft tumours can differ strongly from the reaction in a monolayer [31]–[34] . This is to a large extent an effect of the cell interaction within a tissue and the specific spatially and temporally heterogeneous cell cycle distribution which will arise in a tumour spheroid [35] , [36] . Realistic nutrient gradients , as they develop in response to diffusion through a breathing tissue , will only be found in such three dimensional cell arrangements . Overall a macroscopic tumour in vivo ( with a diameter in the order of centimetre ) is comprised of small microscopic sub-volumes of about 500 diameter which form in between capillaries . Each of these microtumour regions will consist of an outer proliferating rim , an intermediate mostly quiescent region and an inner necrotic region as a result of the limited nutrient diffusion range . Due to the structure of vessels these regions will usually be elongated and stretch out between capillaries but also regular patterns of nutrient support have been observed in tumours [37] . Our model spheroid directly corresponds to one such microregion or tumour nodule [36] , and can also serve as a model for the reaction of a larger tumour volume as a result of its functional and histological correspondence to a microtumour region [38] . The total amount of cell death in response to a radiation dose matches experimental measurements , as the linear quadratic model for single cell survival with measured parameters is employed . In response to irradiation with the dose D ( defined in Gy ) cells obtain a cell cycle phase-dependent survival probability from the linear quadratic model [46]: ( 1 ) As physiological example and values of V79 hamster cells which were subjected to x-rays by Sinclair [2] are employed ( supporting figure S2 and table 1 ) . It has been repeatedly reported that quiescent cells exhibit an increased resistance to radiation damage [47]–[50] . This fact is incorporated into the model by using a quiescence resistance factor ( QRF = 1 . 5 ) to scale down the effective radiation dose which quiescent cells experience . Thus , within this assumption , quiescent cells use the measured LQ-parameters of G1 cells but with reduced dose . Once committed to the death path , a cell can either be killed on a fast timescale ( probability “acute chance” ) or after delay on a slow timescale ( with probability ) as shown in figure 1 . The fast process corresponds to a relatively acute , direct commitment to cell death via apoptosis or necrosis in response to heavy DNA damage ( e . g . clustered lesions ) and accordingly a rather low duration for cell death was chosen with an average of 12 h [51]–[53] . The slow process corresponds to a prolonged inability to pass the G2/M checkpoint which will lead to the pile-up of cells in the G2-phase after irradiation and eventually leads to cell death e . g . via mitotic catastrophe or a loss in the so called “race between repair and cell death” [54] , [55] . Both is represented as failure at the G2/M checkpoint and progression to cell death with a “mitotic mismatch”-rate . While this model drastically simplifies the multitude of mechanisms of radiation-induced cell death [56] , the overall amount of cell death observed will be in agreement with experimental measurements within the LQ-model . The inclusion of a fast and slow damage timescale increases the matching of the predicted cell cycle response to experimental measurements [57] . Damage repair is not considered in detail within the model as it will be phenomenologically contained within the measured LQ-survival . Furthermore the typical radiation delivery interval within the simulations will be large enough in order to assume largely independent irradiation events as the majority of remaining damage will have been repaired in the inter-fraction time [58] , [59] . In order to assess the radiosensitivity of the tumour spheroid , we use the ratio of the virtual total survival observed in our simulation at the time of interest and a baseline survival which is expected for the tumour cells under consideration . The expected survival is defined as the average of the survival probabilities , where each cell cycle phase specific survival probability from the LQ-model is weighted with the average duration of the corresponding phase-length and normalised using the total average cycle time : ( 2 ) This baseline survival reflects the typical survival of an exponentially growing tumour spheroid without quiescent sub-population and with uniform distribution of the cells proportional to the cycle phase-lengths . Consequently it should correspond to the expected survival within fully active microregions of a macroscopic tumour . However , within the scope of this work it will be only applied in the context of tumour spheroids . The observed cell survival can be obtained at any time by virtual simulation of the impact of a dose of radiation , without application of the according changes to the tumour system . The fraction of surviving cells yields the observed survival: ( 3 ) Consequently we define the enhancement as the ratio of expected and observed survival: ( 4 ) An enhancement larger than one reflects a tumour in a state of increased sensitivity to radiation , while a lower enhancement reflects a resistant state , as is the case for a tumour which contains a large quiescent population . As a measure of treatment success we use the tumour burden , which is defined as the integral of the total number of cells in the tumour over a time of interest ( area under the curve ) . A typical unit for this observable is cell-days . Further radiobiological observables like the mitotic index ( MI ) and S-phase fraction ( SPF ) are directly accessible from the cell cycle distribution of the agent-based model at all times . They can be used to predict radiosensitivity directly as in [60] and can be compared to experimental measurements . The cell phase-angle is used to measure the relative progression of an individual cell through its cell cycle , independent of functional cell cycle phases . is defined as the ratio of total time spent in the active cell cycle phases ( cells which enter quiescence will thus not advance their phase-angle ) and the individual total cell cycle time : ( 5 ) Since the cell cycle times are drawn from a normal distribution ( with a maximum variation ) individually for every cell and cycle phase , two cells can have an identical phase-angle while their functional cell cycle phase is not identical . Using the phase-angle we define the orderedness of the tumour cell population , by calculation of the Shannon entropy of the system . The probability mass function will be obtained by sorting all cells of the tumour into bins according to their cell phase-angle . Thus we can calculate the Shannon entropy of the tumour system ( 6 ) and use its maximum to define the orderedness of the population as ( 7 ) The entropy and orderedness of the system are well behaved , so that it is possible to use a small number of bins for grouping . One such arrangement is the ordering of cells by functional cell cycle phase or cell DNA content , which are both easily assessed experimentally in in vitro settings or in vivo from biopsies . The orderedness of the system will approach 1 for synchronous populations and 0 for populations which are uniformly distributed in the cell cycle .
In silico tumour spheroids were grown in a standardised protocol from 10 tumour seeder cells using the parameters in table 1 . The seeder cells were allowed to grow for 14 days and formed microtumours of about cells with a typical diameter of 700 . An initial exponential growth phase was followed by a subsequent growth retardation by induced quiescence and necrosis . Treatment of the spheroids started at day 14 . The fully grown microtumours incorporated all typical histological regions which are of importance for the radiation response . A large , stable quiescent cell population was present , which could quickly respond to radiation-induced changes in the tumour environment ( figure 2 ) . Due to dissolution of necrotic cells a hollow core formed in the tumour spheroid before a treatment plan was started ( figure 2 and supporting figure S3 The synchronicity of the tumour cell population steadily decreased over time as the cell cycle progression was desynchronised by the normal distribution of cell cycle times . This decrease is visible as smoothing of the oscillation in the cell cycle distribution in figure 2A and directly via the decrease in orderedness as shown in supporting figure S1 . Another major contribution to the desynchronisation was the entry of cells into quiescence and subsequent re-entry into the active cycle . After homogeneous irradiation of the tumour spheroid with 4 Gy a large fraction of cells committed to cell death ( figure 2 ) . However , irradiation of the tumour also led to its subsequent reactivation . Through the clearing of dead cells the pressure and nutrient situation for surviving cells improved considerably , which triggered a fast re-entry of previously quiescent cells into the active cycle ( figure 2 ) , as has been observed experimentally [61] , [62] . This radiation-induced regrowth was exponential as almost all clonogenic cells in the spheroid were dividing again . Radiation led to a redistribution and synchronisation of the cell cycle progression as it killed predominantly cells in sensitive phases of the cycle . The observed redistribution and subsequent evolution of the cell cycle distribution corresponded well to experimental observations [57] ( figure 3 ) . A G2-block of cell cycle progression was observed , where DNA damaged cells gathered at the G2/M checkpoint . Thus the ratio of cells in G1 to cells in G2 was transiently inverted in response to a radiation dose ( figure 2 ) . Best agreement was achieved when a high degree of fast , acute and a lower level of slow cell death e . g . through mitotic catastrophe were used for the radiation death dynamics . The timescale but not the quality of the dynamic reaction is subject to variations by cell- and radiation type as can be seen in [58] for Chinese hamster V79 lung cells or [63] for SiHa xenograft tumours . Due to the higher radioresistance of quiescent cells , immediately after irradiation the relative fraction of quiescent cells among all viable was temporarily increased . The subsequent re-entry of quiescent cells into the active cycle was largely synchronised at the G1/S checkpoint ( figure 2 ) . The synchronisation of the cell cycle progression led to collective oscillations of radiosensitivity in the tumour ( figure 3 ) . The enhancement in the tumour exhibits a transient , two-peaked reaction to irradiation . The observed loss of sensitivity for a quiescent tumour and the subsequent gain in sensitivity after irradiation increased with dose . While a quiescent tumour was only half as sensitive to a dose of 8 Gy as its exponentially growing counterpart , after irradiation its sensitivity increased more than twofold . Accordingly , one goal in experimental scheduling can be to design a radiation delivery which is optimised to use these recurring periods of transient sensitivity and avoid dose delivery during times of radiation resistance . Clinically a large integral dose will be applied in multiple fractions in order to sterilize a tumour or reduce its size . Dose delivery will be fractionated in order to limit side effects in surrounding tissue and exploit the initially mentioned effects that the fractionated delivery has on the tumour [1] . The timing of dose application is typically chosen such as to provide a balance between practical restrictions such as clinical workload , curative effect and side effects . The standard clinical radiotherapy protocol is the repeated application of doses of 2 Gy each in daily fractions which will be administered over a prolonged time until an integral dose of typically 60 Gy is reached . Treatment is often paused during weekends to allow for tissue regeneration and re-oxygenation , but also for reasons of clinical workload . Common alternative fractionation schedules include hyperfractionation e . g . with the delivery of 2 smaller fractions every 12 hours or hypofractionation with the delivery of higher single doses and a shorter total treatment time [46] , [64] , [65] . Typically a similar integral dose is used ( table 2 ) . Alternative schedules which employ very high single doses as in Stereotactic Body Radiation Therapy [66] or oligofractionation [67] will no be part of the investigation , as they would most likely exceed the validity of the linear-quadratic model . Figure 4 provides an overview of the effects of selected fractionation schemes from table 2 when applied to the model tumour . In general a high degree of regrowth in response to irradiation was observed in silico . Reactivated cells repopulated the tumour and due to their unlimited replicative potential lead to a quick reformation of the spheroid . This was true even when only a very small number of cells was left alive . A typical integral dose of 60 Gy thus did not fully sterilize the model tumour , even when applied in a short amount of time such as in a hypofractionated schedule . This is in agreement with experimental observations on multicellular tumour spheroids in vitro , where a much more rapid growth of spheroid cells is observed than for cells in an in vivo setting [58] . In terms of a reduction of the tumour burden , the high dose-per-time schedules all performed better . In general they allowed less regrowth of the tumour to occur due to the shortened treatment time . Furthermore they benefited from the quadratic term in the dose-survival relation of the LQ-model eq . 1 due to the high single-doses used . Longer treatment pauses , as in the conventional , “un-accelerated” schedules , had a significant negative effect on the tumour control . Each pause allowed for an unchecked period of regrowth within the tumour , which was not cancelled out , as the integral dose was kept constant . Treatment pauses can make all the difference between the achievement of a steady reduction in tumour load , or a failure to keep the tumour in check ( figure 4 ) . Schedules which employed a low dose per fraction ( such as hyperfractionation ) performed better than schedules which delivered the same dose per time in medium-sized single fractions . This is not to be expected , as the quadratic survival term in the LQ-model will yield a lower survival for larger doses . The reason for this observation is the timing of the radiation delivery in conjunction with the development of tumour radiosensitivity ( figure 4 ) . While the conventional radiation schedule delivered follow-up doses at a time of low tumour radiosensitivity , within the hyperfractionated schedule follow-up doses were delivered at a time of high radiosensitivity . Dose delivery within the conventional , accelerated conventional or split course treatment occurred in intervals , which failed to induce a persistent high enhancement in the tumour . Hyperfractionated schedules in contrast succeeded at keeping the enhancement in the tumour at a steady high level , which was especially true for the accelerated hyperfractionation schedule . Effectively the hyperfractionated schedule suppressed the reformation of a radioresistant quiescent subpopulation . Although it allowed the tumour to grow exponentially at all times , the frequent delivery of doses kept the growth in check . Even so CHART used lower single doses it was able to achieve a high tumour control at an overall lower integral dose . However , the dose per time interval which is applied in CHART treatment is very high with 4 . 5 Gy/24 h , thus possibly increasing side effects of the treatment . Considering the fast repair of sublethal damage in most cells , CHART would however allow for repair of most damage in surrounding tissue with a delivery interval of 8 hours . CHART-fractionation kept the enhancement of the tumour for follow-up doses steadily above a level of one , thus achieving a moderate increase in effectivity ( figure 4 ) . For a better comparison of the effects of delivery timing , it is useful to systematically compare schedules which apply the same integral dose over the same time , but with a systematically varied dose per time interval . We thus investigated how the varied fractionation of a typical constant dose per time of 2 Gy per day would influence the outcome of a radiotherapy regimen ( figure 5 ) . The tumour burden was significantly different and best performance was observed for delivery intervals of 30 , 36 and 48 hours ( figure 5 ) . Larger single fractions , as for a delivery interval of 48 h , have the advantage of inducing a higher amount of cell death when compared to the combination of multiple smaller doses ( due to the quadratic term in the LQ model ) . While it is thus not surprising that a run with the largest single doses of 4 Gy showed a good performance , it is interesting that this performance was closely matched by a run with single doses of only 2 . 5 Gy . Treatment with intermediate single doses of 3 . 5 Gy performed significantly worse than with doses of 2 . 5 Gy , which demonstrates that the quadratic dose-effect alone does not determine the success of the treatment . Instead the success of the 2 . 5 Gy schedule can be explained by the good match between the fractionation timing an the tumour enhancement development ( figure 5 ) . A negative timing effect is present in the 3 . 5 Gy schedule when compared to the 4 Gy schedule ( figure 5 ) . The enhancement effects cancel out the advantage of the larger single dose due to LQ-survival . Repeated delivery of doses of 3 Gy with varying inter-fraction time were applied until the in silico tumour was fully sterilised ( figure 5 ) . Due to the radiation-induced reactivation and regrowth , longer inter-fraction times will be associated with a higher amount of tumour regrowth , so that a linear dependency of total dose necessary for sterilisation and fractionation interval might be expected , which turns out to be wrong . Instead the required number of fractions for sterilisation exhibits a minimum at fractionation intervals of 500–700 minutes . Analysing the development of enhancement during the continued radiation delivery reveals that the nature of the fractionation curve can be explained by the relation between irradiation interval and enhancement development ( see also supporting figure S6 ) . Low fractionation interval times of 100 to 300 minutes are inefficient , because the tumour is still in a region of low enhancement when it receives a follow-up dose . A follow-up interval of 400 minutes already allows for a gain in enhancement before the next dose is applied . This gain in enhancement is so large that it counterbalances the effect of tumour regrowth for treatment intervals from 400 to 1000 minutes . If a larger interval is used , the number of fractions needed to sterilise the tumour grows drastically as the follow-up irradiation coincides with a minimum in enhancement at the 1200 minutes interval . For even larger fractionation intervals a lower integral dose will be sufficient for sterilisation even though a higher total regrowth time is allowed . The coincidence of rising triggered enhancement and follow-up radiation dose delivery leads to the local minimum in fractions needed between 1300 and 1600 minutes fractionation interval time . A range of tailored radiation protocols was designed in order to exploit the induced dynamic changes of radiosensitivity in the tumour and deliver radiation at timepoints of high enhancement ( figure 6 ) . One strategy was to divide the dose delivery into trigger-doses and subsequent effector-doses . Effector doses were delivered with a constant time-shift after the trigger-doses , which corresponded to the peak-timing in enhancement which was observed after administration of a single dose ( figure 3 ) . After each combined trigger and effector dose block , irradiation was paused in order to achieve an overall constant dose per time interval of 2 Gy/24 h . In general , protocols were successful which used a smaller trigger dose in combination with a larger follow-up dose . The initial trigger dose induced a synchronisation in the tumour and increased enhancement . The large following effector dose would then be delivered to a sensitive tumour . Very small trigger doses below 1 Gy induced only a partial resynchronisation of the population and thus lead to an overall poor performance when employed in triggered schedules . Surprisingly the protocol which delivers a trigger dose of 2 Gy followed by an effector dose of 4 Gy was able to cancel out the high regrowth which resulted from the pause of 48 h in between an effector dose and the next trigger-effector combination . Except for the fact that this protocol employs large single doses of 4 Gy ( which might increase side-effects ) , it is especially interesting for a combination with adjuvant approaches which could reduce regrowth during the treatment pauses and thus could further improve the outcome substantially . All triggered treatment protocols resulted in an increase in tumour reduction when compared to the standard accelerated conventional or accelerated hyperfractionated schedule . However , the simple altered protocol of constant 2 . 5 Gy/30 h was still the most successful protocol in terms of overall tumour burden reduction ( figure 6 ) . In this case the timing of the follow-up dose by chance persistently matched the peak in triggered sensitivity over the whole treatment time ( figure 5 ) . In contrast , while the initial trigger-effector dose combination achieved the desired effect of inducing and exploiting a state of high radiosensitivity , the trigger-effector block of the same timing would not always prove to be right at later times during the irradiation regimen ( figure 6 ) . In many cases a fixed timing for the trigger-effector block would lead to the delivery of the effector dose at times of lowered radiosensitivity , once the tumour composition had changed during treatment . The time for the tumour to settle into a steady state in terms of enhancement reaction was larger than 48 hours and therefore larger than the typical inter-fraction time . Constant schedules which included longer pauses thus were able to maintain a proper trigger-effector dose timing for a part of the treatment regimen before changes in the tumour composition caused the timing to fail . In many cases after application of the effector dose , a further strong peak in enhancement developed ( figure 6 ) . In principle , this allows for an increasing “stacking” of trigger and effector doses up to the case of continuous delivery at the next triggered sensitivity peak . Protocols with a combination of 3 consecutive well-timed doses in a constant block however did not prove to be effective , as delivery suffered strongly from the shift of the enhancement response during treatment . As the enhancement response timing changes during the course of a prolonged treatment regimen , the targeting of the optimal enhancement point is only possible with permanent recalculation of the timing and , thus , can usually not be achieved with a fixed schedule . In order to exploit the build-up of radiosensitivity , triggering algorithms were tested which automatically delivered a follow-up dose at times of high enhancement ( figure 6 ) . A peak in enhancement was detected either by linear regression of the enhancement in a time window of interest , or in the simplest case by absence of an increasing enhancement value within a time window of . Once a peak was detected , radiation was delivered if the resulting dose was above a minimum of . The dose was calculated in order to reach a constant dose per time interval of 2 Gy/24 h . For comparison a manually optimised schedule was tested , where a dose was always delivered exactly at the suitable enhancement peak . The simple automatic triggering algorithm performed significantly better than conventional schedules , if the delivery of low doses was allowed by setting to 1 Gy . As a result of the small time interval which was necessary in order to identify each enhancement peak , the automatic triggering performs slightly worse than a manual optimised treatment schedule ( figure 6 ) . While this automatic dose delivery could achieve a very good performance in terms of tumour reduction , it was still slightly inferior to the most successful schedule of 2 . 5 Gy/30 h . This inferior performance was due to the fact that the triggering algorithms and also manual scheduling performed only a local optimisation , triggering at the next suitable maximum of enhancement . However , an effective overall treatment schedule design requires a global optimisation , which cannot be achieved with algorithms that only take into account the following sensitivity maximum .
We employed an agent-based model in order to study the reaction of a microtumour to radiotherapy with special emphasis on the cell cycle distribution , synchronicity changes and the subsequent development of the overall radiosensitivity . The two-peaked increase in radiosensitivity which followed a dose of irradiation ( figure 3 ) was used as a guideline for optimal irradiation timing in fractionated treatment regimens . The simple use of experimentally determined cell cycle-specific radiosensitivity , combined with a simple survival model , thus predicts optimisation possibilities in radiation delivery . The predicted results must must be validated or refuted in either an in vitro or an in vivo system . The total possible gain or loss in efficiency of a treatment schedule due to cell cycle effects is immense . This becomes evident when the best and worst possible outcome for irradiation with 2 Gy are compared with according cell survival of 30% or 70% , depending on the cycle phase . For a treatment regimen with only 20 fractions this will yield a worst-case difference of a factor . Even if this value represents an extreme case , most regimens will actually feature more than 20 fractions so that even small changes in survival based on cell cycle-dynamic can significantly alter the overall chances of tumour control . In general the suppression of quiescent cells achieved by most hyperfractionated schedules is beneficial on one hand , as it will avoid quiescent radio-resistance . On the other hand , it will fully activate the growth potential of the tumour and thus allow for an exponential regrowth . The latter effect is especially devastating when combined with longer treatment pauses . An efficient combination with regrowth-cancelling adjuvant treatments would be needed , which could be combined with treatment protocols that make use of large inter-fraction pauses . Another viable option for combination of adjuvant chemotherapy and radiotherapy is the use of drugs which can prepare the tumour into a radiobiologically sensitive state [68] , [69] . This can be achieved by the well-timed administration of drugs which have a cell-cycle synchronising effect , such as hydroxyurea [70] , [71] . Within the simulation appropriate radio-chemo-schedules were tested and able to achieve significant enhancements in treatment outcome , especially when used in conjunction with high single doses ( results not shown ) . The observed cell cycle effects and reoxygenation of the tumour spheroid are also of interest for modern heavy-ion irradiation whenever spread out Bragg peaks are used that show a mixed-LET composition [72] . Especially in treatments which employ large single doses , such as in relativistic plateau proton-radiosurgery [73] or Stereotactic Body Radiation Therapy [66] , [74] , the cell cycle effects could be considerable and at the same time their dynamics can be easily estimated . Also in modern oligofractionated schedules which employ very high fractions [67] , cell cycle effects could accordingly affect the treatment efficiency and could be possibly used quite actively . In order to study these effects in silico new radiation damage models need to be considered , which accurately describe radiation effects also in the range of very high doses [75]–[79] . While the exact timing of the effects will vary by cell- and radiation type , the universal effects such as the transient periods of radiosensitivity and radioresistance are present in every tumour and should subsequently be further studied within in vitro experiments . Variation of cell parameters such as quiescence radiation resistance , damage dynamics parameters , cell death durations and quiescence criterion led to minor quantitative changes , but the qualitative finding of transient radioresistant and radio-sensitive periods was conserved . The readiness of cells to enter and leave quiescence is of special interest , as it can increase the dampening of the oscillatory response in enhancement . Furthermore , the cell cycle duration and its typical variation are important for the sensitivity timing . Even for high variations of the typical cycle durations , which has been assumed in the simulation , the enhancement effects were pronounced and could be used for treatment optimisation . The specific nature of cell cycle checkpoint regulations ( or the loss of it ) and their genomic basis were disregarded in the present model . If a particular cell line is under consideration the status of key regulatory genes such as TP53 or ATM can be taken into consideration for refinement of the cell behaviour within the model [80] . The presented model rests on a foundation of very basic assumptions for the radiation reaction which are justified in most cells: first , cells exhibit a variation in radiosensitivity between different cell cycle phases [81] , second , cells are subject to a degree of cell cycle regulation in response to damage or due to environmental effects ( such as oxygenation , nutrient support or pressure ) [38] , [82] , and third , cells in quiescence will show a resistance to radiation [83] . Ergo the described cell cycle effect should be present in any tumour system in which these assumptions are justified , irrespective of cell type or composition , although they may overlap or even be completely masked by other effects , e . g . reoxygenation dynamics . Considering the overall development of radiosensitivity in a tumour which is triggered by irradiation , it seems reasonable to apply a scheme of trigger- and follow-up-doses to exploit the induced dynamics as was proposed and tested . Protocols which use a small trigger dose followed by a larger effector dose aimed at periods of high sensitivity could in principle be used clinically without alteration of the overall dose-rate . However , the identification of the transient periods of increased radiosensitivity is mandatory , as a wrong timing could result in a decrease of efficiency . When a multi-fractionated regimen is applied , the timing of irradiation cannot be simply derived from the sensitivity development in response to a single irradiation dose . Instead the development of sensitivity will be more complex , as the internal dynamics of the tumour ( especially reactivation and depletion of quiescent cells ) play an important role . With the use of simple automatic enhancement-based scheduling algorithms a significant increase in treatment performance was achieved . Triggering based on the monitoring of cell cycle-based enhancement is thus a possible method to automatically design optimised schedules . Such schedules would be robust as they can adapt to dynamic changes of the tumour and would furthermore be largely independent of any undetermined tumour parameters . In order to use any optimised scheduling approaches , the identification of high and low-enhancement periods is mandatory . Thus , live monitoring , or at least a higher sampling frequency combined with a model for the periods in between two measurements , is required to allow for a stable exploitation of the potential of cell cycle synchronisation effects . While a higher frequency of monitoring induces additional clinical workload it is in principle simple to achieve , especially with combined PET/CT installations which are increasingly available at clinical treatment sites . A higher imaging frequency is also called for in conjunction with related phenomena such as hypoxia dynamics [84] , where it has been shown that temporal variations of pO2 in mouse models exhibit 18-fold fluctuations with patterns on the scale of only minutes [85] . This observation clearly illustrates that measuring key tumour attributes only once or twice during a prolonged therapy regimen is not sufficient to understand or even therapeutically employ the kinetics of cell cycle redistribution or reoxygenation . An experimentally or even clinically accessible observable for the synchronisation of the cell population is thus of utmost importance and should be the target of future investigations . If the orderedness of the cell cycle distribution can be assessed , its correlation with the radiosensitivity enhancement could be used to predict optimal irradiation times ( see supporting figure S1 ) . Another approach could be the monitoring of oxygen or glucose uptake in the tumour with high temporal resolution , as is regularly called for in the context of hypoxia [84] . This uptake is related to the collective development of the cycle distribution and therefore the overall radiosensitivity . In the best case a continuous monitoring of vital parameters such as cell cycle durations , key gene expressions and so forth would be available by a combination of imaging and possibly also sequential biopsies in order to predict suitable irradiation intervals . In summary this suggests a basic scheme for the inclusion of cell cycle effects in therapy . In a first step the degree of cell cycle redistribution in the tumour which occurs in response to a treatment should be assessed . This assessment can also take into account a known genetic profile for cycle regulation and deregulation in the tumour . If the tumour is found to be susceptible to cell cycle redistribution and regulation , a synchronisation-based fractionation scheme should be considered [71] . The prediction of radiation sensitivity timings can thus be achieved using a basis of simulations and monitoring or biopsies with cultures of primary tissue . In the ideal case a feedback between modelling and measuring can be achieved , where information from only a few biopsies will be combined with a model in order to predict suitable patient-specific irradiation timings . | The sensitivity of a cell to a dose of radiation is largely affected by its current position within the cell cycle . While under normal circumstances progression through the cell cycle will be asynchronous in a tumour mass , external influences such as chemo- or radiotherapy can induce a synchronisation . Such a common progression of the inner clock of the cancer cells results in the critical dependence on the effectiveness of any drug or radiation dose on a suitable timing for its administration . We analyse the exact evolution of the radiosensitivity of a sample tumour spheroid in a computer model , which enables us to predict time windows of decreased or increased radiosensitivity . Fractionated radiotherapy schedules can be tailored in order to avoid periods of high resistance and exploit the induced radiosensitivity for an increase in therapy efficiency . We show that the cell cycle effects can drastically alter the outcome of fractionated irradiation schedules in a spheroid cell system . By using the correct observables and continuous monitoring , the cell cycle sensitivity effects have the potential to be integrated into treatment planing of the future and thus to be employed for a better outcome in clinical cancer therapies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | In Silico Analysis of Cell Cycle Synchronisation Effects in Radiotherapy of Tumour Spheroids |
Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection ? To answer this question , we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects . Rendered images consist of smoothly varying , globally aligned contour fragments ( amoebas ) distributed among groups of randomly rotated fragments ( clutter ) . The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time ( 20-200 ms ) , followed by an image mask optimized so as to interrupt visual processing . Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms , depending on amoeba complexity . Key aspects of the psychophysical experiments were accounted for by a computational network model , in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields , represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images . Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least ms of cortical processing time . Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception , with the remaining discrepancies postulated to arise from the influence of higher cortical areas .
The perception of closed contours is fundamental to object recognition , as revealed by the fact that common object categories can be rapidly detected in black and white line drawings in which all shading and luminance cues have been removed [1] . Cortical association fields , hypothesized to capture spatial correlations between local image features via long-range lateral synaptic interactions , provide a natural substrate for rapid contour perception [2] . The link between cortical association fields and contour perception has been investigated through a variety of behavioral , experimental , and theoretical techniques [3]–[6] . Psychophysical measurements reveal that the detection of implicit contours , defined by sequences of Gabor-like elements presented against randomly oriented backgrounds , becomes more difficult as the local curvature increases and as the individual Gabor elements are spaced further apart or their alignment is randomly perturbed . This dependence on proximity and relative orientation implies that , in early visual areas , cortical association fields are primarily local and aligned along smooth trajectories [2] , [7] , [8] . In related studies , collinear Gabor patches have been shown to both increase and decrease the contrast detection threshold of a central Gabor patch in a manner that depends on the relative timing , orientation and spatial separation of the flanking elements [9]–[11] , providing further psychophysical evidence that lateral influences act at early cortical processing stages , although the contribution of collinear facilitation to contour integration remains controversial [12] . In primary visual cortex ( V1 ) , electrophysiological recordings indicate that the responses to optimally oriented and positioned stimuli can be facilitated by flanking stimuli placed outside the classical receptive field center [5] , [6] , [10] , [13] , although these effects have also been ascribed to elongated central receptive fields [14] , [15] and facilitation has been attributed to increases in baseline activity [16] . Nonetheless , collinear facilitation is consistent with anatomical studies indicating that orientation columns are laterally connected to surrounding columns with similar orientation preference [17]–[19] . Because extensive association fields are present in the primary visual cortex [17]–[19] , lateral interactions may be key to discriminating smooth object boundaries at very fast time scales ( of the order of tens of ms ) , as observed in numerous speed of sight psychophysical experiments [1] , [20]–[23] . Correspondingly , theoretical models have proposed that V1 cortical association fields can be described mathematically on the basis of cocircularity , and that relaxation dynamics based on cocircular association fields can extract global contours by suppressing local variation [24] . Such models are qualitatively consistent with human judgments as to whether pairs of short line segments belong to the same or separate contours , with human judgments closely following the pairwise statistics of edge segments extracted from natural scenes [25] . Further , model cortical association fields , when used to detect implicit contours , can predict key aspects of human psychophysics , particularly the measured dependence on the density of foreground elements relative to background elements [8] , [26] . In this paper , we extend the above studies by investigating whether model cortical association fields can account not only for dependence of contour perception on intrinsic task difficulty , a relationship that has been previously explored [8] , [26] , but also for the detailed time course of human contour detection , an aspect that has heretofore not been modeled explicitly , although the time-dependent influence of lateral interactions has been determined for several theoretical models [27] , [28] . In this work , we employ multiplicative relaxational dynamics to estimate the time course of contour detection from a computational model employing optimized kernels . Model results are then compared to speed-of-sight measurements from human subjects performing the same contour detection task . To obtain optimized cortical association fields , we design lateral connectivity patterns using a novel method that exploits the global statistical properties of salient contours relative to background clutter . Our procedure , which can be generalized beyond the present application , can be summarized as follows . We begin by generating a large training corpus , divided into target and distractor images , from which we obtain estimates of the pairwise co-occurence probability of oriented edges conditioned on the presence or absence of globally salient contours . From the difference in these two probability distributions , we construct Object-Distractor Difference ( ODD ) kernels , which are then convolved with every edge feature to obtain the lateral contextual support at each location and orientation across the entire image . Edge features that receive substantial contextual support from the surrounding edges are preserved , indicating they are likely to belong to a globally salient contour , whereas edge features receiving minimal contextual support are suppressed , indicating they are more likely to be part of the background clutter . The lateral contextual support is applied in a multiplicative fashion , so as to prevent the appearance of illusory edges , and the process is iterated several times , mimicking the exchange of information along horizontal connections in the primary visual cortex . Our method is thus intended to capture the essential computational elements of cortical association fields that are hypothesized to mediate the pop-out of salient contours against cluttered backgrounds . To obtain a large number of training images and to better isolate the role of cortical association fields linking low-level visual features , we employ abstract computer-generated shapes consisting of short , smooth contour segments that could either be globally aligned to form wiggly , nearly closed objects ( amoebas ) , or else randomly rotated to provide a background of locally indistinguishable contour fragments ( clutter ) . Amoeba targets lack specific semantic content , presumably reducing the influence of high level cortical areas , such as IT . However , our computer-generated images would not be expected to eliminate the contribution to contour perception from extrastriate areas [29]–[32] . Thus , our model of lateral interactions between orientation-selective neurons is designed to account for just one of several cortical mechanisms that likely contribute to contour perception . Our amoeba/no-amoeba image set differs from stimuli used in previous psychophysical experiments that employed sequences of Gabor-like elements to represent salient contours against randomly oriented backgrounds [2] , [7] , [8] . An advantage of contours represented by random Gabor fields is that the target and distractor Gabor elements can be distributed at approximately equal densities , thereby precluding the use of local density operators as surrogates for global contour perception [2] . However , our amoeba/no-amoeba image set is more akin to the natural image sets used in previous speed-of-sight object detection tasks [33] , particularly with respect to studies employing line drawings derived from natural scenes [1] . Humans can detect closed contours , whether defined by aligned Gabor elements or by continuous line fragments , in less than ms [1] , [20] , which is shorter than the mean interval between saccadic eye movements [34] , thus mitigating the contribution from visual search . Like Gabor defined contours , our amoeba/no-amoeba image set implements a pop-out detection task involving readily perceived target shapes whose complexity can be controlled parametrically . To benchmark the accuracy and the time course of the ODD kernel-based procedure applied to the amoeba/no-amoeba task , we compare our model results to the performance of human subjects on a 2AFC speed-of-sight task in which amoeba/no-amoeba images are presented very briefly side by side , followed by a mask designed to limit the time the visual system is able to process the sensory input [1] , [20]–[23] . Since it takes an estimated ms for activation to spread through the ventral stream of the visual cortex [21] , an effective mask presented within this time frame can potentially degrade object detection performance by interfering with the neural processing mechanisms underlying recognition [22] , [35] . By plotting task performance as a function of the stimulus onset asynchrony ( SOA ) –the interval between image and mask presentation onsets–the resulting psychometric curves are hypothesized to estimate the neural processing time required to reach a given level of classification accuracy . Amoeba targets of low to moderate complexity were found to reliably pop-out against the background clutter , allowing subjects to achieve near perfect performance at SOAs less than ms , even when followed by an optimized mask consisting of rotated versions of the target and distractor images [20] . Our model cortical association fields were able to account for the dependence of human performance on amoeba complexity as well as for aspects of the time course of contour perception as measured by the improvement in human performance with increasing SOA . Thus , we present the first network-level computational model to simultaneously account for spatial and temporal aspects of contour perception , as measured in human subjects performing the same contour detection task . Aspects of the experimental data for which our model fails to account , particularly data showing that human subjects require longer processing times to detect more complex targets , may indicate the possible involvement of extrastriate areas , which may be essential for the perception of more complex shapes .
To investigate low-level cortical mechanisms for detecting smooth , closed contours presented against cluttered backgrounds with statistically similar low-level features , we designed an amoeba/no-amoeba detection task using a novel set of synthetic images ( Figure 1 ) . Amoebas are radial frequency patterns [36] constructed via superposition of periodic functions described by a discrete set of radial frequencies around a circle . In addition , we added clutter objects , or distractors , that were locally indistinguishable from targets . Both targets and distractors were composed of short contour fragments , thus eliminating unambiguous indicators of target presence or absence , such as total line length , the presence of line endpoints , and the existence of short gaps between opposed line segments . To keep the bounding contours smooth , only the lowest radial frequencies were included in the linear superposition used to construct amoeba targets . To span the maximum range of contour shapes and sizes , the amplitude and phase of each radial frequency component was chosen randomly , under the restriction that the minimum and maximum diameters could not exceed lower and upper limits . When only radial frequencies were included in the superposition , the resulting amoebas were very smooth . As more radial frequencies were included , the contours became more complex . Thus , , the number of radial frequencies included in the superposition , provided a control parameter for adjusting target complexity . Figure 1 shows target and distractor images generated using different values of . Human subjects are able to infer whether a two isolated line segments extracted from a natural scene are from the same or from separate contours using only distance , direction and relative orientation of the two segments as cues [25] , [37] . The performance of human subjects is well predicted by differences in the empirically calculated co-occurrence statistics of short line segments drawn from either the same or from different contours . To explore the ability of cortical association fields to account for the perception of smooth contours , we developed a network-level computational model of lateral interactions between orientation-selective elements governed by sigmoidal ( piecewise linear ) input/output synaptic transfer functions . To model lateral interactions , we constructed “Object-Distractor Difference ( ODD ) kernels” for the amoeba/no-amoeba task by computing coactivation statistics for the responses of pairs of orientation-selective filter elements , compiled separately for target and distractor images ( Figure 2 ) . Because the amoeba/no-amoeba image set was translationally invariant and isotropic , the central filter element may without loss of generality be shifted and rotated to a canonical position and orientation . Thus the canonical ODD kernel was defined relative to filter elements at the origin with orientation ( to mitigate aliasing effects ) . Filter elements located away from the origin can be accounted for by a trivial translation . To account for filter elements with different orientations , separate ODD kernels were computed for orientations then rotated to a common orientation and averaged to produce a canonical ODD kernel . The canonical kernel was then rotated in steps between and ( offset by ) and then interpolated to Cartesian axes by rounding to the nearest integer coordinates . The resulting ODD kernels were generally consistent with the predictions of cocircular constructions [24] , except that support was mostly limited to line elements lying along low curvature contours , which follows naturally from the prevalence of low curvatures in our amoeba training set . Curiously , the largest differences in the coactivation statistics occur close to the center of the kernel , where targets and distractors are presumably most similar . However , even at short distances , amoeba segments are still more likely to be aligned than clutter elements . Moreover , nearby pairs occur much more frequently than more distant pairs , amplifying their contribution to the difference map . Since , by design , the individual clutter fragments were locally indistinguishable from the target fragments , co-occurrence statistics of oriented fragments were necessary to solve the amoeba/no-amoeba task . The simplest solution , adopted here , was to focus on pairwise co-occurrences . Notably , in some neural preparations , pairwise interactions have been shown to be sufficient to account for a large fraction of all higher-order correlations [38] , [39] . At the retinal stage , target and distractor images were represented as pixel monochromatic , binary line drawings . At the next stage , corresponding to an early cortical processing area such as V1 , a set of filters was used to represent orientations , uniformly-spaced and centered at each pixel , with the axes rotated slightly ( by ) to mitigate aliasing artifacts . The bottom-up responses of each orientation-selective element were computed via linear convolution using filters composed of a central excitatory subunit flanked by two inhibitory subunits . Each subunit was an elliptical Gaussian with an aspect ratio of , consistent with the aspect ratios of V1 simple cell receptive fields measured experimentally [40] and similar to values employed in previously published models of V1 responses [41] . Likewise , we estimate that each image pixel subtended a visual angle of approximately ( see Methods ) , so that each orientation-selective element in the model subtended a visual angle of approximately , consistent with physiological estimates of V1 receptive field sizes at small eccentricities [42] . All subunits had the same total integrated strength ( to within a sign ) , whose magnitude was adjusted to yield relatively clean representations of the original image in terms of oriented edges . The synaptic transfer function was piecewise-linear with a minimum value of 0 . 0 and a maximum value of 1 . 0 and a fixed threshold of 0 . 5 . A finite threshold and saturation level were essential in order to allow non-supported contour fragments to be suppressed while preventing well-supported fragments from growing without bound . The precise values used for threshold and saturation were not critical , as responsiveness was controlled independently by adjusting the overall integrated strength of the bottom-up and lateral interaction kernels ( see Methods ) . Orientation-selective responses were modulated by successive applications of the multiplicative ODD kernel . Lateral support was first computed via linear convolution of the ODD kernel with the surrounding orientation-selective elements , out to a radius of pixels . Given that images were approximately in extent ( see Methods ) , ODD kernels spanned a total visual angle of approximately degrees , roughly in correspondence with the estimated visuotopic extent of horizontal projections in V1 [42] . The previous activity of each cell was multiplied by the current lateral support , passed through the piecewise-linear synaptic transfer function , and the process repeated for up to iterations . Contour segments that received insufficient lateral support were thereby suppressed , whereas strongly supported elements were either enhanced or remained maximally activated . When applied to the amoeba/no-amoeba image set , the ODD kernels typically suppressed clutter relative to target segments ( Figure 3 , left column ) . When applied in a similar manner to a natural gray-scale image to which a hard Difference-of-Gaussians ( DoG ) filter has been applied to maximally enhance local contrast ( see Figure 3 , right column ) , ODD-kernels tended to preserve long , smooth lines while suppressing local spatial detail . Although ODD kernels were trained on a narrow set of synthetic images , the results exhibit some generalization to natural images due to the overlap between the cocircularity statistics ( see Figure 2 ) of the synthetic image set and those of natural images . To quantify the ability of the model to discriminate between amoeba/no-amoeba target and distractor images , we used the total activation summed over all orientation-selective elements after iterations of the ODD kernel . A set of target and distractor images was used for testing; test images were generated independently from the training images . Histograms of the total activation show increasing separability between target and distractor images as a function of the number of iterations ( Figure 4 ) . To maximize the range of shapes and sizes spanned by our synthetic targets and distractors , we did not require that the number of ON retinal pixels be constant across images . Rather , the retinal representations of both target and distractor images encompassed a broad range of total activity levels , although the two distributions strongly overlapped and there was no evident bias favoring one or the other . At the next processing stage , prior to any lateral interactions , there was likewise little or no bias evident in the bottom-up responses of the orientation-selective elements . Each iteration of the multiplicative ODD kernel then caused the distributions of total activity for target and distractor images to become more separable , implying corresponding improvements in discrimination performance on the amoeba/no-amoeba task . The general principles governing the operation of our model cortical association fields are conceptually straightforward . ODD kernels , which capture differences in the coactivation statistics of edge segments belonging to amoebas relative to edge segments belonging to the background clutter , are used to determine the lateral contextual support for individual edge segments in an image . Edge segments receiving sufficiently strong support are preserved , indicating they are likely to be part of an amoeba , whereas edge segments receiving insufficient support are suppressed , indicating they are likely to belong to the background clutter . To assess the ability of the model cortical association fields to account for the time course of human contour perception , we measured the stimulus presentation time required for human subjects to reach a given level of accuracy on an amoeba/no-amoeba task . The psychophysical experiment was implemented using a speed-of-sight protocol employing a two-alternative forced choice ( 2AFC ) design , with subjects using a slider bar to indicate which of two images , presented side-by-side , contained an amoeba ( Figure 5 ) . The distance the bar was displaced to the left or to the right was used to indicate confidence , see Methods . To effectively interrupt visual processing at a given SOA , both target and distractor images were replaced by an optimized mask , constructed by combining randomly rotated amoeba and clutter segments [20] . Our optimized masks were designed to render the amoeba targets virtually invisible in the fused target-mask composite . As a measure of human performance on the amoeba/no-amoeba task , we constructed receiver operating characteristic ( ROC ) curves [43] ( Figure 6 ) , using each subject's reported confidence ( slider bar location relative to the center position ) as a noisy signal for estimating which side , either left or right , contained the target on a given trial . True positives corresponded to trials on which the subject reported the target was on the left ( relative to threshold ) and the target was actually on the left ( relative to threshold ) . False positives corresponded to trials on which the subject reported the target was on the left whereas the target was actually on the right ( relative to threshold ) . To construct each ROC curve , the confidence scale along the slider bar was divided into 6 discrete threshold values . For each threshold value , a cumulative proportional true positive rate was calculated by considering only those trials as true positives in which the confidence value was above threshold . The cumulative proportional false positive rate for each threshold value was calculated similarly . Each threshold value thus contributed one point on the ROC curve , with true positive rate plotted as the ordinate and the false positive rate as the abscissa . The complete set of points was connected by straight lines to guide the eye ( Figure 6 ) , with a separate ROC curve computed for each combination of SOA and target complexity . ROC curves for quantifying the performance of the model on the amoeba/no-amoeba task were computed similarly , using the difference in total luminance between the left and right images as the raw signal for estimating which side contained the target on a given trial . If the total luminance of the left image was higher than that of the right ( relative to threshold ) , the response of the model would be reported as target on the left . Ideally , after several iterations of the ODD kernel , no segments would remain in the distractor image and only amoeba segments would remain in the target image; in practice , the total luminance served as a measure of confidence . Given the much larger number of trials ( 1000 ) available for assessing model performance , 100 equally spaced threshold values were used to calculate the corresponding ROC curves . As with the ROC curves constructed from the confidence values reported by the human subjects , the ROC curves computed from the confidence values reported by the model give the cumulative proportional true positive rate as a function of cumulative proportional false positive rate , with the confidence threshold varied from zero to maximum . Graphically , the area under the ROC curves is given by the amount of overlap between the total luminance histograms ( see figure 4 ) for the target and distractor images [44] . ROC curves for human subjects show performance increasingly above chance , indicated by a diagonal line of slope , as a function of both increasing SOA and decreasing target complexity . For amoeba targets of low to moderate complexity , ROC curves obtained from human subjects were well matched to those generated by the model cortical association fields , consistent with the hypothesis that lateral interactions between orientation-selective neurons contribute to human contour perception , at least for simple targets . The area under the ROC curve ( AUC ) gives the probability that a randomly chosen target image will be correctly classified relative to a randomly chosen distractor image , and thus provides a threshold-independent assessment of performance on the 2AFC task . Both the average over human subjects and the model cortical association fields exhibited qualitatively similar performance on the 2AFC amoeba/no-amoeba task ( Figure 7 ) . Performance declined as a function of increasing target complexity , both for human subjects , measured at a fixed SOA , and for the model , measured at a fixed number of iterations , implying that was an effective control parameter for adjusting task difficulty . At ms SOA , the performance of human subjects was indistinguishable from chance , suggesting that our optimized masks effectively prevented the development of bottom-up cortical responses , even for the simplest targets ( ) . Although some studies report that line drawings are processed more rapidly than natural images , with above chance performance being observed at short SOA values [1] , [26] , the fact that performance on the amoeba/no-amoeba task was no better than chance at a ms SOA implies that our optimized masks effectively interrupted visual processing of the amoeba targets . Since the model used here did not include any account for the time course of bottom-up retinocortical dynamics , we assumed that the performance of human subjects at ms SOA should be equated to model performance at iterations ( prior to any lateral interactions ) , a time frame consistent with the distribution of the shortest measured response latencies recorded in primary visual cortex [45] . Overall , average human performance improved as a function of increasing SOA in a manner analogous to the improvement in model performance as a function of the number of iterations of the ODD kernel . This correspondence was especially evident for amoebas of low to moderate complexity ( ) . For more complex targets , model performance lagged well behind that of human subjects . Studies suggest that low and high radial frequencies are processed by different cortical channels [46] . Model performance might have been improved by training a new set of ODD kernels specifically for targets containing radial frequencies , thereby utilizing a hypothetical sub-population of orientation-selective neurons optimized for detecting high-curvature contours . Here , our model was limited to a single multiplicative kernel for detecting all predominately smooth contours . To quantify how average human performance on the 2AFC amoeba/no-amoeba task varied with SOA , and to compare with the dependence of model performance on the number of iterations of the ODD kernel , areas under both sets of ROC curves were fit to a monotonically increasing function of the following sigmoidal form: ( 1 ) For human experiments , the parameter corresponds to the SOA in ms . Since we expect humans to perform close to accuracy for very long SOA , we set . Since humans perform essentially at chance ( ) for ms SOA , we set ms . Thus was the only free parameter; fits to the average human data were denoted by ; has units of . Likewise , model performance was fit to a curve with the same functional form , with measuring the number of iterations; was used to denote curve fits to the model data . However , visual inspection of the model data suggests that its performance saturates at less than accuracy even after an infinite number of iterations , thus we forced the sigmoidal curve fit to the model results to asymptote at the final measured value of AUC: . Since the model performs better than chance after only iteration , we set . For both the human experiments and the model performance , the functional form of ensures that , corresponding to a minimal performance equal to chance . We find that and behave quite differently as a function of , the number of radial frequencies used in amoeba generation ( Figure 7 ) . As anticipated for a relaxational process governed by a single kernel , the model data was well described by a single value of ( in units of ) , equal to . For the human subjects data , values of increased from to as a function of amoeba complexity , corresponding to lateral processing times of to ms , respectively . If human performance depended on only a single set of lateral connections , then , at least in the linear approximation case , we might expect human performance to be well described by a single dominant time constant , representing the dominant eigenmode of the horizontal interactions [47] , [48] . Multiple time scales in the human performance case may emerge from any number of physiological mechanisms not included in the present model , including additional non-linearities in the action of the horizontal connections and/or contributions to contour perception from extrastriate areas . Our data do not allow us to make a firm distinction between these possibilities . However , one possible interpretation of the present results is that the perception of simple contours is dominated by relatively fast lateral interactions placed early in the visual processing pathway , thereby accounting for the good fit between the model and experimental results for targets of low to moderate complexity . Building on this interpretation , we postulate that the perception of more complex contours requires more extensive , and therefore slower , processing mechanisms involving higher cortical areas , thus explaining the discrepancy between model and experimental performance as target complexity increases . Under the assumption that human perception of simple amoeba targets ( ) depends primarily on recurrent lateral interactions between orientation-selective neurons , we can estimate the time required for each iteration of the multiplicative ODD kernel . This rate is estimated using the time constants from the fits: ms per iteration , a value consistent with estimates of lateral conduction delays within the same cortical area [13] . Having shown that the lateral interactions based on multiplicative ODD kernels can account for both spatial and temporal aspects human contour perception , we seek to identify model details that are essential to the performance reported here . First , we demonstrate that the proposed model is robust and does not require that the magnitude of the ODD kernel be carefully titrated to a precise value . Model performance on the 2AFC amoeba/no-amoeba task , measured by the area under the ROC curve ( AUC ) for increasing numbers of iterations , was plotted for different values of the strength of the ODD kernel , given by the total integrated strengths of the equal and opposite target and distractor contributions ( Figure 8 ) . The number of radial frequencies was fixed at . Qualitatively similar performance was obtained for ODD kernel strengths ranging from to . The ODD kernel used in the present study , whose strength was set to , produced near optimal performance and also exhibited monotonic improvement with increasing numbers of iterations . That performance was generally insensitive to the value of the main free parameter in the model provides strong evidence for the robustness of the proposed contour detection mechanism based on multiplicative lateral interactions . A second aspect of the model that merits scrutiny is the detailed structure of the ODD kernels , which were trained using computer-generated images in which the pairwise edge statistics uniquely identifying globally salient contours could be calculated directly . Previous models of contour perception typically employed much simpler patterns of lateral connectivity , in which excitatory interactions were either collinear or cocircular , and inhibitory interactions were approximately independent of relative orientation [8] , [24] , [27] , [47]–[49] . To determine if the detailed structure of the ODD kernel was critical to the observed performance , we repeated the amoeba/no-amoeba experiment using a much simpler kernel whose basic form was consistent with a number of previously published models ( see Figure 8 ) . Specifically , we used a “Bowtie” kernel in which excitatory connections fanned out with an opening angle of and the difference in the preferred orientations of the pre- and post-synaptic elements differed by no more than . Both excitatory and inhibitory connection strengths fell off in a Gaussian manner , with inhibition strength being insensitive to orientation . Although the overall accuracy of the Bowtie kernels was lower than that achieved by the ODD kernels , performance on the amoeba/no-amoeba tasks was qualitatively similar , particularly regarding the general monotonic improvement with the number of iterations and the absence of a sensitive dependence on kernel strength . Thus , we conclude that multiplicative lateral interactions are able to preserve smooth closed contours while suppressing clutter in a manner that is robust to broad changes in model details .
We have shown that simple models of neural activity in primary visual cortex , enriched with lateral association kernels , reproduce some of the behavioral features regarding the human perception of broken closed contours . Our results agree not only with the measured dependence on contour complexity but also with the temporal dependence of human perception as a function of SOA , suggesting that horizontal connections in V1 may play a non-trivial and global computational role in the perception of closed contours on very fast timescales . A number of studies relate to the potential contribution of cortical association fields to human contour perception; these encompass a range of anatomical , physiological , psychophysical , and theoretical techniques [2]–[5] , [7]–[10] , [10] , [11] , [13] , [16]–[19] , [50] . In particular , a number of theoretical models have sought to account for human contour perception at the level of biologically-plausible neural circuits [8] , [27] , [28] , [49] , [51]–[54] , with most studies incorporating some form of cortical association field configured to reinforce smoothness [24] . Although biologically plausible models of cortical association fields have been used to account for the dependence of contour visibility on key parameters controlling task difficulty , such as smoothness , closure , and density of background clutter [8] , model cortical association fields have not been directly compared to the time course of human contour perception as a function of contour complexity . Here , we used cortical association fields based on ODD kernels , which were computed from differences in the pairwise coactivation statistics of orientation-selective elements arising from target as opposed to distractor images . While we designed the kernels specifically for the amoeba-clutter disambiguation , we emphasize that the algorithm for the ODD kernel construction is completely general and can be used to improve detection of salient image features in any situation where generative models of targets and distractors are known , or there exists data sets of sufficient size to characterize the contour co-occurrence statistics empirically for both targets and distractors . In our experiments , ODD kernels were able to account for the experimentally observed variations in the saliency of closed contours as a function of parametric complexity and for the time course with which smooth contours are processed by cortical circuits . Crucial for these results was our use of a synthetic target/distractor data set with controllable complexity and the absence of top-down contextual features or local cues that might give away target presence . Here , we used a semi-supervised training scheme to learn lateral connectivity patterns optimized for performing the amoeba/no-amoeba task . Necessarily , we sought to model only a subset of the lateral interactions between orientation-selective neurons , namely , those horizontal connections configured to reinforce smooth , closed contours . We did not attempt to capture the full range of spatial relationships between features extracted at early cortical processing stages [24] , [55] . Presently , databases containing sufficient numbers of fully annotated and segmented natural images needed to reproduce the weeks ( or months ) of visual experience required to train the full complement of horizontal connections in the primary visual cortex do not exist . Moreover , the computational resources to exploit such databases , even if they did exist , are highly non-trivial to assemble . Thus , we focused here on a subset of horizontal connections for which it was possible to construct synthetic surrogate images . At most , the proposed model represents a subset–and only a subset–of the lateral connections between orientation-selective cortical neurons . Moreover , even a complete set of such horizontal connections would , at most , represent but a subset of the cortical mechanisms that contribute to the time course and shape-dependence of contour perception . The supervised training scheme employed here might be related to perceptual learning phenomena , which take place over time scales much shorter than those typically associated with developmental processes [56]–[58] . It is possible that known physiological mechanisms , such as spike-timing-dependent plasticity ( STDP ) , especially with accounts for realistic conduction delays [59] , could mediate a rapid refinement of lateral connections so as to facilitate the perception of amoeba targets . Moreover , physiological plasticity mechanisms might produce different patterns of connectivity for orientation-selective elements representing points of low as opposed to high local curvature , thereby optimizing lateral interactions for contours of varying complexity . Here , we made no attempt to customize distinct ODD kernels for detecting contours of varying complexity . Instead , a single ODD kernel was trained using a complete set of images in which different numbers of radial frequency components were equally represented . Although we did not investigate whether , or to what extent , the performance of human subjects improved over the course of the amoeba/no-amoeba experiment , such investigations might shed insight into the role of perceptual learning in the detection of closed contours . The question of how lateral connectivity based on ODD kernels might be acquired during development was not addressed explicitly . In principle , coactivation statistics between pairs of orientation-selective neurons could be accumulated over time in an unsupervised manner by a Hebbian-like learning rule [60] . Under natural viewing conditions , we expect that contour fragments consistent with smooth , closed boundaries would tend to occur simultaneously , whereas contour fragments inconsistent with object boundaries would tend occur at random temporal delays . Thus , a Hebbian-like learning rule sensitive to temporal correlations , such as certain mathematical forms of STDP-like learning rules [61] , might under normal developmental conditions lead to connectivity patterns that reinforce smooth contours . Of course , human contour perception may have nothing to do with cortical association fields , or lateral interactions may play a subordinate role . Early models showed how spatial filtering could enhance texture-defined contours in the absence of orientation-specific interactions [4] and short-range lateral interactions can accentuate texture-defined boundaries [31] , [62] . However , psychophysical studies employing implicit contours [2] , [7] , [8] , in which foreground and background elements are present at equal density and which lack explicit texture cues , appear to rule out explanations that omit long-range , orientation-specific interactions . An influential class of biologically-inspired computer vision models achieves a degree of viewpoint-invariant object recognition by constructing feed-forward hierarchies to extract progressively more complex and viewpoint invariant features [33] , [63] . By analogy with such models , scale- and position-independent representations for detecting long , smooth contours could in principle be constructed hierarchically , starting with simple edge detectors and building up progressively longer , more complex curves using a “bag-of-features” approach . Presently , there appear to be insufficient data to decide whether human contour perception involves primarily lateral , feed-forward , or even top-down connections [30] , [32] , [64] . Hypothetically , the cortical association fields used in the present study could have been implemented as a feed-forward architecture , using a hierarchy of orientation-selective neurons to link progressively more widely separated contour fragments . Functionally , there may not exist a clean distinction between lateral , feed-forward and feed-back topologies , with the possibility that all three types of connectivity contribute to human contour perception . To quantify the temporal dynamics underlying visual processing , we performed speed-of-sight psychophysical experiments that required subjects to detect closed contours ( amoebas ) spanning a range of shapes , sizes and positions , whose smoothness could be adjusted parametrically by varying the number of radial frequencies ( with randomly chosen amplitudes ) . To better approximate natural viewing conditions , in which target objects usually appear against noisy backgrounds and both foreground and background objects consist of similar low-level visual features , our amoeba/no-amoeba task required amoeba targets to be distinguished from locally indistinguishable open contour fragments ( clutter ) . For amoeba targets consisting of only a few radial frequencies ( ) , human subjects were able to perform at close to accuracy after seeing target/distractor image pairs for less than ms , consistent with a number of studies showing that the recognition of unambiguous targets typically requires ms to reach asymptotic performance [22] , [23] , [35] , here likely aided by the high intrinsic saliency of closed shapes relative to open shapes [7] . Because mean inter-saccade intervals are also in the range of ms [34] , speed-of-sight studies indicate that unambiguous targets in most natural images can be recognized in a single glance . Similarly , we found that closed contours of low to moderate complexity readily “pop out” against background clutter , implying that such radial frequency patterns are processed in parallel , presumably by intrinsic cortical circuitry optimized for automatically extracting smooth , closed contours . As saccadic eye movements were unlikely to play a significant role for such brief presentations , it is unclear to what extent attentional mechanisms are relevant to the speed-of-sight amoeba/no-amoeba task . Our results further indicate that subjects perform no better than chance at SOAs shorter than approximately ms . Other studies , however , report above chance performance on unambiguous target detection tasks at similarly short SOA values [1] , [23] , [26] , [33] . The discrepancy may be attributed to the different masks employed . Whereas the above cited studies used masks consisting of either spatially filtered ( e . g . ) noise , distractor images , or scrambled versions of the target image set , we constructed rotation masks that were optimized for each target/distractor image pair [20] . Our working hypothesis was that an optimized mask should completely obscure the target object in the target-mask composite image; also referred to as pattern masking . The requirement that the mask completely hide the target follows from the assumption that at very short SOA , the target and mask images are likely to be effectively fused due to the finite response time of neurons and receptors in the early visual system [65] . For the amoeba/no-amoeba task , we created optimized masks by rotating the amoeba and clutter fragments with the goal of producing the maximum amount of interference in the responses of orientation-selective cells . Presumably , maximum interference occurs when orientation-selective neurons are presented with randomly rotated contour fragments in rapid succession . Although backward masks can have heterogeneous effects , with performance in some cases showing a -shaped dependence on SOA [66] , for the masks used here performance always increased monotonically with SOA . Empirically , the fact that performance was no better than chance at ms SOA suggests that our optimized masks were able to effectively interrupt the processing of smooth , closed contours at early cortical processing stages . Indeed , the ability to drive overall performance down to chance at SOA values shorter than ms could provide an operational criteria for assessing the degree to which a given backward pattern mask is able to effectively interrupt visual processing . The amoeba/no-amoeba task required the integration of information over length scales spanning viewing angles of approximately , larger than the classical excitatory receptive field size of parafoveal V1 neurons . The amoeba/no-amoeba image set ( see Figure 1 ) was configured so that purely local information , such as a few adjoining contour fragments , would not be sufficient to solve the target detection problem . Rather , distinguishing amoebas from clutter required integrating global information across multiple contour fragments . Our results suggest that such global integration can be accomplished via lateral interactions between local , orientation-selective filters . Although the density of target and clutter segments was not precisely equilibrated in our amoeba/no-amoeba image set , the wide range of target sizes and shapes spanned by our image generation algorithm makes it unlikely that the near perfect performance of human subjects at long SOA could have been attained using density cues alone [4] . Here , lateral inputs were used to modulate the bottom-up responses in a multiplicative fashion , so that our cortical association fields acted primarily as gates that suppressed contour fragments that did not receive sufficiently strong contextual support . By preventing lateral inputs from producing activity unless there was already a strong bottom-up input , a multiplicative non-linearity prevented the activation of contour fragments not present in the original image . The phenomenon of illusory contours suggests that in some cases contextual effects can produce activity even in the absence of a direct bottom-up response [30] . The precise form of the multiplicative interaction used here was adopted for algorithmic simplicity rather than for biological realism . We observed that including a small additive contribution from the lateral interactions did not fundamentally affect our conclusions . This suggests that ODD kernels , if implemented more generally , might account for the perception of illusory contours as well . However , a more realistic description of the underlying cellular and synaptic dynamics would likely be necessary to model a relaxation process that includes both additive and multiplicative elements . Both the model and the psychophysical experiments employed a 2AFC design ( see Figure 5 ) in which the goal was to correctly identify which of a pair of images contained an amoeba target . Since each trial involved a forced choice between two images , the model used a simple classifier that labeled the image with greater total activity as the target . For both human subjects and the model , the number of radial frequencies proved to be a good control parameter for adjusting task difficulty ( see Figure 7 ) . For targets of low to moderate complexity , both model performance ( as a function of number of iterations ) and human performance ( as a function of increasing SOA ) monotonically approached nearly perfect asymptotic performance as described by a single sigmoidal function with a characteristic scale , representing either time or number of iterations , that increased with ( see Figure 7 ) . Based on comparison with human performance at different SOA values , each iteration of the ODD kernels was estimated to require approximately ms of cortical processing time , consistent with measured conduction delays between laterally connected cortical neurons [13] . Prior to any lateral interactions , the stimulus was projected onto a retinotopic array of orientation-selective filter elements , providing a convenient representation for learning cortical association fields by computing differences in pairwise coactivation statistics between target and distractor images . We found that each iteration of the ODD kernel increased the activity of contour fragments that were part of amoebas compared to the activity of clutter fragments , so that after several iterations the mean overall activity , summed across all orientation-selective filter elements , was higher on average for target images than distractor images ( see Figure 4 ) . Even in trials that were incorrectly classified , contour fragments belonging to amoebas were typically still favored relative to background clutter . Because the total number of contour fragments varied from trial to trial , with only the average number of fragments being fixed across the entire image set , our relatively crude criterion for discriminating between target and distractor images sometimes led to classification errors even when amoeba fragments had been partially segmented from the background clutter , simply because the distractor image initially contained more fragments . A more sophisticated classifier might have led to a closer correspondence between model and human performance . Although performance of the present multiplicative model appeared to saturate after only a few iterations of the ODD kernel ( e . g . ) , it is possible that a different implementation might have continued to show improvements after additional iterations . However , the longer processing time implied by additional iterations suggests that other physiological mechanisms , particularly visual search , would likely come into play . Granted , there is an apparent mismatch between the fading of clutter elements in the model and the persistence of such elements perceptually in human subjects . To reconcile this apparent mismatch , it has been suggested that the initial perception of brightness might be driven by the initial bottom-up response of the individual orientation-selective feature detectors , whereas persistent responses across these same feature detectors might drive salience [28] . The amoeba/no-amoeba image set was designed to allow for parameterized complexity ( in terms of the amount of clutter , number of radial frequencies , etc . ) while avoiding reference to exogenous world knowledge . Since the amoeba/no-amoeba image set was machine generated , it was possible to produce a very large number of training images; target and distractor images at pixel resolution were used to train ODD kernels in the present study . Many computer vision systems employ standard image classification datasets such as the Caltech [67] , which allows for uniform benchmarking and thus facilitates direct comparison between models . Datasets based on natural images , however , suffer from several shortcomings . First , the resolution and number of images are fixed when the set is created . While some man-made datasets , such as MNIST [68] ) , consist of tens of thousands of handwritten characters , annotated sets of natural photographs ideal for speed-of-sight experiments are typically limited to a few hundred images . In contrast , humans are exposed to millions of natural scenes during visual development . Biologically motivated models that attempt to replicate human performance might require similar numbers of examples . A second shortcoming of natural image datasets is prevalence of high-level contextual information that utilizes exogenous world knowledge , such as the increased a priori likelihood of finding a car on a road , or an animal in a forest . Exploiting such exogenous world knowledge posses a formidable challenge for existing computational models and , on tasks that employ natural images , may obscure the ability of such models to extract behaviorally meaningful information from low-level visual cues . Third , natural image datasets typically provide limited capability for adjusting intrinsic task difficultly . For example , one widely used dataset [33] includes photographs of animals at different distances , but only a few discrete distances are annotated and the relationship of target distance to task difficultly is not easily quantified . Here , we illustrated how a synthetic set of images could be used to compare model and human performance in a task with parametric difficulty , potentially validating the use of artificial as opposed to natural images . The present study addressed the role of cortical association fields in the perception of closed contours , which are presumably important for detecting visual targets based on shape or outline . Although studies show that human subjects can rapidly distinguish between images containing target and non-target object categories using only the line drawings obtained by filtering natural scenes [1] , normal experience involves a number of complementary visual cues , such as texture , color , motion and stereopsis . Presumably , cortical association fields also act to reinforce features representing these complementary visual cues as well . Human subjects , for example , can distinguish whether pairs of texture patches were drawn from the same natural object or two different natural objects in a manner that exhibits a similar dependence on pairwise co-occurrence statistics as was found for orientated edges [55] . We may speculate that an analysis of coactivation statistics for features selective to a combination of cues such as local orientation , texture , color , motion , and disparity may lead to a more general and more powerful set of kernels capable of fast and effective determination of global object properties , which in turn can play an important role in complex object identification .
An amoeba is a type of radial frequency pattern [36] consisting of a deformed circle in which the radius varies as a function of the polar angle . By choosing the number and relative amplitudes of the different frequency components , the radius can describe an arbitrarily complex shape , exactly analogous to how a Fourier basis can be used to construct an arbitrary waveform on a finite interval . Each radial frequency component was represented by a sinusoidal function defined at discrete polar angles , spaced uniformly on the interval . The cutoff radial frequency used in constructing the closed contour provided a control parameter for regulating the complexity of the resulting figure , which ranged from nearly circular , when only the lowest radial frequencies had non-zero amplitudes , to highly sinusoidal and irregular , when the first radial frequencies had non-zero amplitudes . All amoeba shapes generated here may be considered smooth , in that local curvature was always bounded . In detail , the radius of an amoeba at each polar angle was: ( 2 ) All amplitudes were initially drawn from normal distributions with mean and unit variance . All phases were drawn from uniform distributions over the interval and . The resulting radial frequency pattern was then linearly rescaled so that the maximum radius , , was equal to a random number drawn from a uniform distribution such that , where is the linear size of the square image ( pixels ) , and the minimum radius was given by a second randomly chosen value so that . Uniform pseudo-random numbers were generated by the intrinsic MATLAB function RAND , or its Octave equivalent . To facilitate the construction of locally indistinguishable clutter and model contour occlusion in natural images , amoeba contours were divided into periodically-spaced fragments by removing short sections whose lengths varied within a specified range . Specifically , the gaps between amoeba fragments varied from to in units of discrete polar angle . Amoeba contours were then broken into fragments by periodically inserting gaps of variable width ranging from to , spaced segments apart . Gaps were deleted from the underlying contour , so that the polar angle subtended by each fragment varied in accordance with the changes in preceding gap width . The starting point of the first gap was chosen randomly on the interval , so that over the entire image set the inserted gaps were distributed uniformly around the circle . To create clutter fragments , an amoeba was first generated using the above procedure . Consecutive amoeba fragments were then grouped , with the number of fragments in each group determined by a Poisson process with a mean value of and an upper cutoff of . Each group of amoeba fragments was then rotated about its center of mass through random angles on the interval to . The resulting clutter consisted of the same fragments as the original amoeba but rotated so that collectively the rotated fragments no longer supported the perception of a closed object . Clutter fragments constructed in this manner were thus locally indistinguishable from amoeba fragments . To create clutter in both target and distractor images , several amoebas were first superimposed at random positions and then groups of fragments rotated following the procedure described above . All amoebas contained the same total number of contour fragments ( and therefore the same number of gaps ) but varied in both maximum diameter and total contour length . The center of each amoeba was chosen randomly under the restriction that no contour be allowed to cross an image boundary . Specifically , the -coordinate of the amoeba center , , was chosen randomly on a restricted interval , , and likewise for the -coordinate , . When groups of amoeba fragments were randomly rotated to make clutter , portions of a contour belonging to a clutter fragment would occasionally cross an image boundary . In such cases , any out-of-bounds portions of a contour were reflected back into the image region using mirror boundary conditions . Target images always consisted of set of amoeba fragments and sets of clutter fragments . Distractor images consisted of sets of clutter fragments and thus , averaged over the entire image set , had the same mean luminance and the same variance as the target images . Mask images were constructed following a procedure nearly identical to that used for constructing distractor images , except that mask images consisted of sets of clutter fragments , obtained by randomly rotating the original amoeba objects used in constructing the corresponding target and distractor images . All contour fragments were initially represented as a set of points in polar coordinates , corresponding to the radius at each discrete polar angle . Points along the contour were then transformed back to Cartesian coordinates and rounded to the nearest discrete pixel value . MATLAB scripts for generating the image set used in this study are publicly available at: http://petavision . sourceforge . net . The Los Alamos National Laboratory ( LANL ) Human Subjects Research Review Board ( HSRRB ) has reviewed the following experimental protocol and determined that it provides adequate safeguards for protecting the rights and welfare of human subjects involved in the protocol . The protocol was reviewed and approved in compliance with the U . S . Department of Health and Human Services ( DHHS ) regulations for the Protection of Human Subjects , 45 CFR 46 , and in accordance with the LANL Federal Wide Assurance ( FWA#00000362 ) with the National Institutes of Health/Office for Human Research Protections ( NIH/OHRP ) . The identification number is LANL 08-03 X . Human performance was evaluated using two-alternative forced choice ( 2AFC ) psychophysical experiments . There were subjects , all with normal or corrected-to-normal vision . One subject only contributed data for a portion of the tested SOAs . Each subject was seated in a dark room , at an approximate distance of cm from a -inch nominal ( cm actual size ) Hitachi CRT monitor . Images spanned a viewing angle of approximately . The monitor resolution was pixels and the refresh rate was Hz . The display was driven by a dual-core GHz Mac Pro , with MATLAB running Psychtoolbox [69] . After a short training period to familiarize the subject with the task , one target image and one distractor image were shown side by side , followed by a mask intended to interrupt cognitive processing of the target and distractor images . Two separate sets of experiments were conducted for each subject . In one set , the SOA was chosen randomly from the values ms . For the second set of experiments , the SOA was chosen randomly from the values ms . The duration of the stimulus was always the same as the SOA , and thus both the target and distractor images remained visible until mask onset . The duration of the mask was always ms . Each subject was shown images divided into blocks of images , with rest breaks in between blocks ( rest break duration was at the discretion of each subject ) . The pace of the experiment was under the control of the subject , who initiated each trial using the space bar . A small temporal jitter , chosen uniformly between to ms , was added to the interval preceding each trial , to prevent entrainment . Task conditions , consisting of variations in both the SOA and the number of radial frequencies , were randomly interleaved such that each condition occurred the same number of times over the course of the entire experiment . On each trial , subjects indicated which side contained the target , using a mouse-driven slider bar to report confidence ( see Figure 5 ) . The reported confidence values were used to construct receiver operating characteristic ( ROC ) curves , which plot the percentage of true positives ( or hits ) against the percentage of false positives ( or false alarms ) , with each true/false positive pair obtained by setting a confidence threshold at a different location along the slider bar . A correct response was not necessarily considered a true positive: to generate one point on the ROC curve , the reported confidence on each trial was measured relative to the current threshold position , which could be to either the left or to the right of center . Thus , a trial might be labeled as incorrect , even though the subject moved the slider bar in the correct direction , as long as the threshold level was not exceeded . Specifically , whenever the reported confidence fell to the left of threshold , the corresponding trial was treated as though the subject reported the target as being to the left , even if the threshold location had been set to the right of center and the confidence bar had actually been slid to the right . Likewise , when the reported confidence fell to the right of the current threshold position , the trial was always treated as if the subject had reported the target to the right , again regardless of how the subject moved the slider bar relative to the center position . By choosing a range of threshold positions , spanning the full range of reported confidence values , a complete ROC curve was obtained . Note that as the threshold was moved closer to the left edge of the slider bar , the percentage of true and false positives both approached minimum values , since only trials with very high reported confidence could contribute to either the true positive or false positive rate ( most trials were rejected as either true or false negatives ) . As the threshold position moved closer to the center of the confidence slider bar , the percentage of true positives increased . Finally , as the threshold was moved closer to the right edge of the slider bar , both the true positive rate and the percentage of false positives approached maximum values . The true positive rate averaged over all false positive rates , or the area under the ROC curve ( AUC ) , was used as an overall measure of subject performance . The AUC is equivalent to the probability that a randomly chosen target image will be correctly classified relative to a randomly chosen distractor image , and thus directly predicts performance on the 2AFC task . Results for each SOA and for each value of were averaged over subjects . Error bars denote the standard deviation over the 5 subjects . Model cortical association fields were based on differences in the coactivation statistics of orientation-selective filter elements drawn from target and distractor images . Geisler and Perry measured co-occurrence statistics for oriented edges in human segmented natural images [25] , and found a close correspondence to human judgments as to whether pairs of short line fragments were drawn from the same or different contours . Thus , we refer to the difference in coactivation statistics between target object and distractor images as Object-Distractor Difference ( ODD ) kernels . ODD kernels were trained using target and distractor images , each divided into sets of images each , with each set associated with a different value of . The order in which the images were presented had no bearing on the final form of the ODD kernel; that is , there was no temporal component to the training . Training with more images did not substantively improve performance , although small differences were observed in the ODD kernels trained using a smaller number of images ( target and distractor images ) . Each pixel training image activated a regular array of retinal elements whose outputs were either or , depending on whether the corresponding image pixel was ON or OFF , respectively . Each retinal unit activated a local neighborhood of orientation-selective filters , which spanned angles spaced uniformly between and . To mitigate aliasing effects , the orientation-selective filters were rotated by a small , fixed offset , equal to , relative to the axis of the training images . All orientation-selective filters were pixels in extent and consisted of a central excitatory subunit , represented by an elliptical Gaussian with a standard deviation of in the longest direction and an aspect ratio of , flanked by two inhibitory subunits whose shapes were identical to the central excitatory subunit but were offset by pixels in the direction orthogonal to the preferred axis . The weight , from a retinal element at to a filter element at with dominant orientation , was given by a sum over excitatory and inhibitory subunits: ( 3 ) where the position vector is given by and the matrix describes the shape of the elliptical Gaussian subunits for . In Eq . 3 , is a unitary rotation matrix , ( 4 ) and is a translation vector in the direction orthogonal to the dominant orientation when . The amplitude was determined empirically so that the total integrated strength of all excitatory connections made by each retinal unit equaled ( and thus the total strength of all inhibitory connections made by each retinal unit equaled ) . Mirror boundary conditions were used to mitigate edge effects . The retinal input to each orientation-selective filter element was then given by ( 5 ) where is the binary input image patch centered on . The sum is over all pixels that are part of this image patch . The initial output of each orientation-selective filter element was obtained by comparing the sum of its excitatory and inhibitory retinal input to a fixed threshold of . Values below threshold were set to whereas values above unity were set to . Thus ( 6 ) where the function , ( 7 ) is an element-wise implementation of these thresholds . The responses of all suprathreshold orientation-selective filters contributed to the coactivation statistics , with only the relative distance , direction , and orientation of filter pairs recorded . Because of the threshold condition , only the most active orientation-selective filters contributed to the coactivation statistics . For every suprathreshold filter element extracted from the -th target image , coactivation statistics were accumulated relative to all surrounding suprathreshold filter elements extracted from the same image . Thus the ODD kernel is given by ( 8 ) where the radial distance is a function of the coordinates of the two filter elements , the direction is the angle measured relative to , the sum is over all suprathreshold elements within a cutoff radius of , the superscript denotes the -th target image , and the difference in the orientations of the two filter elements is taken modulo . Because the amoeba/no-amoeba image set was translationally invariant and isotropic , the central filter element may without loss of generality be shifted and rotated to a canonical position and orientation , so that the dependence on may be omitted . The coactivation statistics for the -th target image can then be written simply as , where gives the distance and direction from the origin to the filter element with orientation , given that the filter element at the origin has orientation . An analogous expression gives the coactivation statistics for the -th distractor image . The ODD kernel is given by the difference ( 9 ) where the sums are taken over all target and distractor images and the normalization factors and are determined empirically so as to yield a total ODD strength of ( see Figure 8 and Results ) , defined as the sum over all ODD kernel elements arising from either the target or distractor components . By construction , the sum over all ODD kernel elements equals zero , so that the average lateral support for randomly distributed edge fragments would be neutral . Our results did not depend critically on the RMS magnitude of the ODD kernel ( see Figure 8 ) . To minimize storage requirements individual connection strengths were stored as unsigned 8-bit integers , so that the results of the present study did not depend on computation of high precision kernels . As described above , the canonical ODD kernel is defined relative to filter elements at the origin with orientation . Filter elements located away from the origin can be accounted for by a trivial translation . To account for filter elements with different orientations , separate ODD kernels were computed for all orientations then rotated to a common orientation and averaged to produce a canonical ODD kernel . The canonical kernel was then rotated in steps between and ( offset by ) and then interpolated to Cartesian axes by rounding to the nearest integer coordinates . Although it has been demonstrated that global contour saliency is enhanced for orientations along the cardinal axes [58] , this bias is by construction absent from this model . ODD kernels were used to compute lateral support for each orientation-selective filter element , via linear convolution . The output of each filter element was then modulated in a multiplicative fashion by the computed lateral support . The procedure was iterated by calculating new values for the lateral support , which were again used to modulate filter outputs in a multiplicative fashion: ( 10 ) where the subscript denotes the -th iteration . The same kernel was used for all iterations . All source code used to train and apply cortical association fields is publicly available at http://sourceforge . net/projects/petavision/ . To measure model performance , in each trial target image and distractor image were tested as a pair , so as to emulate the 2AFC format of the human experiments . The orientation-selective filter responses to both test images were evaluated after iterations of the ODD kernel . The total activation across all filter elements , , was used to compare the two test images . Since the model cortical association fields tended to support contour fragments belonging to amoebas while inhibiting clutter fragments , the image with higher total activation was assumed to be the target image . Error bars for the model performance ( as shown in Figure 7 ) were estimated using the standard deviation of a binomial distribution with probability equal to percent correct and equal to the number of trials . | Current computer vision algorithms reproducing the feed-forward features of the primate visual pathway still fall far behind the capabilities of human subjects in detecting objects in cluttered backgrounds . Here we investigate the possibility that recurrent lateral interactions , long hypothesized to form cortical association fields , can account for the dependence of object detection accuracy on shape complexity and image exposure time . Cortical association fields are thought to aid object detection by reinforcing global image features that cannot easily be detected by single neurons in feed-forward models . Our implementation uses the spatial arrangement , relative orientation , and continuity of putative contour elements to compute the lateral contextual support . We designed synthetic images that allowed us to control object shape and background clutter while eliminating unintentional cues to the presence of an otherwise hidden target . In contrast , real objects can vary uncontrollably in shape , are camouflaged to different degrees by background clutter , and are often associated with non-shape cues , making results using natural image sets difficult to interpret . Our computational model of cortical association fields matches many aspects of the time course and object detection accuracy of human subjects on statistically identical synthetic image sets . This implies that lateral interactions may selectively reinforce smooth object global boundaries . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"computational",
"neuroscience",
"biology",
"neuroscience"
] | 2011 | Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception |
Research has shown that RNA virus populations are highly variable , most likely due to low fidelity replication of RNA genomes . It is generally assumed that populations of DNA viruses will be less complex and show reduced variability when compared to RNA viruses . Here , we describe the use of high throughput sequencing for a genome wide study of viral populations from urine samples of neonates with congenital human cytomegalovirus ( HCMV ) infections . We show that HCMV intrahost genomic variability , both at the nucleotide and amino acid level , is comparable to many RNA viruses , including HIV . Within intrahost populations , we find evidence of selective sweeps that may have resulted from immune-mediated mechanisms . Similarly , genome wide , population genetic analyses suggest that positive selection has contributed to the divergence of the HCMV species from its most recent ancestor . These data provide evidence that HCMV , a virus with a large dsDNA genome , exists as a complex mixture of genome types in humans and offer insights into the evolution of the virus .
Human cytomegalovirus ( HCMV ) is member of the β-herpesvirus family . It is a ubiquitous , opportunistic pathogen , with seroprevalence of 30–90% in the United States [1] . In healthy individuals , primary HCMV infection is usually asymptomatic or can result in a mild febrile illness . However , infection persists throughout the life of the host . HCMV infections can be problematic for those with compromised or immature immune systems . For example , congenital HCMV infection is the leading cause of birth defects resulting from an infectious agent , affecting about 0 . 5% of all live births [2] and costing the U . S . Health care system ∼$2 billion annually [3] . Long term sequelae of congenital HCMV infections include deafness , blindness and/or mental disability [4] . HCMV contains the largest genome of any human virus with a dsDNA genome of ∼236 kilobase pairs [5] . Sequence analysis predicts that the genome encodes approximately 164 open readings frames ( ORFs ) [6] . The genome contains two unique regions ( termed UL and US ) that are flanked by repeats ( termed RL and RS ) both internally and terminally , although the internal RL region is not present in clinical isolates or low passage strains . Previous work with cell culture passed virus has shown that the genome of HCMV displays sequence variability . For example , the laboratory strain AD169 is a highly passaged , attenuated variant . The genome of AD169 as compared to low passage strains has an approximately 15 kb deletion which encodes an additional 19 or 22 open ORFs , referred to as the UL/b' region [6] , [7] , [8] . Approximately 20 ORFs of HCMV have been shown to exhibit nucleotide variability when sequenced from infected hosts [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . These studies have often focused on the variability of ORFs encoding envelope glycoproteins or ORFs of UL/b' , which are thought to be important for pathogenesis . As examples , UL55 and UL73 , encoding the gB and gN glycoproteins , respectively , commonly exist as one of 4 genotypes , with less common genotypes also identified [19] , [20] . In the UL/b' region , UL144 , encoding a TNF-α receptor [21] , and UL146 and UL147 , encoding α-chemokines [22] , also show significant variability among hosts [9] , [14] , [23] , [24] , [25] . Although it is known that HCMV is polymorphic among hosts , the source of the variability remains unresolved . There are at least two possibilities to explain the observation . The first is that de novo mutations arise upon introduction into a new host , resulting in a unique strain for each individual . The second possibility is that multiple HCMV genotypes exist within each host , and infection into a new host represents a selection event whereby a new dominant genotype is selected for and detected in subsequent assays . In support of this model , others have found evidence of mixed genotype populations at the few loci examined . Mixed populations have been observed when measuring gB genotypes [26] , [27] , [28] , [29] , [30] , though the phenomenon has also been shown for other ORFs , such as gN , gO , gH , gL , UL139 , and UL146 [31] , [32] , [33] , [34] , [35] , [36] , [37] . Furthermore , mixed populations have been shown in a range of patient populations , including immunocompetent , asymptomatic adults [31] and have been shown at multiple loci simultaneously [33] . While definitive relationships between genotypes and diseases are lacking , there is mounting evidence that mixed genotype infections serve as markers of severe or prolonged complications from HCMV infections [26] , [27] , [29] , [30] , [34] , [36] . A shortcoming of the mixed genotype studies has been limited coverage of the HCMV genome . To our knowledge , less than 5% of the HCMV genome has been sequenced from clinical specimens in these types of studies ( Figure S1 ) . Thus , a remaining question is whether HCMV diversity is limited to a subset of ORFs or is found throughout the genome . From earlier studies , it appears that HCMV may exist as a mixture of genotypes . Due to limitations of previous technology , it was unrealistic to study mixed HCMV populations to great depth or sequence the HCMV genome to high coverage . To address these shortcomings , we have adapted high throughput sequencing to sample many members of the HCMV genomic population , rather than just a dominant member . With the improved output of next generation sequencing , we were able to take a genome wide approach and sequence thousands of HCMV genome equivalents from each patient sample . Here we sampled the HCMV genomic populations present in urine samples collected from three congenitally infected newborns . These data reveal a high level of intrahost variability and offer strong evidence that HCMV exists as a complex mixture of variants . We also found evidence of selection at both the intrahost and interhost levels , highlighting evolutionary forces that shape the HCMV genome . These results greatly improve our understanding of the structure of HCMV populations in humans , and have important implications for the study of DNA viruses .
In clinical samples , HCMV DNA represents a very low proportion of the total DNA . Thus , direct sequencing would yield a low depth of the HCMV population with human DNA being a major source of contaminant . Because there is homology between the human and HCMV genomes [38] , [39] , this contaminant would be problematic in downstream sequence analyses . We developed a series of approximately 70 long range , overlapping PCR reactions to selectively amplify the entire HCMV genome . However , PCR amplification can introduce errors of its own , which could be misinterpreted as polymorphisms . To assess the error associated with sample processing , we resequenced BACs that contained the genomes of the HCMV strains AD169 and Toledo . The BACs have been shotgun sequenced to a 10X depth [40] , producing reliable reference sequences for these purposes . The BAC DNA was amplified through a series of PCR reactions and sequenced on the Illumina GA II paired end platform . The sequence output was equivalent to ∼220 genomes per strain ( Table 1 ) . The sequence reads were aligned to the appropriate reference sequence and the alignments were analyzed for errors . We assumed that all mismatches between the sequencing reads and the reference sequence were errors introduced by either PCR or sequencing . This assumption is most likely conservative because there is the possibility that variants were created by propagating the BACs in E . coli or that errors could be present in the reference sequences . The alignment data contained in the pileup file was then processed with a variant filtering program . The variant filtering program only outputs variants that are above threshold values for basecall quality , mapping quality , depth at the position , number of occurrences of the same variant and frequency of the variant in the data . The thresholds used were: basecall quality ≥30 , mapping quality ≥89 , depth ≥15 , number of occurrences ≥3 , and frequency ≥ . 019 . The basecall quality and mapping quality values are used to filter nucleotides with low confidence from sequencing or from reads that align with low confidence , respectively . Depth , number of occurrences of variant and frequency are used to remove likely errors because random errors ( from either sample amplification or sequencing ) have the highest likelihood of occurring as singletons and doubletons ( 1 or 2 occurrences ) . These threshold values were chosen by training the filtering program with BAC resequencing data . The resequencing data from AD169 and Toledo were mixed in various ratios to model a mixed population . The filtering thresholds were selected to increase specificity of detecting true variants; however , they carry a penalty of reducing sensitivity and underestimating the amount of variants in the sample ( Table S1 ) . The number of false positives remains low at various depths and mixtures of the sequences . We did not find evidence of amplification-induced skewing of variant frequencies . Further discussion of analysis of error can be found under Materials and Methods and Supplemental Information . We sampled HCMV genomic populations present in the urine collected from 3 HCMV-positive neonates within 2 weeks of birth ( identified as U01 , U04 , and U33 ) . The entire HCMV genome was amplified as discussed above . The PCR reactions and amount of template DNA were identical between the BAC resequencing and the clinical sequencing . Therefore , the error filtering protocol developed through BAC resequencing can be applied to the clinical sequence data . From clinical sequencing , >300 megabases of output per sample yielded an average depth of 1843 genome equivalents and an average genome coverage of 97 . 8% for the 3 samples ( Table 1 and Figure S3 ) . Initially , the sequence reads from the urine samples were aligned to the sequence of the Merlin strain , which was used as the HCMV reference genome ( Ref Seq ID: NC_006273 ) . From the alignment , >104 single nucleotide variants ( range: 11289–15709 ) were detected per viral population . Variants segregated into clusters at frequencies ≤ . 1 or ≥ . 9 ( Figure 1 ) . Variants with frequency ≤ . 1 represent on average 73% ( Range 67%–78% ) of the total and variants with frequency ≥ . 9 represent 20% ( Range: 16%–24% ) . From these data , we conclude that the high frequency variants result from the major alleles found in the viral population while the low frequency variants result from the minor alleles . To study HCMV intrahost variability , we defined the major HCMV genome type of each sample and called intrahost variants from this reference genome type ( Figure S4 ) . A genome type is the genome wide analog of a genotype [41] , [42] , [43] . The major genome type contains the major allele found at every position of the genome . Thus , any variants from this genome type represent minor alleles or minor variants . It should be noted that the genome type may not represent any single DNA molecule in the viral population . Rather , the major genome type is a computational tool that allows for the detection of minor variants in the population , and every position of the genome in this analysis and all later analyses are treated independently ( i . e . unlinked ) . To define the major genome type , output from an initial alignment to Merlin was used to detect variants with frequencies >0 . 5 ( Figure S4 ) . These variants were interpreted to represent the major allele of the sample at each position . Variants were incorporated into the reference sequence to create an initial sample-specific genome type . Reads that did not initially align were used as substrate for de novo contiguous sequence ( contigs ) assembly . The contigs were aligned to the initial sample-specific genome type and incorporated into the genome if sequence identity was found . This modified genome type was used to serve as the reference sequence for another round of alignment of the sequencing reads and subsequent incorporation of high frequency variants and assembly of contigs onto the sample specific genome type . This process of constructing a sample-specific genome type was repeated until no additional reads were aligned between rounds of building the genome type ( usually 4 rounds ) . At the end of the process , a single sequence was produced that represents the sample-specific genome type and contains the major nucleotide of the sample at every position of the genome . Lastly , the sequence reads were aligned to this genome type , and the alignment was used to call intrahost variants and to quantify intrahost diversity . Intrahost variants were classified by ORF to quantify both intergenic and genome wide variability ( Table 2 , Table S3 and Figure 2 ) . There were >8 , 500 intrahost variants in each sampled population . ( Range: 8 , 562–13 , 335 ) ( Table 2 ) , and ∼91% of the variants were present at frequencies <0 . 1 . We compared the levels of variants from clinical sequencing and BAC resequencing to determine the level of false positives or errors within the clinical data . The false positive rate was reduced to 6 . 7% with filtering ( Figure S2 ) . Our initial analysis of the intrahost variability focused on the ORFs encoding the glycoproteins , gB ( UL55 ) and gN ( UL73 ) . These ORFs have well defined genotype classifications [19] , [20] and previous studies have shown mixed genotype populations for these ORFs [27] , [44] . Full genotypes cannot be determined using short read sequencing because linkage information is lost between regions larger than a sequence read ( i . e . 72 nt in this work ) . We analyzed the presence and frequency of amino acid variants that are markers of gB or gN genotypes as a substitute for full-length genotype data . For example , at position 181 of gB , a lysine is unique to the gB2 genotype and an arginine is unique to gB3 [19] . K181 or R181 within gB serves as a marker of these two genotypes . The frequency of these markers is the inferred frequency of the full-length genotype . We determined that mixed genotype populations existed for the gB ( UL55 ) and gN ( UL73 ) loci in congenitally infected infants in agreement with previous studies [27] , [44] ( Table 3 ) . However , these data represent ∼0 . 5% of the HCMV genome , and led us to determine whether evidence of mixed populations exists throughout the genome . To further define the intrahost diversity of HCMV populations , we first analyzed the genome wide data at the nucleotide level . We used the measures of nucleotide diversity ( π ) [45] and mean diversity [46] , which were calculated as averages for all ORFs of the HCMV genome ( Tables 2 and S3 and Figure 2 ) . π is the average pairwise distance of sequences in the population , and mean diversity is the percentage of variant sequence within the population . The genome wide average for π for the 3 samples was 0 . 22% ( Range: 0 . 18%–0 . 25% ) . As a point of comparison , this value is similar to the genome wide π for HIV [47] and the single ORF intrahost π of other RNA viruses , such as hepatitis C , dengue , and West Nile [48] , [49] , [50] , [51] ( Figure 3 and Table S4 ) . Single ORF intrahost π was as high as 0 . 64% for HCMV . The HCMV genome wide mean diversity was 0 . 20% ( Range: 0 . 17%–0 . 22 ) and is similar to that of HIV-1 and dengue virus [46] , [50] . Figure 2 also reveals that intrahost diversity was not limited to a few loci but was found within most ORFs . The ORFs encoding gB ( UL55 ) and gN ( UL73 ) were in the 32nd and 20th percentile for ORF intrahost diversity , respectively , ( Table S3 ) and do not reflect the genome wide diversity . Therefore , HCMV populations are variable and using unbiased , genome wide data for studying that diversity offers an advantage over previous techniques that have focused on a limited set of loci . We grouped ORFs by gene product function or expression kinetics using the classification of Sylwester et al . [52] to further investigate the patterns of intrahost diversity ( Figures 4 and S6 ) . However , there was considerable variation of sequencing depth of some ORFs ( Table S3 ) raising the possibility that uneven sequencing depths could influence this analysis of diversity . Indeed , there was a correlation between nucleotide diversity of an ORF and the extremes of sequencing depth ( Figures S5A ) . To reduce the influence of excessive depth on the analysis , we focused on ORFs with sequencing depths between 15 and 1200 ( n = 338 ) ( Figure S5B ) . In this range , the influence of depth on nucleotide diversity will be ∼ . 01% , which is approximately the level of noise generated from errors in BAC resequencing . After selecting for ORFs sequenced to depths within this range , we did not observe significant difference nucleotide diversity across expression class . However , we did find a statistically significant association between ORF function and intrahost nucleotide diversity ( p < . 0001 ) ( Figure 4 and S6 ) . ORFs encoding glycoproteins showed a reduced level of intrahost nucleotide diversity . This latter result was unexpected given that glycoproteins were the most frequently analyzed in earlier studies of intrahost variability . To confirm the results obtained via high throughput sequencing , we assayed for π and genotype distribution by clonal Sanger sequencing of three highly variable ORFs in each patient sample . We found that the major genotype detected in both methods is the same ( data not shown ) . Also , the values for π determined by both high throughput and Sanger sequencing were generally similar for each ORF ( Table 4 ) . Clonal Sanger sequencing of these ORFs revealed a high density of unique genotypes in the clinical samples , with as many as 13 unique genotypes from 20 clones . The Sanger sequence data was also used to generate unrooted phylogenetic trees ( Figures 5 and S7 ) . Within the trees , we have included major genotype sequence data from the other patient samples in this study to provide perspective on the diversity of the clones . In some Sanger datasets , the diversity of clones could be explained by one or two mutational steps from the major genotype ( Figure 5A ) . Other datasets revealed clones within a patient sample that were more divergent than sequences among patient samples ( Figure 5B ) . This result could represent a highly mutagenic viral population , a co-infection with two or more strains , mixtures of viral variants from different compartments , or a combination of these mechanisms . An interesting side note is that , in a single patient sample , there is evidence for diversity from a few mutational events ( Figure 5C ) , and possible evidence of co-infections ( Figure 5D ) . Thus , the mechanism ( s ) that leads to the diversity of HCMV populations may be complex . Because the coding sequence of HCMV populations appeared to be highly variable , we next investigated whether there were differences in variability between coding and non-coding regions of the genome . For this analysis , coding regions were defined as protein coding sequences , and non-coding regions comprised the remainder of the genome . Thus , the non-coding regions likely contain functionally important sequences due to the inclusion of regions such as the origin of replication , transcription factor binding sites and miRNA sequences . Using these parameters , we found that there was a statistically significant difference between intrahost diversity of the coding and non-coding regions ( Table 5 ) . The coding regions had higher nucleotide and mean diversity values than the non-coding regions; however , the average frequency of coding variants was significantly less than the average frequency of non-coding variants . Although the differences in values for these summary statistics are small , as seen in the U04 population , it should be noted that coding and non-coding variants are interspersed across the genome . Thus , this proximity should allow for statistical robustness and may reflect a fine-scale mechanism regulating the amount and frequency of coding and non-coding variants . We next investigated the clinical HCMV populations at the amino acid level . The average intrahost amino acid diversity ( πAA ) was 0 . 18% ( Table 2 ) , which is comparable to RNA viruses such as dengue and West Nile [48] , [50] . The diversity at nonsynonymous sites ( πAA ) was ∼3-fold higher than at synonymous sites ( πSYN ) , suggestive of a slight excess of nonsynonymous mutations within the HCMV populations . The genome wide average for the percentage of amino acid sites that exhibited intrahost variability was 13 . 4% ( Range: 12 . 3%–14 . 0% ) ( Table 2 ) . This value reveals the substantial variation in intrahost coding potential of HCMV populations . Taken together , these data support a model of HCMV existing as diverse populations at both the nucleotide and amino acid levels . This result is novel for a large dsDNA virus , which encodes a DNA polymerase with exonuclease activity [53] . Having found significant levels of intrahost variability , we felt it was important to determine whether the patterns in variability were the result of genetic drift ( i . e . neutrality ) or if selection could explain the observed variant frequency patterns in the populations . We applied the model of Nielsen et al [54] to detect selective sweeps within the genome wide variant data . Selective sweeps are caused by positive selection and result in reduced variability around the region under selection [55] , [56] . Importantly , the test of Nielsen et al is robust to demographic effects . This is a critical function because the HCMV populations under study have most likely undergone significant recent demographic changes , such as population bottlenecks and expansions associated with primary infection . The Nielsen approach is an outlier test that calculates the likelihood of a selective sweep based on the distribution of variant frequencies within a region as compared to the genome as a whole . The composite likelihood ratio ( CLR ) of the region is a measure of this comparison , with higher CLR values indicating the region is a more extreme outlier and thus , more likely a target of positive selection . Applying the model of Nielsen et al to the HCMV genome wide data , we identified an average of 9 ORFs per population ( Range: 2–15 ) under statistically significant positive selection ( Figures 6 and S8 and Table S5 ) , including UL83 ( pp65 ) and UL123 ( IE1 ) . While there was no overlap between the positive selected ORFs in the three samples , there was evidence of overlap in protein function . For example , UL102 in the U01 sample and UL105 in the U04 sample were targets of selective sweeps , and protein products of both ORFs are subunits of the helicase-primase complex . Many of the ORFs highlighted in this analysis have either poorly defined or no known function . The generation of HCMV sequence data from urine specimens allowed for genome wide analysis of interhost polymorphisms across clinical samples , as opposed to those observed in laboratory passaged strains . For this analysis , polymorphisms were defined as variants from the HCMV reference sequence with frequencies >0 . 5 , and are the same class of variants previously incorporated into a sample specific genome type . By resequencing HCMV BACs , we determined that the error rate for calling polymorphisms is 0 . 028% . , i . e . , ∼65 erroneous polymorphisms are called within a 236 , 000 bp genome type ( Table S2 ) . On average , there were ∼2600 polymorphisms per genome type resulting in an interhost variability of 1 . 1% at either the nucleotide or amino acid level ( Table 6 ) . Only 7 . 9% ( 612 of 7 , 780 ) of the nucleotide polymorphisms and 1 . 2% ( 25 of 2 , 129 ) of the amino acid polymorphisms were common among the 3 samples . This result shows that most of the polymorphisms are not only different between clinical populations and a laboratory passaged strain ( Merlin ) , but they appear to be uniquely associated with the specific environments of the viral populations . Thus , these findings are consistent with previous work showing diversity of the HCMV species [5] . Next , we wanted to determine whether there is evidence of selection within the interhost sequence data . Previously , single ORFs of the HCMV genome have exhibited dN/dS ratios of less than 1 [57] , [58] , suggestive of negative selection . Using the genomic data , we calculated dN/dS values for all ORFs of the HCMV genome and also calculated a genome wide average . In agreement with previous studies [57] , [58] , the genome wide average dN/dS values were significantly below 1 ( p <0 . 0001 , G-test ) ( Table 6 , Table S6 and Figure 7 ) . Approximately 5% of ORFs exhibited dN/dS values greater than 1 , which is suggestive of positive selection . To find patterns in the genome wide dN/dS values , ORFs were classified according to protein product function and expression kinetics ( Figures 8 and S9 ) . No significant association was seen between dN/dS and expression kinetics , but a highly significant association was observed between protein product function and dN/dS ( p = 0 . 0002 ) . Envelope proteins exhibited elevated dN/dS values and DNA replication proteins showed low dN/dS values ( Figure 8 ) . We next used the McDonald-Kreitman ( MK ) test on the clinical sequence data to further analyze selective pressures . The input data for the MK test are the divergent ( i . e . interspecies ) nonsynonymous ( DN ) and synonymous ( DS ) mutations and the polymorphic ( i . e . intraspecies ) nonsynonymous ( PN ) and synonymous ( PS ) mutations [59] . Due to the inclusion of both polymorphic and divergent mutations , the MK test is a more sensitive test for selection than the dN/dS statistic . A 2x2 contingency table of the values is used to calculate significance of the mutational pattern and the respective ratios provide information regarding the direction of the test rejection . For example , positive selection is generally regarded to result in a ( DN/DS ) / ( PN/PS ) ratio >1 , while negative selection results in a ratio <1 . A genome wide MK test was performed using sequences of all orthologous ORFs ( n = 160 ) from Merlin and the three clinical samples with the inclusion of chimpanzee cytomegalovirus ( CCMV ) as the outgroup . Approximately 65% ( n = 104 ) of ORFs were scored as neutral in this test . ORFs yielding ( DN/DS ) / ( PN/PS ) ratios significantly >1 were ∼4-fold more frequent than ORFs producing ratios significantly <1 ( n = 45 and n = 11 , respectively ) ( Table S7 ) . This pattern could result from positive selection . However , considering the statistically robust , non-neutral dN/dS values , there is also widespread evidence of pervasive negative selection . Taken together , the results suggest that positive selection has driven the fixation of HCMV-specific mutations , and contributed to the divergence of the HCMV and CCMV species . However , demographic effects could also contribute to the observed mutational patterns and cannot be completely ruled out from these analyses , though considering inter-digitated synonymous and nonsynonymous sites ought to allow for a robust statistic .
High throughput sequencing has dramatically increased the number of genomes sequenced and is a useful tool for analyzing populations present within various environments . Our work represents the first use of high throughput sequencing technology to study the intrahost genomic populations of a large DNA virus in clinical samples . We observed substantial intrahost variability that was found throughout the HCMV genome and found evidence of selection both at the intrahost and interhost levels . An unexpected finding of this study was that almost every ORF of the HCMV genome showed some level of intrahost diversity in the three populations that were sampled . Thus , these results are an important extension of previous work that has revealed intrahost diversity within a small number of ORFs , including gB and gN [27] , [44] . However , the present data suggest that genotyping may not be a reliable surrogate for measures of HCMV diversity in clinical specimens . For example , the gB and gN genotype data in Table 3 suggest that sample U01 is genetically the most diverse and U04 is the least diverse . However , Table 2 shows the opposite to be true . U01 is the least diverse and U04 is the most diverse for HCMV on genome wide scales . By quantitating variability using the measure of nucleotide diversity , it can be seen how the intrahost diversity of HCMV is comparable to those of RNA viruses , including HIV . The similarity in values is striking considering the common assumption that RNA viruses exist in more highly diverse populations than DNA viruses due to the lower replication fidelity of RNA genomes . Thus , this work leads to a questioning of the source of the diversity observed in HCMV populations . One possibility is the prevalence of high mutation rates during replication of viral DNA genomes , similar to RNA viruses . This possibility does not seem likely considering that HCMV encodes a DNA polymerase with proofreading activity [53] . A second possibility is low mutation rates but high levels of replication , leading to an accumulation of mutations . In support of this model , it is suspected that only a single or very few virions cross the placenta to initiate a congenital infection . At the time of collection ( <2 weeks postnatally ) , the samples contained ∼107 HCMV genome copies per mL of urine ( data not shown ) . Thus , there had been many rounds of recent replication within the new host before the populations were sampled , which could lead to the accumulation of many variants even with a low mutation rate . Alternatively , the diversity could result from re-infection or co-infection . The phylogenetic trees of select ORFs ( Figure 5 and S7 ) suggest that some ORFs are highly divergent from a central population of genotypes , which suggests re/co-infection events . However , phylogenetic trees for other ORFs reveal highly similar clones . More experiments are needed to sort out these possibilities . Although the source of diversity is currently unclear , the existence of high intrahost diversity does lead to models of HCMV evolution . Creation of de novo mutations is stochastic and most likely occurs rarely , as suggested by the proofreading DNA polymerase encoded by HCMV . A high level of standing or pre-existing variation means that a pool of variants exists prior to the introduction of a new selective pressure . A low frequency variant ( s ) could quickly rise to high frequency because the selection coefficient of this allele could be increased under the new environmental conditions . Thus , diversity should offer a rapid mechanism of evolution for the virus in an environment of changing selective pressures . Alternatively , the low frequency variants could simply represent non-functional genomes or be reduced in frequency by negative selection . Data showing that the frequency of variants in coding regions is significantly lower than the frequency of variants in non-coding regions of the viral genome ( Table 5 ) are consistent with this explanation . Again , it is possible that changing selective pressures could reverse this effect and cause a change in frequency of these variants . Future experiments should test the effect of changing selective pressures on the frequency of pre-existing variants in the population . Analysis of the sequence data revealed evidence of selection within the viral populations . The results of the selective sweep analysis ( Figure 6 and Table S5 ) are intriguing in the context of host-pathogen dynamics . Both UL123 , encoding IE1 , and UL83 , encoding pp65 , were found to be within regions of selective sweeps in one patient sample ( U04 ) . These proteins are demonstrated targets of CD8+ T cells in neonates with congenital infection [60] and suggest an immune-mediated mechanism of selection . This is the first evidence that known HCMV immune targets are also targets of positive selection . The selective sweep analysis also detected many ORFs with no known function . Whether these ORFs are under immune selection or are targets of positive selection for other reasons , such as tropism or viral replication , is still unknown . We found evidence of both positive and negative selection within the genome when comparing interhost variation . The results suggest a model in which positive selection contributed to the divergence across the HCMV species , but genetic stability of the viral species is maintained with negative selection . Contrasting these long term selective forces to the observed high level of standing variation of the intrahost populations may lead to a clearer interpretation of the results . As mentioned above , the standing variation potentially reduces the time of adaptation to a novel environment or pressure . However , the negative selection acting on the variants may balance this phenomenon and prevent deleterious mutations from reducing the fitness of the overall HCMV species . Two groups have recently reported using high throughput sequencing to study HCMV from clinical material . In the report by Cunningham et al [61] , a major genome type sequence was generated from clinical material . In contrast , Gorzer et al [44] . studied genetic populations at three loci . These approaches are complementary to that presented here in which we sequenced HCMV populations on genome wide scales . As compared to the work of Cunningham et al , our study requires PCR amplification to select for HCMV DNA , which produces more HCMV-specific sequence data on a single sequencing run and greater depth of the viral population . This increased sequencing depth allows for a more accurate detection of minor variants within the population ( Table S1 ) . However , the approach by Cunningham et al differs from ours in that it allows for a more rapid sequencing of the major genome type , thereby producing greater sequence information about the HCMV species . In contrast , Gorzer et al sequenced three loci of the HCMV genome to a greater depth than our study , leading to higher levels of confidence in detecting minor and rare variants . However , our use of a genome-wide approach allows for unbiased detection of variability . As proof of the power of this approach , a commonly studied variable ORF , such as UL73 ( gN ) , is in the lowest quintile for intrahost diversity , while many of the ORFs with the highest intrahost diversity have not been studied for variability . Therefore , a genome-wide study can highlight loci for future studies using ultra-deep sequencing . The results presented here suggest that diversity of DNA virus populations should be studied more thoroughly to determine the universality of the high level of variability . For example , in this study we sampled HCMV populations from urine of congenitally infected children . It is unknown if the genomic populations sampled from urine are representative of the populations in other compartments of the host . Also , the levels of replication during congenital infections are very high , such that the diversity observed in asymptomatic , adult hosts may be much lower due to lower levels of replication and , therefore , fewer opportunities for mutagenesis . Alternatively , the chance of co- or re-infection in adults is much higher , possibly leading to more diverse populations . Others have shown that Marek's disease , another herpesvirus , virus exists as a collection of mixed genotypes in culture [62] . Thus , there is evidence of a similar phenomenon . Whether high diversity , mixed genotype populations exist for other herpesviruses or other dsDNA viruses outside of this family remains to be seen .
Clinical specimens were obtained from neonates with congenital HCMV infection and de-identified prior to receipt by the investigators . Specimens were gathered as part of a standard clinical procedure . None of the investigators were involved in specimen collection . The use of these specimens for research was approved by the University of Massachusetts Medical School Institutional Review Board ( IRB Docket # 10778 ) . Neonates within two weeks of age were diagnosed with congenital HCMV infection at the request of their respective care providers . The University of Massachusetts Memorial Health Center clinical virology laboratory performed diagnostic virus isolation . De-identified urine samples were then used for this study . No clinical information about the infants was available . Samples were stored at −80°C until DNA purification . DNA was purified using a Qiagen Blood and Tissue Kit using the standard protocol . HCMV BAC DNA has been described previously [40] and was kindly provided by Tom Shenk ( Princeton University ) . Isolation of BAC DNA from E . coli strains was performed as described [63] . We constructed a set of primer pairs spanning the entire HCMV genome . Primers were designed to anneal to conserved sites of the HCMV genomes , based on publicly available HCMV sequences . These databases included the sequence of an HCMV genome type ( Strain 3157 ) that was produced directly from clinical material without amplification [61] . Primer homology with this strain supports the assertion that the chosen sites are found in wild type strains , and will reduce primer mismatch bias . Amplicons overlapped by ∼100–500 bp such that sequence was generated at primer binding sites from the adjoining amplicon . Using this overlap data , primers were reevaluated and redesigned primers as necessary , given that these new data potentially represent thousands of unique HCMV genomes per experiment . Lastly , primers were designed to have no or low homology to both human sequence and any other possible contaminating DNA sources , such as other herpesviruses or common human parasites and commensal bacteria . Most amplicons were ∼6 kilobases ( kb ) . Some were reduced to 3 kb if the original longer amplicon either gave no/weak amplification or non-specific products as determined by Sanger sequencing . Primer sequences used in this study are listed in Table S8 . For BAC and clinical sample PCR amplification , initial PCR reactions were carried out using serially diluted templates to determine the lowest quantity necessary for efficient amplification . Quantitative PCR was performed using primers and probes described previously [64] and it was determined that each reaction contained ∼1300 HCMV genomes . The conditions for PCR were as follows: 1X PfuUltra II PCR buffer , 0 . 25 mM each dNTP ( NEB ) , . 25 uM each primer ( IDT DNA ) , 0 . 5 uL PfuUltra II Polymerase ( Agilent ) and 1 M betaine . A touchdown PCR was run on an Eppendorf Mastercycler ep gradient S with the following program for all reactions: 98°C for 2 min , 5 cycles of 98°C for 30 s , 63°C ( decreasing by 1°/cycle ) for 30 s , 72°C for 2 min , followed by 25 cycles of 98°C for 30 s , 58°C for 30 s and 72°C for 2 min , with a 10 min final extension at 72°C . All amplified products were size-selected on agarose gels and gel purified . Because insertions or deletions could produce amplicons of visibly different sizes than expected , we used direct Sanger sequencing of questionable amplicons to test for presence of the expected HCMV sequence . After amplification of the HCMV genome , all amplicons were quantified on a Nanodrop 1000 , pooled in equimolar proportions and used as substrate in Illumina sequencing . The DNA in pooled amplicons was sheared by sonication on a Sonic Dismembrator 550 ( Fisher ) until the median size was ∼350 bp . The DNA library was prepared as stated previously [65] . Briefly , DNA was end-repaired using the End-Repair Enzyme Mix ( NEB ) , and A-tailed using the ATP and Klenow ( exo- ) ( NEB ) . Adapters with appropriate barcodes were ligated onto the modified DNA ends . The library was then size selected on a 2% agarose gel , to produce a library with a median size of 350 bp+/−50 bp . The library was amplified with Illumina primers ( P/N 1003454 ) ( www . illumina . com ) . Once prepared , the libraries were combined in appropriate ratios and submitted for paired-end sequencing on the Illumina GAII . A Toledo strain amplicon set was included as an internal control for measuring error rates . HCMV BAC DNAs of the AD169 and Toledo strains were PCR amplified and processed for sequencing as described above . The barcoded DNAs were then sequenced on a single lane of the Illumina GAII . Output sequences from the Illumina GAII were first converted from Illumina FASTQ format to Sanger standard FASTQ and were then separated based on barcode sequences , which were subsequently trimmed before subsequent processing . The sequences were then aligned to either the AD169 BAC ( GenBank # AC146999 ) or Toledo BAC ( GenBank # AC146905 ) using Novoalign ( Novocraft ) . The alignment data were then ported to MAQ through the Novo2MAQ utility ( Novocraft ) and downstream analyses were performed with the MAQ software suite [66] . The pileup output from the alignment was then analyzed to call any mismatches between the sequence reads and the reference genome . All mismatches from this output have an associated basecall quality , mapping quality , local depth , number of mismatch occurrences and mismatch frequency . The basecall quality and mapping quality are calculated by the sequencing and alignment software , respectively . We used HCMV-BACs as templates for PCR amplification and paired-end sequencing on the Illumina GAII to develop an algorithm that would reduce error . The output was 108 megabases of HCMV sequence or the equivalent of approximately 466 HCMV genomes ( Table 1 ) . The data were aligned to the appropriate reference genome using Novoalign and MAQ . Using these data , we developed a variant filtering algorithm . This algorithm has been designed to filter the mismatch output from the alignment stage and aid in sorting “true” variants in the viral population from those mismatches created by PCR or sequencing errors . We produced in silico models of mixed viral populations in which the AD169:Toledo ratio was 1:1 , 1:10 , 1:100 , 1:200 , and 1:1000 . Thresholds for minimum basecall quality ( ≥30 ) , mapping quality ( ≥89 ) , depth ( ≥15 ) , mismatch count ( ≥3 ) and mismtach frequency ( ≥0 . 019 ) were found to minimize false positives . With these conservative thresholds , we had a detection rate of up to 75% , suggesting that the variants detected in clinical samples will under-represent the true level of variation in the populations . However , the number of false positives was very low in these in silico experiments even when the input minor genome was 1% of the population ( Table S1 ) . Modeling of two genotype mixed populations , like those represented in Table S1 , illustrates a worst case scenario for a false positive rate . In Table S1 , there are two types of variants: “true” variants , sourced from the minor genome type , and errors resulting from PCR or sequencing . The absolute level of true variants will be dependent on the number of minor genome types; as the number of minor genome types increases , the number of true variants also increases . The number of errors , though , is a function of PCR and sequencing and should be independent of the number of minor genome types . Thus , the ratio of errors to true variants ( the false positive rate ) will decrease as the number of minor genome types increases . In this modeling experiment , there is only one minor genome type and thus , we are recording the upper limit of false positive rates of a mixed genome type population . From the Sanger dataset ( Table 4 ) , it was shown that the populations studied are comprised of many genotypes ( e . g . 13 unique genotypes from 20 clones ) , not just one minor genotype . Thus , this modeling experiment overestimates the actual false positive rate of the clinical data . It was possible that the relatively high G:C content of the HCMV genome could alter error rates across the genome , and should be addressed by the error filtering protocol . However , we did not detect a relationship between error rates and G:C content from the BAC resequencing data ( data not shown ) . We did observe an association between G:C content and depth , with reduced depth at very low ( 20% ) or very high ( >80% ) G:C content ( data not shown ) . This characteristic of the Illumina platform has been documented previously [67] . We corrected for differences in depth when analyzing the intrahost populations ( Figure S5 ) so that changes in depth associated with G:C content should not alter our analyses . To determine the quantitative capabilities of our methodology , we combined Toledo and AD169 BAC DNA in ratios of 1:10 and 1:100 as templates for PCR amplification ( with Toledo present as the major genome ) and then amplified two regions of the genome using our PCR amplification technique . These two regions represent ∼6 kb of the HCMV genome and have a GC content of 58% , approximately equal to the genome wide average of 57% . In these regions , there are 118 sites of mismatch between the Toledo and AD169 genomes . The amplification products were processed and sequenced using the Illumina GAII platform and the output was aligned to the Toledo genome . We ran the data through our variant filtering algorithm to detect the minor variants in the sequence population ( i . e . AD169-derived sequence ) . Our data revealed a 48% detection rate when the minor genome is present as 10% of the PCR template and a 38% detection rate when present as 1% ( Table S9 ) . The relatively low detection rate is a consequence of the stringency of the filtering algorithm we developed . The frequency of the minor variants detected in the output sequence was approximately equal to their frequency in the input DNA . These data show that this methodology is suitable for detection and quantitative description of variants in populations . A schematic for calling genome types is shown in Figure S4 . The high throughput sequencing reads were initially aligned to Merlin ( Ref Seq ID: NC_006273 ) . Output from this initial alignment was used to call variants with frequency >0 . 5 at every position of the genome because these variants were interpreted to best represent the major allele of the sample . Sites that did not have an allele with a frequency >0 . 5 were left as uncalled bases ( N ) , and were excluded from intrahost diversity measurements since they represent tri- or quad-allelic sites . The high frequency variants were incorporated into a sample specific genome type . Reads that did not initially align were used as substrate for de novo contiguous sequence ( contigs ) assembly using SHARCGS [68] . These contigs were then aligned to the sample specific genome type using Geneious [69] and incorporated into the genome if sequence identity was found . Using this strategy , we were able to remove up to ∼1 kb of uncalled bases from the genome type . The sample specific genome type was used in another round of alignment of the sample's sequencing reads . With this strategy , we observed a 1–6% increase in the number of aligned reads after this round as compared to the initial alignment to Merlin . Because more reads aligned , additional high frequency variants were called . The high frequency variants were incorporated into the sample specific genome type and again contigs were aligned to the genome type . This process was repeated until no additional reads aligned between rounds of building the sequence ( usually 4 rounds were required ) . At the end of the process , a single specific genome type was created for each sample , which incorporates all high frequency variants found within . It is unknown if the sample specific genome type represents any single genome within the sample because linkage information is lost from short read sequencing . The sample specific genome type is a computational tool that aids in the alignment of short reads , particularly when a pre-existing reference sequence is unavailable or is divergent from the sample . Variants were called from the clinical sequencing data or BAC resequencing data through filtering with the variant caller algorithm . All alignments used to generate the data were normalized to an average depth of 200 genome equivalents . A depth of 200 was chosen because the lowest depth of an included dataset was ∼200 ( i . e . , AD169 BAC resequencing ) , so the ceiling was set to normalize across datasets . Mismatches from BAC resequencing were assumed to be errors and mismatches from clinical sequencing were assumed to be either errors or true variants . Without filtering , BAC resequencing generated , on average , 106 , 485 called mismatches and clinical sequencing generated 116 , 594 mismatches ( Figures S2A , S2C ) . Therefore , we estimate a false positive rate in the unfiltered clinical data of 91 . 3% ( 106 , 485 of 116 , 594 ) . However , filtering with the variant caller reduced estimated false positives to 6 . 7% of the clinical variants within populations ( Figures S2B , S2D ) . To determine the error rates of calling interhost polymorphisms ( frequency >0 . 5 ) , a similar analysis of the BAC resequencing data was undertaken . We determined that the error rate for calling polymorphisms is 0 . 028% , or ∼65 erroneous polymorphisms per genome ( Table S2 ) . On average , interhost HCMV sequence data contained >2300 polymorphisms per genome . It should be noted that the error rate for calling interhost polymorphisms is significantly lower than the error rate for calling intrahost variants ( 0 . 028% vs . 6 . 7% ) . Intrahost variants must occur at least 3 times as part of the filtering strategy . However , interhost polymorphisms , because they are present at frequency >0 . 5 and the minimum depth is 15 , must occur more than 8 times to be called . Because random errors generated by PCR or sequencing will most likely be rare , the possibility of random errors occurring ≥8 times and occurring in >50% of reads is low . Thus , a lower percentage of errors are included in the interhost polymorphism data then the intrahost variant data . The genome wide intrahost variant data was analyzed using the program SweepFinder ( http://people . binf . ku . dk/rasmus/webpage/sf . html ) , which implements the methods of Nielsen et al . [54] and outputs the position , selection coefficient and composite likelihood ratios ( CLRs ) of genomic regions . CLRs are measures of the probability of a selective sweep within a genomic region . To determine the significance of the data , 1000 simulations were performed under a standard neutral model using the ms program [70] . A set of simulations was run for each clinical sample population , in which the number of segregating sites and value of θ ( Watterson estimator ) of the simulation equaled the corresponding values calculated from the clinical samples . The simulation was then processed with SweepFinder , and output from this analysis ( Figure S8 ) was used to determine p values by comparing the clinical value to the simulated outputs . ORFs were chosen for clonal Sanger sequence by selecting candidate ORFs from each patient sample that displayed high intrahost variability . All clonally sequenced regions were between 500–700 bp , such that variability data could be generated in a single Sanger sequencing reaction . The regions were amplified with the appropriate primers using the PCR protocol described above , A-tailed with Kleno exo- and dATP ( NEB ) , and cloned into the Strataclone cloning vector ( Stratagene ) . For each ORF , 20 clones were selected at random and sequenced . As a control , a 500 bp region of Toledo-BAC was amplified and clonally sequenced in the same manner . These data were then analyzed in DnaSP [71] to determine nucleotide diversity ( π ) and genotype distribution . For analysis of the association of ORF function or kinetics with intrahost nucleotide diversity or interhost dN/dS , a 1-factor ANOVA analysis was performed . A Bonferroni correction for multiple testing was carried out , where a significant p-value was considered < . 05/K where K is the number of tests run per dataset . A G-test was performed on the interhost dN/dS values with the null hypothesis set as dN/dS = 1 . The McDonald-Kreitman test was done using the web portal as described in [72] , which performs the analysis using a Jukes-Cantor correction for divergence and the statistical analysis based on a 2×2 contingency table . A neutral model was rejected if p<0 . 05 . A Z-test was used to determine the significance of the proportions of the mean diversity of non-coding and coding variants . The distribution of variant frequencies was analyzed by a two-tailed Mann-Whitney test . Raw sequencing reads from Illumina sequencing are deposited in the Sequence Read Archive ( http://www . ncbi . nlm . nih . gov/Traces/sra/sra . cgi ) . Major genome types generated from this study are deposited in Genbank ( http://www . ncbi . nlm . nih . gov/genbank/index . html ) . | Human Cytomegalovirus ( HCMV ) is a dsDNA virus that is the leading source of birth defects associated with an infectious agent . There is currently no effective HCMV vaccine and few treatment strategies for congenital infections exist . Thus , a better understanding of HCMV infections is warranted . Limited data has shown that HCMV exists as a mixture of a few genotypes in human hosts . Here , we describe our use of high throughput sequencing to study the extent of genome wide variability within HCMV infections sampled from congenital infections . Surprisingly , we find that HCMV populations are as variable as quasispecies RNA viruses; it is commonly believed that DNA viruses are more genetically stable than RNA viruses , and thus produce homogenous populations . Additionally , we find evidence of evolutionary pressures acting on the HCMV genome , both within and among populations . These results provide the first evidence that diversity of DNA virus populations can be comparable to that of RNA virus populations . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/genomics",
"virology/virus",
"evolution",
"and",
"symbiosis",
"virology"
] | 2011 | Extensive Genome-Wide Variability of Human Cytomegalovirus in Congenitally Infected Infants |
Many bacteria and eukaryotic cells express adhesive proteins at the end of tethers that elongate reversibly at constant or near constant force , which we refer to as yielding elasticity . Here we address the function of yielding elastic adhesive tethers with Escherichia coli bacteria as a model for cell adhesion , using a combination of experiments and simulations . The adhesive bond kinetics and tether elasticity was modeled in the simulations with realistic biophysical models that were fit to new and previously published single molecule force spectroscopy data . The simulations were validated by comparison to experiments measuring the adhesive behavior of E . coli in flowing fluid . Analysis of the simulations demonstrated that yielding elasticity is required for the bacteria to remain bound in high and variable flow conditions , because it allows the force to be distributed evenly between multiple bonds . In contrast , strain-hardening and linear elastic tethers concentrate force on the most vulnerable bonds , which leads to failure of the entire adhesive contact . Load distribution is especially important to noncovalent receptor-ligand bonds , because they become exponentially shorter lived at higher force above a critical force , even if they form catch bonds . The advantage of yielding is likely to extend to any blood cells or pathogens adhering in flow , or to any situation where bonds are stretched unequally due to surface roughness , unequal native bond lengths , or conditions that act to unzip the bonds .
Bacteria and Eukaryotic cells must resist mechanical forces when they bind to their surroundings . For example , bacteria and blood cells adhere to other cells or tissues in the presence of fluid flow that applies a drag force on the cell , while many other cells apply force to each other or to solid surfaces via cytoskeletal contraction . These mechanical forces affect the lifetime of the individual noncovalent receptor-ligand bonds that mediate cell adhesion . Some receptors form slip bonds , which are shorter-lived with applied force . However , it is now understood that many adhesive receptors form catch bonds , which are longer-lived at higher force [1] , [2] , [3] , [4] , [5] . Still others form ideal bonds , which have a constant lifetime over a range of force [5] . However , all bonds transition to slip bonds above a critical force , which is generally much less than the total force involved in cell adhesion . Thus , strong and stable cell adhesion requires clusters with multiple receptor-ligand bonds . This raises the question of whether cells have evolved mechanisms of stabilizing bond clusters . Multivalent receptor-ligand adhesion is affected not just by the properties of the receptors , but by how they are incorporated into a cluster or cell [6] . For example , a receptor-coated and a ligand-coated surface can be easily separated by peeling forces , which stretch bonds to unequal lengths , but resist much higher forces if all bonds are stretched to the same length by shearing between two parallel surfaces [7] , or if multiple bonds are stretched in parallel [8] . However , many surfaces are rough or curved , or tethers have unequal equilibrium lengths , so that bond strains are unequal regardless of force direction . When bond strains are unequal , the elastic properties of the tethers anchoring each receptor or ligand to the cell or surface affect how force is distributed among bonds . For example , longer tethers increase the rupture force of the clusters [9] . In most studies of clusters of bonds , it is assumed that tethers are either stretched equally [7] , [10] , or are Hookean springs [11] , [12] , for which force increases linearly with extension ( Fig . 1A ) . However , many biological tethers anchoring adhesive molecules exhibit nonlinear elasticity . While entropic polymers and tissues often exhibit strain-hardening elasticity ( Fig . 1A ) , many bond tethers exhibit yielding elasticity , where the force plateaus at a critical force , allowing long extensions at a constant force ( Fig . 1A ) . Yielding elasticity is observed for biological macromolecules and organelles as structurally and evolutionarily divergent as membrane microvilli [13] , [14] , [15] , [16] , alpha helical proteins [17] , or quaternary helices in bacterial fimbriae [18] , [19] , [20] , [21] , [22] . Moreover , the ‘saw tooth’ pattern ( Fig . 1A ) caused by the sequential unfolding of multiple globular domains in proteins like fibronectin [23] approaches yielding elasticity when pulled more slowly . This raises the question as to why yielding elasticity is so common in cell adhesion . Escherichia coli bacteria with type 1 fimbriae provide an ideal model system for studying the role of nonlinear elasticity in cell adhesion because the adhesive structures are well characterized . Type 1 fimbriae exhibit nonlinear elastic extension due to uncoiling of a quaternary helix of a linear polymer that consists of hundreds to thousands of subunits [19] , [24] , [25] . Each type 1 fimbriae has a single FimH adhesin at the tip [26] , [27] , that form bonds with well-characterized properties [2] , [28] . Depending on the FimH sequence , FimH can form either catch bonds that require force to be activated or strong slip bonds that do not require any activation [2] . Type 1 fimbriae thus provide and ideal system for understanding the role of tether elasticity in dynamic cell adhesion . This would require methods to probe the forces on single fimbriae during dynamic cell adhesion , and to change fimbrial elastic properties . Fluorescent methods don’t provide high simultaneous temporal and spatial resolution , in spite of recent advancements in fluorescent force sensors [29] and single molecule fluorescence [30] , while other methods of measuring tether forces [31] disrupt adhesion . We also lack methods of genetically or chemically altering fimbriae to dramatically change elastic properties . Fortunately , computational simulations [28] , can be used to probe bond forces and control elastic properties . Adhesive dynamics simulations were applied to E . coli binding via type 1 fimbriae , but yielding elasticity was not incorporated because simulated forces were too low to uncoil the fimbriae at the flow conditions studied [28] . On the other hand , computational models of uncoiling fimbriae have been fit to data [19] , [24] , [35] , [36] , and used to predict functional advantages [19] , [25] , [37] , [38] , [39] , [40] , but have never been incorporated into experimentally validated models of whole cell adhesion , so the importance of fimbrial yielding to cell adhesion remains unclear . Here we use type 1 fimbrial E . coli adhesion as a model system to investigate the role of yielding elasticity in biological adhesion . We develop a complete model for fimbrial coiling and uncoiling in dynamic conditions by fitting a biophysical model to new elongation and contraction data obtained from Atomic Force Microscope ( AFM ) experiments . We introduce this model into a previously validated adhesive dynamics model for bacterial adhesion without fitting any additional parameters . We validate the complete model with new experimental data on bacterial adhesion; both model and experiments showed that bacteria crept forward but did not detach with large increases in shear stress . We showed that robust adhesion at high flow requires yielding elasticity since it could not be reproduced when other elastic tether properties were used in the simulations . We analyzed the underlying mechanisms to determine that yielding elasticity allows a nearly perfect distribution of load between bonds that were stretched to varying lengths . Finally , we predicted based on the simulations that bacteria binding via catch-bonds can withstand low flow only if exposed previously to sufficiently high flow to induce elastic yielding , which we validated experimentally . These observations demonstrate that yielding elasticity is critical for robust cell adhesion in dynamic conditions via noncovalent bonds .
In order to characterize the elastic behavior of fimbriae in experiments , we stretched and relaxed single fimbriae in a back-and-forth manner with an AFM ( Fig . 1C ) . During initial extension , the force ramped up rapidly , then plateaued suddenly ( Fig . 1D ) , showing the instantaneous switch from linear to yielding elasticity that has been observed previously for many fimbriae and pili [19] , [24] , [35] , [36] . During the back-and-forth movement , the fimbriae demonstrated hysteresis since the force cycled between a higher force during extension and a lower force during retraction ( Fig . 1D ) . The force levels during extension and retraction were calculated from pulls on several fimbriae at several velocities , ( diamonds and triangles , Fig . 1E ) and this was used to fit the parameters that determine the transition between the uncoiled and coiled states ( xAB , xBA , k0AB , k0BA ) . After a long extension , the hysteresis ended , so the extension and retraction phases converged into a single S-shaped force-extension curve that ended at about 150 pN with detachment of the cantilever from the fimbriae ( Fig . 1D ) . For several fimbriae at several forces , the extension was measured from this curve and normalized to maximum extension ( squares , Fig . 1E ) . This data was used to fit the parameters for the Worm-Like Chain ( WLC ) extension of the uncoiled state B and the stretched state C as well as the stretch transition between these states ( keq , xeq , lpB , lpC , x0B , x°C ) . Together this fitting resulted in the parameters shown in the table of Text S1 and dynamic elastic behavior as shown in Fig . 1E . Previous models that did not address the cooperative nature of the uncoiling transition were unable to reproduce the flatness of the main plateau [24] . Previous models that did not include the stretch transition [24] or did not allow for different persistence lengths before and after the stretch transition [25] could not fit the shape of the S-shaped curve ( not shown ) . Thus , our new model was necessary to accurately reproduce the entire range of dynamic stretching data for type 1 fimbriae . We incorporated this fimbrial elasticity model into previously developed adhesive dynamics simulations of E . coli , and validated the complete simulations by comparison to experimental data . Specifically , the shear stress was stepped up from 1 to 25 Pa in both simulations and experiments , and then dropped to 0 . 01 Pa . In both cases , the bacteria crept forward as shear increased , and then relaxed backwards when shear decreased , but not back to the original position ( Fig . 2 ) . There were small quantitative differences between the bacteria in simulations and experiments; in the simulations , the bacteria moved twice as far , and required slightly higher shear stress to begin moving . However , the relatively close fit is remarkable since there were no free fit parameters for this validation; all 33 simulation parameters were determined independently ( Fig . 1 and reference [28] ) . The most important observation , observed in both experiments and simulations , is that bacteria never detached , even at 25 Pa , which is higher than most physiological niches . Visual inspection of a typical simulation ( e . g . Video S1 ) revealed the bacterium is anchored in place via one activated FimH bond at low shear , but creeps forward at increased shear , as the anchoring fimbria uncoils , until a second FimH bond is activated , and so on . We analyzed the simulations to quantify these observations . Each time the flow rate was stepped up , the mean force per bond increased suddenly ( Fig . 3A ) , but relaxed back to about 50 pN per bond within seconds , if it had increased above this range ( Fig . 3A ) . This drop in force corresponded to an increase in the number of uncoiled fimbriae and activated FimH bonds ( Fig . 3A ) . Not only did the average force remain at 50 pN as shear increased further , but the distribution of bond forces was narrow ( Fig . 3B ) . It was shown previously that FimH bonds are long-lived between 30 and 70 pN , but break within seconds above 90 pN [41] because of the exponential effects of force , so we consider bonds exposed to over 90 pN force as vulnerable to dissociation . There were almost no vulnerable bonds in these simulations , as indicated by the presence of only one symbol above the dotted line at 90 pN in Fig . 3A . Thus , bacteria in the simulations withstand high shear stress by recruiting more activated bonds and by distributing the force evenly across these bonds . We next asked whether the nonlinear elasticity of the fimbriae was necessary for bacteria to resist high shear stresses . In the simulations , we changed the elastic properties of the fimbriae to model native yielding elasticity , strain-hardening elasticity , or linear elasticity . Shear stress was increased at 1 Pa/s until 100 Pa , or until the bacteria detached . If only one fimbria was allowed to attach ( by setting the bond association rate to zero for the unbound fimbriae ) , then bacteria detached between 10 and 12 Pa for all regardless of the type of tether ( Fig . 4A , dashed lines ) . Allowing multiple fimbriae to bind provided a small improvement for bacteria with strain-hardening tethers , which all detached between 10 and 18 Pa ( Video S2 ) , and slightly more improvement to bacteria with linear elastic tethers ( Video S3 ) , which detached between 15 and 25 Pa ( Fig . 4A ) . In contrast , bacteria with multiple native yielding tethers withstood much higher shear stress , with very few detaching by 30 Pa , and over 50% remaining bound through 100 Pa ( Video S4 ) . Thus , multiple tethers with yielding elasticity were necessary in the simulations to reproduce the ability of bacteria to withstand over 25 Pa , which was observed experimentally . To understand why the linear and strain hardening elastic tethers were unable to maintain adhesion at high shear stress , we calculated the number of activated bonds per bacterium ( Fig . 4B ) , the mean force per bond ( Fig . 4C ) , and the distribution of bond forces ( Fig . 4D ) . The strain-hardening tethers failed to mediate adhesion at high shear stress because the number of activated bonds remained under two per bacterium , and the average force per bond increased to above 90 pN at and above 10 Pa . In contrast , the linear elastic tethers recruited even more bonds and maintained a similar average force per bond relative to yielding tethers in the same conditions ( Fig . 4B ) . However , the distribution of bond forces for linear elastic tethers was broader , with over one quarter of activated bonds exposed to over 90 pN and thus vulnerable to detachment at and above 10 Pa ( Fig . 4D ) . Since each bacterium had only 3 to 4 activated bonds in these conditions with the linear fimbriae , this means that on average one bond per bacterium breaks rapidly , transferring its load to the remaining bonds , which overloads one of them , and so on . Therefore , linear tethers recruit enough bonds , but fail to protect bacteria from detaching because they do not distribute force evenly between bonds . This demonstrates that the ability to recruit more bonds and distribute force evenly between them , which stabilizes adhesion at high shear stress , requires yielding elastic tethers . Bacteria in vivo are often exposed to variable shear stress due to intestinal peristalsis or salivary motion . Bacteria binding via FimH catch bonds were shown previously to detach when the flow is turned down from 2 to 0 . 01 Pa [42] , presumably because catch bonds detach at low force . However , in our current study , bacteria relaxed backwards but did not detach when shear stress was dropped from 25 to 0 . 01 Pa , in both experiments or in simulations ( Fig . 2 ) . Surprisingly , the number of activated bonds increased when shear stress dropped to 0 . 01 Pa ( Fig . 3A ) . Moreover , while the drag force on a bacterium at 0 . 01 Pa is only 0 . 2 pN , the force per bond did not drop to near zero , but rather remained tightly distributed around 30 pN ( Fig . 3B ) . Simulations show that the uncoiled fimbriae shorten when shear is decreased , pulling the bacterium backwards , and activating new bonds as the bacterium becomes suspended between partially uncoiled fimbriae pulling in opposite directions ( Fig . 5A and Video S1 ) . Since the bacterium is now stationary , the anchoring bond is subjected to the equilibrium uncoiling force ( 32 . 2 pN ) for all partially uncoiled fimbriae . We thus predicted that the bacteria would only stay attached at 0 . 01 Pa in simulations if they were first subjected to enough shear stress to uncoil fimbriae . To test this prediction , bacteria in both simulations and experiments were subjected to 1 , 2 . 5 , 5 , or 10 Pa , and then dropped to 0 . 01 Pa . Below 5 Pa , most bacteria had one or fewer uncoiled fimbriae and activated FimH bond ( Fig . 5B ) , and almost none maintained activated bonds after shear was decreased to 0 . 01 Pa . In contrast , at 10 Pa , most bacteria had 2 or more uncoiled fimbriae and activated bonds ( Fig . 5B ) , and consistently retained activated bonds after shear was decreased . This corresponded to the ability of bacteria to remain attached after shear stress was decreased from 10 Pa but not from 5 Pa or less ( Fig . 5C ) . This supports the idea that uncoiling and recoiling are needed to withstand variable shear stress . Finally , we validated this prediction by performing the same test in experiments ( Fig . 5D ) . Slight quantitative differences were observed , with bacteria in experiments requiring slightly less shear stress for the same behavior , and with a higher fraction failing to detach at low flow . Thus , the experiments validated the prediction that bacteria can withstand a prolonged period at low flow better after being subjected to enough shear stress to uncoil fimbriae .
In this work , we draw our important conclusions from the simulations themselves , so it is essential that they be reliable . We ensure this by using a previously validated model in which almost all parameters were identified independently in cell-free assays , with only two parameters determined by fitting the simulations to cell adhesion data [28] . To add fimbrial uncoiling to this model , we determined all parameters independently by characterizing the elastic properties of individual type 1 fimbriae with atomic force microscopy , and fitting the data with a biophysical model ( Fig . 1 ) . Finally , we validated the accuracy of the combined model for cell adhesion by testing predictions of the model ( Fig . 2 , 5 ) . Since none of the 33 parameters were adjusted to fit the cell adhesion data , minor quantitative discrepancies are expected , such as the higher shear stress required for the same behavior ( Fig . 2 , 5 ) , and larger distance moved ( Fig . 2 ) in simulations relative to experiments . The first discrepancy suggests that we underestimated the drag coefficient [28] . The second discrepancy suggests that we underestimated the number of fimbriae from the 2D projection in the electron micrographs . These small quantitative differences likely vary from cell to cell and do not affect the conclusions of this paper . The creeping we observe in simulations also resembles the behavior of E coli binding through type 1 fimbriae as flow increased in a recent publication [43] . The ability to reproduce a variety of adhesive behaviors with no adjustable parameters provides a high level of certainty to the conclusions drawn from the simulations . Our major conclusion is that E . coli can withstand high shear stress because yielding elastic tethers called fimbriae distribute the drag force equally between multiple bonds ( Fig . 3B ) . To understand why this is important , consider that simple mechanics theory dictates that cell adhesion in flow , like many other conditions , occurs in a peeling manner in which tethers at one edge of the adhesive contact zone are stretched the farthest [11] . While many studies have shown that tethers exhibit some sort of strain-softening , or yielding , viscoelastic behavior [13] , [14] , [15] , [16] , the theories developed to address the strength of clusters of bonds during rolling or peeling have assumed that the tethers are linearly elastic , so longer tethers apply proportionally higher force [11] , [12] . Since bond lifetimes decrease exponentially with force above a critical value even for catch bonds , the bonds under most force break first , transferring force to the remaining bonds , and causing the cluster of bonds to unzip [7] . Indeed , we observe this behavior in our simulations with linear tethers , which apply a wide range of forces on bonds ( Fig . 4D ) and peel from the rear until they detach as the drag force is increased ( Video S3 ) . Most biological polymers and materials demonstrate nonlinear elastic properties . We show here that strain-hardening materials , which concentrate force even more on the most stretched tethers ( Fig . 1 , 4D ) , mediate even weaker adhesion in flow ( Fig . 4A ) . However , many cells have evolved yielding elastic tethers , which provide a constant or nearly constant force independent of extension length ( Fig . 1 ) . It is well understood that bond clusters are mechanically stronger when oriented relative to force such that all bonds are stretched equally , rather than oriented so that force can unzip the cluster by stretching them one at a time [7] , because the former distributes force better . However , we demonstrate here that yielding tethers can ensure equal force distribution even when bonds are stretched unequally , preventing peeling or unzipping in situations such as cell adhesion in flow . In our study , we assume that each tether has only one FimH , because this is dictated by the structure of type 1 fimbriae [26] , [27] . Tethers such as microvilli can have multiple receptors per tether . In these cases , the force per tether may be distributed between multiple receptors , but our conclusions about load sharing between tethers should still apply . Thus , adhesion with yielding tethers is much more robust . Our results demonstrate that robust adhesion requires the perfect load distribution that is unique to yielding elasticity ( Fig 4 ) . However , other previously demonstrated properties of yielding elasticity also benefit cell adhesion . Yielding provides a mechanism for creating long tethers , which reduce the force on a bond when a cell is anchored to a surface via a single tether in flowing fluid [44] , although this effect is similar for long tethers with any elastic property , as shown in Fig . 4A ( dashed lines ) . Yielding elastic tethers of all sorts are also usually viscoelastic [16] , [45] , [46] , [47] , so that they buffer force on a single bond in variable flow conditions [39] , [40] , or during dynamic single molecule force spectroscopy [14] , [15] . Previous studies have shown that yielding forces are optimized for the catch bonds at their tips [19] , [40] , [48] , which suggests that robust cell adhesion requires not just yielding , but yielding at a force that is appropriate for the mechanical properties of the bond supported by the yielding tether . Our conclusion can also explain previous observations about yielding tethers . Theory and simulations showed that elastic yielding tethers allowed clusters of bonds loaded in parallel to survive much longer than single bonds [37] and experiments showed that long yielding tethers greatly increased the adhesive strength of clusters of bonds between two surfaces [49] . In summary , while yielding elasticity provides many advantages to buffering force on single bonds , we demonstrate here that elastic yielding is most critical to robust adhesion of cells or large bond clusters because it uniquely distributes load equally in a complex environment . This conclusion may apply to many cell types , because many cells have elastic yielding tethers comprised of alpha helical proteins [50] , unfolding domains [51] , quaternary helices [43] , or membrane tethers [52] , all of which yield under force . For example , leukocytes and platelets extend membrane microvilli with selectin or GPIb [13] , [53] , fibroblasts bind to extensible fibronectin [23] through integrin , platelets to extensible fibrin [54] , bacteria bind through many types of quaternary helical fimbriae that yield [20] , [21] , [22] , [55] , and many adhesion proteins like integrins and cadherins are anchored to the cytoskeleton via alpha-helical adaptor proteins that also unfold [50] . Our simulations and experiments used catch bonds , and many of the proteins anchored to yielding tethers also form catch bonds , including P-selectin [1] , L-selectin [56] , GPIb [3] , integrin [4] , and fibrin knob-hole interactions [57] . Catch bonds and yielding tethers appear to co-evolve to provide the ideal force to optimize catch bond lifetime in order to enable robust binding in high force environments [19] . Catch bonds with yielding tethers ( Fig . 3A and 4B ) , but not catch bonds with other elastic anchors ( Fig . 4B ) allow the number of activated bonds to increase proportionally to the flow rate , finally providing a mechanism for the ‘automated braking system’ observed previously for leukocytes binding via selectins [58] . Nevertheless , it is unlikely that the importance of yielding tethers is unique to catch bonds , since all catch bonds transition to slip bonds above a critical force , and in our simulations , the ability to distribute force evenly was critical to preventing the failure of FimH bonds in the high-force slip regime . This analysis suggests that yielding elastic tethers may be critical for robust adhesion of a wide range of cells binding through a wide range of receptors . While the importance of yielding tethers to bond force distribution has not been shown previously for cell adhesion , yielding elasticity has been shown to be important in related fields . Adhesives are weak in a peeling mode , where load is applied so that stress concentrates at one edge , which propagates in a crack as the adhesive fails . This is minimized by soft thin film adhesives that can undergo a plastic deformation to form long yielding fibers that distribute stress equally along multiple fibers in spite of their difference in length . Because this deformation is irreversible , the thin film adhesives are weakened by this process , and could be improved by the development of a bio-inspired adhesive material that exhibits fully reversible viscoelastic yielding , like the yielding biological tethers described above . Yielding elasticity has also been demonstrated in fibers that make up certain biological materials , such as fibrin clots [54] and the spectrin network in red blood cell membranes [59] . These materials are resistant to tearing because yielding fibers prevent stress concentration . Thus , thin film adhesives , biological materials and cell adhesion are all strengthened by yielding fibers that prevent stress concentration and crack propagation . However , cell adhesion provides a new level of elegance , as the yielding force of the fibers must be optimized for the lifetime of the adhesive bond .
AFM experiments were conducted with an Asylum MFP3D AFM to determine the dynamic behavior of fimbriae in response to force . Olympus Biolever cantilevers were incubated with RNaseB ( a naturally mannosylated protein ) and surfaces with type 1 fimbriae using direct nonspecific adsorption , essentially as previously described [49] . Force pulls were controlled with a custom written script that allows back-and-forth movements at speeds from 0 . 1–10 µm/s . Plateau forces were determined by averaging at least 23 separate pulls from 2–4 experiments performed on different days with different cantilevers , except for the condition of recoiling at 10 µm/s , for which only 8 pulls were performed . All experiments were conducted in Phosphate Buffered Saline with 0 . 2% Bovine Serum Albumin ( PBS/BSA ) to prevent nonspecific adhesion . We have shown previously that proteins incubated in this manner remain adherent under much higher forces than 150 pN [2] , and that adhesive strength is maintained even after hundreds of pulls on the same surface-immobilized fimbriae [49] , so it is safe to assume that the proteins remain attached to the surface and cantilever during our experiments . Thus , the observed yielding behavior is not due to an experimental artifact . Flow chamber experiments were performed as previously described [42] . Briefly , a bolus of E . coli expressing KB-91 FimH and K12 fimbrial shafts was introduced at a moderate shear stress ( 0 . 1–0 . 3 Pa ) to allow bacteria to accumulate and then the shear was increased to 1 Pa to induce predominately stationary adhesion and to wash out unbound bacteria . The shear was then increased or decreased as indicated in each figure . Time-lapse videos were taken at 1 frame per second and analyzed to quantify cell position and detachment . In some experiments , a second syringe pump was used in parallel with the first to deliver a soluble inhibitor with minimal disruption to the system . Simulations were performed as previously described [28] except that the fimbrial uncoiling model was added . Briefly , the 3D simulations model the interaction of a fimbrial-coated spherical cell with a mannose-coated planar surface in a laminar fluid flow . The tip of each fimbria represents a single FimH adhesin which can stochastically form and break bonds with the surface according to the two-state allosteric catch bond model [60] . Fimbriae can stretch , bend and buckle due to linear elastic properties [28] , or uncoil and recoil with higher tension using the model described below . Simulations start with a single fimbria bound to the surface in the high-affinity state representing a bacterium that has just transitioned to stationary adhesion as in the experiments . In simulations with only a single fimbriae , the fimbriae was always set to 1 µm in length to remove differences that result from varied fimbrial lengths . In simulations with multiple fimbriae , bacteria were surrounded by 186 randomly distributed fimbriae with an average length of 0 . 572 µm around an exponential distribution [61] . All fimbrial models had the same length distribution as the native fimbriae . The native yielding fimbriae were modeled with a three state model in which each subunit can be in state A ( fully coiled ) , state B ( uncoiled ) , or state C ( uncoiled and stretched ) , with transitions allowed between A and B and between B and C , as illustrated in Fig . 1B . The subunits in state A form a contiguous segment , since uncoiling is a cooperative phase transition that only occurs at the edge of the helical coil , as indicated by the flat uncoiling transition in Fig . 1D and 1E . However , the subunits in states B and C form noncontiguous segments because the stretch transition occurs independently for any uncoiled subunit , as indicated by the sloped stretch transition in Fig . 1D and 1E . The number of subunits in each segment is determined by force-dependent transitions between the states , according to the Bell model . The A segment was modeled as a spring , and the B and C segments were modeled as worm-like chains with different persistence lengths . The total length of the fimbriae is the sum of the lengths of the three segments . All parameters were fit to AFM data . The linear elastic tethers were modeled by disallowing all uncoiling , so that the native linear elasticity is always in effect . The strain-hardening tethers were modeled as if the uncoiling occurs at negligible force , so that they elongated beyond their native rod length under force with the WLC model , using the parameters for segment B . A complete description of the uncoiling model is provided in Text S1 . | Cells adhere to surfaces and each other in the presence of forces that would easily overpower the individual noncovalent receptor-ligand bonds that mediate this adhesion , raising the question as to how these bonds cooperate to withstand such high forces . Here we show that cooperation and robust adhesion depends on the elastic properties of the bonds . A type of nonlinear elasticity referred to as elastic yielding ensures that the total force is distributed equally across the individual bonds regardless of geometry . In contrast , with linear or strain-hardening elasticity , the bonds that are stretched the most are exposed to higher forces , which cause them to fail sequentially . This work explains why elastic yielding is found in structurally and evolutionarily diverse adhesive complexes . | [
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] | 2014 | Yielding Elastic Tethers Stabilize Robust Cell Adhesion |
Cancer cells frequently undergo chromosome missegregation events during mitosis , whereby the copies of a given chromosome are not distributed evenly among the two daughter cells , thus creating cells with heterogeneous karyotypes . A stochastic model tracing cellular karyotypes derived from clonal populations over hundreds of generations was recently developed and experimentally validated , and it was capable of predicting favorable karyotypes frequently observed in cancer . Here , we construct and study a Markov chain that precisely describes karyotypic evolution during clonally expanding cancer cell populations . The Markov chain allows us to directly predict the distribution of karyotypes and the expected size of the tumor after many cell divisions without resorting to computationally expensive simulations . We determine the limiting karyotype distribution of an evolving tumor population , and quantify its dependency on several key parameters including the initial karyotype of the founder cell , the rate of whole chromosome missegregation , and chromosome-specific cell viability . Using this model , we confirm the existence of an optimal rate of chromosome missegregation probabilities that maximizes karyotypic heterogeneity , while minimizing the occurrence of nullisomy . Interestingly , karyotypic heterogeneity is significantly more dependent on chromosome missegregation probabilities rather than the number of cell divisions , so that maximal heterogeneity can be reached rapidly ( within a few hundred generations of cell division ) at chromosome missegregation rates commonly observed in cancer cell lines . Conversely , at low missegregation rates , heterogeneity is constrained even after thousands of cell division events . This leads us to conclude that chromosome copy number heterogeneity is primarily constrained by chromosome missegregation rates and the risk for nullisomy and less so by the age of the tumor . This model enables direct integration of karyotype information into existing models of tumor evolution based on somatic mutations .
Cancer genomic heterogeneity , which is often driven by genomic instability , enables Darwinian selection , leading to tumor metastasis and increased resistance to therapeutic pressures [1–3] . A frequent , yet understudied source of genetic heterogeneity is numerical chromosomal instability , which allows cancer cells to rapidly vary the number of copies of each chromosome ( karyotype ) through whole chromosome missegregation events during mitosis [4–7] . This karyotypic heterogeneity can lead to tumor cells with varying fitness levels depending on the potency and distribution of oncogenes ( proliferative ) and tumor suppressor genes ( anti-proliferative ) on individual chromosomes [8] . Despite its importance , the contribution of numerical chromosomal instability toward tumor evolution has been poorly understood due to limitations in experimental and theoretical models that attempt to understand this process on the systems level . Chromosome missegregation was first incorporated into a model of tumor evolution by Gusev et al . [9] and later in a continuous time model by Desper et al . [10] . While helpful , these models neglected the observed phenomenon that having more copies of chromosomes encoding a higher fraction of oncogenes is advantageous for the cell , while having more copies of chromosomes encoding tumor suppressor genes increases its chances of dying [8] . Laughney et al . addressed this limitation by building a stochastic model that tracks single cell karyotypes derived from clonal populations over hundreds of generations , while simultaneously allowing the cumulative proliferative or anti-proliferative effects of genes encoded on individual chromosomes to alter cellular viability [4] . This model incorporates chromosome-specific scores derived from a recent genomic analysis by Davoli et al . [8] , which weighs individual chromosomes based on the potency and chromosomal distribution of oncogenes ( proliferative , contributing positively ) and tumor suppressor genes ( anti-proliferative , contributing negatively ) . The scores of the individual chromosomes are then aggregated to determine the survival probability of each cell . In its most basic form , the model assumes the following: The model by Laughney et al . unveiled several key observations which were validated experimentally . First , it revealed a highly favorable , and commonly observed near-triploid state , onto which evolving cells converge . This is in line with enrichments for near-triploid karyotypes observed in human tumors deposited in the Mitelman database , as well as tumor ploidy inferred from bulk DNA sequencing of TCGA tumors [11 , 12] . It also predicted the existence of an optimal missegregation rate —which maximizes cell viability with the generation of heterogeneity—that agreed with the experimentally measured chromosome missegregation rates observed in human cancer-derived cell lines [13 , 14] . Finally , it was directly validated by predicting the frequency at which single cells deviate from the modal chromosome numbers for any given chromosome in an expanding clonal population after 25 cell divisions , as experimentally measured in single-cell-derived clones by fluorescence in situ hybridization . This model , however , was unable to predict the limiting distribution of cellular karyotypes in a tumor population or to complement models of tumor evolution based on somatic mutations , which occur with relatively low frequency , given the sheer number of cells that must be tracked for many generations in order to reach a probabilistic conclusion . It was also unable to test the dependence of large tumor cell populations on multiple parameters due to the sheer computational power required to perform such simulations . In this paper , we construct and mathematically analyze a Markov chain that describes the evolution of the karyotype of a random cell in the above stochastic model . A special case of this Markov chain was briefly mentioned in [4] and used in some computations . However , no mathematical analysis was given , where the focus was to obtain a biological understanding of the role of numerical chromosomal instability in tumor evolutionary dynamics . The structure of the paper is as follows: in the Methods section , we start by describing a simplified version of the model and its associated Markov chain without chromosome-specific influence on cellular viability . Then we describe the full model which enables chromosome-specific scores to alter cellular viability . In the Results section we analyze both models . First we show that the simplified Markov chain , after some slight adjustments , has interesting mathematical properties; for example , the limiting cellular karyotype does not depend on the chromosome missegregation rate . We study this limiting karyotype , as well as its dependence on the maximum allowed number of copies of each chromosome . Next we focus on the full model , showing that , interestingly , the limiting distribution of cellular karyotypes is no longer independent of missegregation rate in this scenario . We show that by varying key parameters of the model , namely the missegregation rate ( or probability , p ) and the chromosome scores , very different behaviors are obtained in the limit . In particular , for parameters observed in human cancer cells , the resulting limiting behaviors are more realistic than those predicted in [9] . Finally , using our model , we find that maximal karyotype heterogeneity can indeed be achieved after a small number of cell divisions at chromosome missegregation rates frequently observed in cancer . This suggests that chromosome missegregation is more consequential toward genomic heterogeneity than the tumor lifetime , as tumors with low missegregation rates cannot reach maximal heterogeneity even after tens of thousands of generations of cell division . The Discussion section explains these conclusions , and compares our model to others in the literature .
Let us begin by describing a simplified version of the stochastic model , which is also used in [4] . The karyotype of a cell is the vector ( n1 , … , n23 ) where nk is the number of copies of chromosome k that it contains . Starting from a founder cell with a given karyotype , at each generation , all the cells in the colony divide , giving rise to two cells . When a cell divides , each of the nk copies of chromosome k , for 1 ≤ k ≤ 23 , splits into two copies . In normal circumstances , each copy goes to one of the daughter cells , so the daughters have the same karyotype as the mother . However , with probability p , the two copies go to the same daughter cell , while the other daughter receives no copies . Such an event is called a missegregation , and p is called the missegregation rate ( per chromosome copy per cell division ) . Note that at each cell division , each copy of each chromosome undergoes a missegregation with probability p , and these events are independent of each other . If the number of copies of a chromosome in a cell reaches 0 or goes above the maximum allowed number of copies , N , the cell automatically dies and no longer reproduces . Thus , for a cell to be viable , it must have 1 ≤ nk ≤ N for all k . The basic stochastic model described in this section does not include chromosome-specific scores; these will be included in the next section . In the basic model , the only way for a cell to die is if the number of copies of a chromosome leaves the range [1 , N] . We construct a Markov chain M that models the proportion of copies of a given chromosome in the colony . The following simplifications will make our model more tractable: With the above assumptions , the transition matrix M for the non-absorbing states has entries M i j = { 1 - i p if i = j , i p / 2 if | i - j | = 1 , 0 if | i - j | ≥ 2 , for 1 ≤ i , j ≤ N , where Mij is the probability of transitioning from state i to state j . Adding an extra row and column corresponding to the absorbing state 0 , we get the matrix M′=[10⋯0p/20⋮0Np/2M] . For example , if the maximum number of chromosomes is N = 8 , which is the bound used in [4] , we have M′=[ 100000000p/21−pp/20000000p1−2pp00000003p/21−3p3p/200000002p1−4p2p00000005p/21−5p5p/200000003p1−6p3p00000007p/21−7p7p/24p0000004p1−8p ] . Indeed , each copy of the chromosome in a cell will produce 0 , 1 or 2 copies in a random daughter with probability p/2 , 1 − p and p/2 , respectively . If a cell has i copies of the chromosome , since each one of these copies missegregates independently , the probability that a random daughter has j copies is given by the coefficient of xj in the polynomial ( p 2 + ( 1 - p ) x + p 2 x 2 ) i . Neglecting quadratic terms in p , we have ( p 2 + ( 1 - p ) x + p 2 x 2 ) i ≈ i p 2 x i - 1 + ( 1 - i p ) x i + i p 2 x i + 1 . This gives the rows of M′ , except for the first row , which is trivial because a dead cell does not divide , and the last row , which takes into account that a cell with N + 1 copies is considered dead . To describe the evolution of the karyotypes of tumor cells with 23 types of chromosomes in this basic model , we consider the product of 23 Markov chains , each of them isomorphic to M . We can do this because missegregation events involving different chromosomes are independent , and the number of copies of each chromosome evolves according to M . Product states where at least one of the components corresponds to a dead ( i . e . absorbing ) state are regarded as dead states in the product chain . Thus , even though the Markov chain for chromosome k does not capture the fact that a cell may die because of a disallowed number of copies of another chromosome , this event is taken into account in the product of the 23 chains . One way to think about it is by pretending that cells with no copies of a chromosome still divide as usual , but they give rise to two dead cells with no copies of that chromosome . In the basic model from the previous section , the only way for a cell to die is if the number of copies of a chromosome reaches 0 or goes above N . A more realistic model should include the possibility that a cell dies for other reasons . In fact , the karyotype of the cell is postulated to have an influence on its survival probability . It has been proposed [8] that having more copies of certain oncogenic chromosomes is subject to positive selection as evidenced by a pan-cancer analysis of chromosome-level amplifications , whereas having more copies of other tumor-suppressive chromosomes is subject to negative selection . In this section we construct a more general Markov chain which takes these factors into account . This Markov chain describes the evolution of the number of chromosome copies in random cells in the stochastic model of Laughney et al . [4] . As in that model , we assign a score sk to each chromosome k , which is positive for oncogenic chromosomes and negative for tumor-suppressive ones , so that the total score of a cell with karyotype ( n1 , … , n23 ) is S = ∑ k = 1 23 s k n k . Numerical values of sk were experimentally inferred by Davoli et al . [8] . Here we describe the Markov chain in a more abstract setting where the sk are left as parameters . The survival probability of the cell with score S at a given generation is Qsurv = ec+dS for some constants c < 0 and d > 0 , which again are parameters of the model . With probability 1 − Qsurv , the cell spontaneously dies at that generation . With probability Qsurv , the cell divides as usual , with missegregation events taking place as in the model without scores . Note that it is still possible for the daughter cells to die if the number of copies of a chromosome leaves the range [1 , N] , but this cause of death is unrelated to the survival probability Qsurv . A key obervation that will make the size of our Markov chains tractable is that Q surv = e c + d ∑ k = 1 23 s k n k = ∏ k = 1 23 e c k + d s k n k = ∏ k = 1 23 q k ( n k ) , ( 1 ) where the ck are arbitrary constants with c1 + ⋯ + c23 = c , and we write q k ( i ) = e c k + d s k i to denote the contribution to the survival probability coming from chromosome k . It will be convenient to write qk ( i ) = Cμi for constants C = e c k and μ = e d s k ( note that μ > 1 if and only if chromosome k is oncogenic ) . Eq ( 1 ) allows us to break up the model with chromosome scores into 23 independent Markov chains A ( k ) , one for each chromosome type . In A ( k ) , a cell in state i has probability qk ( i ) of dividing as usual ( as in the Markov chain M from the basic model ) , and probability 1 − qk ( i ) of spontaneously dying , which is represented by a transition to the absorbing state 0 . The evolution of karyotypes in the colony is then described by the product of the 23 Markov chains A ( k ) for 1 ≤ k ≤ 23 . Again , a product state where at least one of the coordinates corresponds to the absorbing state of some A ( k ) is regarded as a dead state in the product chain . With this setup , a cell with karyotype ( n1 , … , n23 ) has probability Qsurv = ∏k qk ( nk ) of surviving and dividing as in the model without scores , with each chromosome type behaving independently , and probability 1 − Qsurv of spontaneously dying . Since viable states in the product chain correspond to products of viable states in the chains A ( k ) , the proportion of cells with a given karyotype ( n1 , … , n23 ) after g generations ( as a fraction of 2g ) is given by the product for 1 ≤ k ≤ 23 of the probability that the Markov chain A ( k ) is in state nk . This means that the simplification 1 described in the previous section is still applicable in the model with chromosome scores . When it creates no confusion , we will simply write A instead of A ( k ) . The transition matrix of this Markov chain restricted to the non-absorbing states is A , with entries defined as A i j = { ( 1 - i p ) q k ( i ) if i = j , i p q k ( i ) / 2 if | i - j | = 1 , 0 if | i - j | ≥ 2 , for 1 ≤ i , j ≤ N . We can express A as A = DM , where D is the diagonal matrix with Dii = qk ( i ) for 1 ≤ i ≤ N , and M is the matrix from the basic model . If the value of the parameter c is such that Qsurv ≤ 1 for all valid karyotypes , then it is possible to choose the constants ck so that qk ( i ) ≤ 1 for 1 ≤ i ≤ N and 1 ≤ k ≤ 23 , and so the factors qk ( i ) can be interpreted as probabilities . We point out , however , that any arbitrary choice of the constants ck , provided that they sum to c , will give the same transition probabilities in the product Markov chain and thus the results of the analysis do not depend on this choice . It is possible to modify our model to allow for whole genome duplication [5] . To this end , consider an N × N matrix G with entries G i j = { - p gd if i = j , p gd / 2 if 2 i = j , 0 otherwise , for 1 ≤ i , j ≤ N , where pgd is a new parameter giving the probability that a random cell duplicates its genome but does not divide at a given generation . To incorporate whole genome duplication , we use the matrices Mgd = M + G and Agd = DMgd instead of M and A , for the basic model and for the model with chromosome scores , respectively . With this modification , the corresponding Markov chains contain a transition from state i to 2i ( or to the dead state if 2i > N ) with probability pgd/2 . Indeed , with probability pgd , a random cell duplicates its genome instead of producing two daughter cells , thus we can consider the transition probability to the “daughter” cell with duplicated genome to be pgd/2 , while adding an additional transition to the dead state with probability pgd/2 , corresponding to the other “daughter” cell that has not been created . It is possible to modify the matrix G to allow for the genome duplication probability pgd to depend on the number of chromosome copies , by setting different values of pgd for different rows of the matrix . Since our model considers each of the 23 chromosomes independently , it cannot account for correlations between duplications in the different chromosomes ( namely , the fact that all 23 chromosmes duplicate simultaneously ) . Nevertheless , by restricting to one chromosome at a time , the model gives the correct distribution of the number of copies over time , as well as the limiting distribution . Aneuploidy and chromosomal instability are hallmarks of advanced solid tumors . However , during early stages of tumorigenesis , induction of aneuploidy has been shown to mitigate tumor growth [15 , 16] . It was postulated that the negative effect of aneuploidy might be due to the various steps needed for tumor cells to become tolerant to chromosome copy number abnormalities . Loss of the tumor suppressor p53 has been shown to be a landmark event in the ability of mammalian cells to tolerate aneuploidy and complex karyotypes [17 , 18] . In this section we attempt to model the process whereby key tumor suppressor proteins are inactivated either through mutational processes or copy number loss therefore enabling tolerance to chromosome missegregation . To this end , we modify the Markov chain A by adding two additional states that model the early stage of the tumor , when deviation from a perfect diploid karyotype results in death due to the presence of active copies of a certain gene X . Recall that A contains N states corresponding to cells with i copies ( for 1 ≤ i ≤ N ) of a particular chromosome k , which we assume is the one containing gene X . To obtain the modified Markov chain , which we call A X , the first additional state that we add to A corresponds to cells with two copies of chromosome k , both of which contain an active copy of gene X; we denote this state by σ . The second additional state corresponds to cells with two copies: one where gene X is active , and one where gene X is inactive due to mutation; we denote this state by τ . Let mr denote the mutation rate , which is the probability that , at a given generation , a given copy of chromosome k undergoes a mutation that inactivates gene X . The transition matrix of the modified Markov chain consists of the matrix A with two additional rows and columns , indexed σ and τ , and the following entries: A σ σ = ( ( 1 - p ) 46 - 2 m r ) q k ( 2 ) , A σ τ = 2 m r q k ( 2 ) , A τ σ = 0 , A τ τ = ( ( 1 - p ) 46 - m r ) q k ( 2 ) , A τ 1 = p 2 q k ( 2 ) , A τ 2 = m r q k ( 2 ) , A σ i = A i σ = A i τ = 0 for 1 ≤ i ≤ N , A τ i = 0 for 3 ≤ i ≤ N . Indeed , for a cell in state σ , the probability that either of the two active copies of gene X mutates ( transitioning to state τ ) is about 2mr . The entry Aσσ accounts for the fact that the cell dies if any of the 46 chromosome copies in the cell ( 2 for each of the 23 human chromosomes ) missegregates . The probability of none of these copies missegregating is ( 1 − p ) 46 . In the matrix , these probabilities are multiplied by the usual survival probability qk ( 2 ) of a cell with two copies of chromosome k . Similarly , for a cell in state τ , the probability that the active copy of gene X mutates ( transitioning to state 2 ) is mr , and the probability that the active copy missegregates and a random daughter cell receives no active copies ( transitioning to state 1 ) is p/2 .
Let ( Mg ) i , j be the entry in row i and column j of the gth power of M . In the one-chromosome version , this number is the proportion of cells after g generations that , starting with a founder cell that has i copies of a chromosome , have j copies of that chromosome . In particular , the sum of the entries of the ith row of Mg , which we denote by sg ( i ) , is the probability that the number of copies of the chromosome is between 1 and N . When combining the 23 Markov chains to keep track of all chromosomes , the product ∏ k = 1 23 s g ( n k ) is the surviving fraction after g generations when the founder cell has nk copies of chromosome k for every k , as a fraction of 2g , which would be the number of cells after g generations if there were no deaths . Thus , 2 g ∏ k = 1 23 s g ( n k ) is the expected number of viable cells after g generations . Restricting to viable cells , the ith row of Mg divided by sg ( i ) gives the probability distribution of the number of copies of a chromosome after g generations among viable cells , when the founder cell has i copies . More generally , if v is a probability vector that describes an initial distribution of the number of copies , then the vector vMg , divided by the sum of its entries , is the distribution among viable cells of the number of copies after g generations . We are interested in the behavior of the Markov chain when the number of generations tends to infinity . Since the Markov chain M has an absorbing state , namely the one corresponding to dead cells , its stationary distribution is not very interesting: in the long run , the probability that a random branch ends at a dead cell tends to one . Instead , we would like to know the distribution of the number of chromosome copies among viable cells . Mathematically , we can do this by conditioning on not being on the absorbing state , and finding the limiting conditional distribution on the non-absobring states . The Markov chain M has the property of being irreducible on the non-absorbing states , meaning that it is possible to go from any state other than the absorbing one to any other state if we allow enough steps . Markov chains with this property have been studied in the probability literature , see e . g . [19] . It is known that when conditioning on the non-absorbing states , the limiting conditional distribution of the chain is its so-called quasi-stationary distribution , which is unique . In our case , this is the unique ρ-invariant distribution for M , where ρ is its Perron—Frobenius ( i . e . largest ) eigenvalue . In other words , this distribution is the vector v ∈ R ≥ 0 N satisfying vM = ρv and ∑ i = 1 N v i = 1 . We summarize this result as a lemma , since it will be used later on . Lemma 1 . Let Q be a Markov chain with one absorbing state and N non-absorbing states , on which the chain is irreducible . Let Q be the transition matrix restricted to the non-absorbing states , and let ρ be its largest eigenvalue . Then , the limiting distribution of Q conditional on the non-absorbing states is given by the vector v ∈ R ≥ 0 N satisfying vQ = ρv and ∑ i = 1 N v i = 1 . In particular , it follows from Lemma 1 that the limiting distribution of M conditional on the non-absorbing states does not depend on the number of chromosome copies of the founder cell . Next we show that , surprisingly , it does not depend on the missegregation rate p either . It will be convenient to write M as M = I + pJ , where I is the identity matrix , and J is the matrix with entries J i j = { - i if i = j , i / 2 if | i - j | = 1 , 0 if | i - j | ≥ 2 , ( 2 ) for 1 ≤ i , j ≤ N . Theorem 2 . Assuming p ≠ 0 , the limiting distribution of the Markov chain M conditional on the non-absorbing states is independent of p . Proof . Let us check that for p ≠ 0 , the left eigenvectors of M of J are equal . Indeed , if v is a left eigenvector of J with eigenvalue λ , then vJ = λv , which implies that vM = v + pvJ = ( 1 + pλ ) v , that is , v is a left eigenvector of M with eigenvalue 1+ pλ . The converse holds by a very similar argument . In particular , the left eigenvector whose entries are nonnegative and sum to one having largest eigenvalue is the same for M and for J , and so it does not depend on p . By Lemma 1 , such an eigenvector for M is the limiting distribution of the Markov chain on non-absorbing states . From now on , for simplicity , the limiting distribution of M conditional on the non-absorbing states will simply be called the limiting distribution of M . Even though this distribution does not depend on p by Theorem 2 , we will see later that the mixing time does , in the sense that the convergence to the limit distribution is slower if p is small . Our next goal is to describe the limiting distribution of M . The following straightforward result from linear algebra will be useful when determining the eigenvectors of M . Lemma 3 . For each n ≥ 0 , let An be the tridiagonal matrix A n = [ a 1 , 1 a 1 , 2 0 ⋯ 0 0 a 2 , 1 a 2 , 2 a 2 , 3 0 ⋯ 0 0 a 3 , 2 a 3 , 3 a 3 , 4 0 0 ⋮ ⋱ ⋱ ⋱ ⋱ ⋮ 0 ⋯ 0 a n - 1 , n - 2 a n - 1 , n - 1 a n - 1 , n 0 0 ⋯ 0 a n , n - 1 a n , n ] , where the entries ai , j do not depend on n , and let Pn ( x ) = det ( xI − An ) be its characteristic polynomial . Then the following hold: Proof . The recurrence for Pn ( x ) can be obtained easily by expanding the determinant along the last row . To prove part II , note that for 1 ≤ i < n , the i-th component of the vector equation vAn = λv is a i - 1 , i v i - 1 + a i , i v i + a i + 1 , i v i + 1 = λ v i , where we write v = ( v1 , … , vn ) , and we let a0 , 1 = 0 . Solving for vi+1 , we get v i + 1 = 1 a i + 1 , i ( ( λ - a i , i ) v i - a i - 1 , i v i - 1 ) . It now follows by induction and using the recurrence for Pn ( x ) that v i = P i - 1 ( λ ) v 1 ∏ j = 2 i a j , j - 1 . Letting b = v1 we get the stated expression for v . Let PN ( x ) be the characteristic polynomial of the matrix J defined in Eq ( 2 ) . Applying Lemma 3 , we see that it satisfies the recurrence P n ( x ) = ( x + n ) P n - 1 ( x ) + n ( n - 1 ) 4 P n - 2 ( x ) ( 3 ) with initial conditions P0 ( x ) = 1 and P1 ( x ) = x + 1 . For example , for N = 8 , we get P 8 ( x ) = x 8 + 36 x 7 + 504 x 6 + 3528 x 5 + 13230 x 4 + 26460 x 3 + 26460 x 2 + 11340 x + 2835 / 2 . The largest eigenvalue of J , which is the largest root of PN ( x ) , depends on N , as shown in Fig 1 . Using Lemmas 1 and 3 , we can now describe the limiting distribution of M conditional on the non-absorbing states . The i-th component of v in the next theorem is the fraction of viable cells that have i copies of a given chromosome k , in the limit as the number of generations tends to infinity . Theorem 4 . The limiting distribution of the Markov chain M conditional on the non-absorbing states is given by v = 1 ∑ i = 1 N u i ( u 1 , u 2 , … , u N ) with u i = 2 i - 1 i ! P i - 1 ( α ) , where the polynomials Pn ( x ) satisfy recurrence ( 3 ) and α is the largest eigenvalue of J ( equivalently , the largest root of PN ( x ) ) . Proof . By Lemma 1 , the limiting distribution of M conditional on the non-absorbing states is given by the left eigenvector of J with largest eigenvalue α . The result now follows from Lemma 3 , normalizing v so that its components sum to 1 . As shown in the proof of Theorem 2 , if α is the largest eigenvalue of J , then 1 + pα is the largest eigenvalue of M . This eigenvalue determines the limiting growth rate of the tumor , which is the factor by which the number of viable cells multiplies at each generation assuming that karyotypes are distributed according to the limiting distribution . This growth rate is 2 ( 1 + p α ) 23 . Fig 2A shows a graph of this function for N = 8 and varying p . If we modified the model by allowing only a fraction F of the cells to survive at each generation , killing the remaining ones , then the reciprocal of the limiting growth rate , namely 1 2 ( 1 + p α ) 23 , would be the threshold such that for values of F below this threshold , the expected number of viable cells would tend to 0 as g → ∞ , whereas for values of F above this threshold , the size of the colony would grow indefinitely . Finally , Fig 2B shows the proportion of surviving cells , as a fraction of 2g , after g = 1000 generations for different values of p , starting from a cell with 4 copies of each chromosome . The fact that this fraction is close to 1 for very small values of p is another unrealistic prediction of the basic model , which will be addressed by the model with chromosome scores . The limiting distribution described in Theorem 4 is computed in Table 1 for 6 ≤ N ≤ 10 , along with its average , and graphed in Fig 3 for 8 ≤ N ≤ 16 . For every N , the modal chromosomal number is 1 , which agrees with the results of Gusev et al . [9] , although it is not corroborated by experimental observations . In the next section we will describe a better model that will have more realistic outcomes . On the other hand , the average number of chromosome copies depends on N , and it is very close to 3 for N = 8 . Even though Gusev et al . [9] guess from their figures that the chromosome copy numbers reach a “stable distribution” after a few hundred generations and that changes of N “do not affect the results of calculations , ” we remark that the actual limiting distribution is heavily affected by the upper bound N . For example , while for N = 8 the limiting proportion of viable cells with one copy of the chromosome is about 0 . 27432 —which is close to the value observed in [9] with p = 0 . 1 after 200 generations— , for N = 200 this proportion is only 0 . 012984 . Fig 4A–4D and S1 Fig show how the distribution of the number of copies of a chromosome evolves over time in the basic model , for different values of the missegregation rate p . The number of chromosome copies of the founder cell is denoted by f . S1 Fig replicates the data over 200 generations obtained by Gusev et al . [9 , Figs 3A , 4A , 5A] , showing that our simplification 3 does not noticeably affect the outcome for small values of p . Fig 4A–4D shows data for 2000 generations . Note the similarity between Fig 4A and the center panel in S1 Fig . Indeed , for small values of p , increasing the number of generations by a factor of s has a similar effect to multiplying p by a factor of s . This is because ( I + p J ) s ≈ I + spJ . Fig 4D uses a different upper bound N = 16 on the allowed number of copies , and otherwise the same parameters as Fig 4C . The ith row of the matrix Ag , when normalized by dividing by the sum of the entries in the row , gives the distribution of the number of copies of chromosome k in viable cells after g generations , having started with a founder cell that has i copies of the chromosome . Note that before normalizing , the entries of Ag are affected by the choice of the constants ck . However , if we denote by s g ( k ) ( i ) the sum of the entries of the ith row of Ag , then the product ∏ k = 1 23 s g ( k ) ( n k ) is independent of this choice . The expression 2 g ∏ k = 1 23 s g ( k ) ( n k ) is the expected number of viable cells after g generations when the founder cell has nk copies of chromosome k for every k . As in the model without scores , the Markov chain A satisfies the conditions in Lemma 1 . Thus , its quasi-stationary distribution , which is its limiting distribution conditional on the non-absorbing states , is given by the unique vector v ∈ R ≥ 0 N satisfying vA = ρv and ∑ i = 1 N v i = 1 , where ρ is the largest eigenvalue of A . We call this the limiting distribution of A for simplicity , and we note that it does not depend on the number of chromosome copies of the founder cell . However , the analogue of Theorem 2 no longer holds for A: its limiting distribution depends on p . As expected , it also depends on μ ( equivalently , on the chromosome score ) , but not on the constant ck . Indeed , varying C = e c k / 23 only changes A by a constant factor , which does not affect its eigenvectors . Another consequence is that while the number of viable cells in the colony after g generations depends on the parameter c , the limiting distribution of karyotypes among viable cells does not . Theorem 5 . The limiting distribution of the Markov chain A conditional on the non-absorbing states is given by v = 1 ∑ i = 1 23 u i ( u 1 , u 2 , … , u N ) with u i = 2 i - 1 i ! p i - 1 μ ( i 2 + i - 2 ) / 2 P i - 1 ( α ) , where the Pn ( x ) satisfy the recurrence P n ( x ) = ( x - μ n ( 1 - n p ) ) P n - 1 ( x ) - μ 2 n - 1 p 2 n ( n - 1 ) 4 P n - 2 ( x ) with initial conditions P0 ( x ) = 1 , P1 ( x ) = x − μ ( 1 − p ) , and α is the largest eigenvalue of A ( i . e . , the largest root of PN ( x ) ) . Proof . By Lemma 1 , the limiting distribution of A conditional on the non-absorbing states is given by the left eigenvector of A with largest eigenvalue α . Since this eigenvector does not depend on the constant factor C , we can assume that C = 1 , and so qk ( i ) = μi . The entries of A are then A i j = { ( 1 - i p ) μ i if i = j , i p μ i / 2 if | i - j | = 1 , 0 if | i - j | ≥ 2 . Applying Lemma 3 to A , it follows that its characteristic polynomial PN ( x ) satisfies the recurrence in the statement , and that its left eigenvector with eigenvalue α , normalized so that its components sum to 1 , is v . If αk is the largest eigenvalue of A ( k ) , then the limiting growth rate of the tumor is 2 ∏ k = 1 23 α k . ( 4 ) Its value depends on p , on the parameters c , d , and also on the scores of the 23 chromosomes . The estimated values for these parameters that we will use in our figures are c = - 0 . 036132164 and d = 0 . 00039047 . ( 5 ) This value of d was found in [4] using experimental data . On the other hand , our value of c differs slightly from the value in [4] in order to ensure that Qsurv ≤ 1 for all valid karyotypes . Experimental values for the chromosome scores sk were originally found in [8] , and used in [4] . These values are given in Table 2 , together with the corresponding values of μ = e d s k . Fig 5 shows a graph of the growth rate in Eq ( 4 ) as a function of p , with the values of c , d from Eq ( 5 ) and the chromosome scores from Table 2 ( we call these the standard parameters ) . If we were to multiply Qsurv by a factor F to reduce the survival rate for all cells , then the reciprocal of expression ( 4 ) is the threshold for F that determines whether the expected number of cells will tend to zero or to infinity as g → ∞ . The value of the parameter μ = e d s k in human chromosomes , using the estimates for chromosome scores from [8] and for d from [4] , is roughly between 0 . 9994 and 1 . 0012 ( see Table 2 ) . We will use this range for μ in our computations below . Fig 6A–6C shows the limiting distribution described in Theorem 5 for N = 8 , three fixed values of p , and μ varying in the above range . Note that for μ = 1 , which corresponds to a chromosome score of 0 ( this is the score given to the sex chromosome ) , the limiting distribution is the same as in the basic model and it does not depend on p , since in this case A and M differ only by a constant factor . As expected , for higher chromosome scores , the limiting distribution favors higher numbers of copies . Smaller values of the missegregation rate p make the influence of the chromosome scores more noticeable , whereas larger values make the distribution closer to the one in Fig 3 for N = 8 . It is interesting to observe that when the chromosome score is positive ( equivalently , μ > 1 ) , the modal number of copies soon becomes higher than one , and it gets larger as μ increases . This agrees with experimental observations and addresses the main shortcoming of Gusev’s model [9] . Fig 7A shows that for p = 0 . 0025 , small variations of μ in the interval [1 . 0002 , 1 . 0006] cause the modal number of copies in the limiting distribution to take all values between 1 and 5 . The average number of copies and the modal number for three values of p and several values of μ is given in Table 3 . Fig 6D–6F shows the limiting distribution for the experimental values of μ for each of the 23 human chromosomes ( Table 2 ) , for N = 8 and different values of p , as well as the average of these limiting distributions . The average number of chromosome copies in the limit is 3 . 3591 for p = 0 . 001 , 3 . 1618 for p = 0 . 0025 , and 3 . 0107 for p = 0 . 01 . A graph of this dependence on p appears in S3A Fig . We point out that , even though the basic model without chromosome scores also yielded an average number of chromosome copies near 3 for N = 8 ( see Table 1 ) , the shape of the limiting distribution in the basic model was unrealistic , with the modal number of copies always being 1 . The effect of changing the missegregation rate for a fixed chromosome score is shown in Fig 7B , which gives the limiting distributions obtained by fixing μ = 1 . 0004 ( corresponding to a score of sk = 1 . 0242 ) and letting p range from 0 . 001 to 0 . 009 . Next we analyze how these limiting distributions are affected by whole genome duplication . Considering the Markov chain with transition matrix Agd , Fig 6G–6J shows the limiting distribution of chromosome copy numbers for each of the 23 human chromosomes , for N = 8 and different values of both p and the genome duplication rate pgd . Comparing these results to those in Fig 6D–6F , which correspond to the case pgd = 0 ( i . e . , no genome duplication ) , we see that , for rates of pgd below 10−4 , the outcomes are very similar to those of the model without whole genome duplication . On the other hand , larger values of pgd skew the limiting distribution towards higher copy numbers , with this tendency being more noticeable when the missegregation rate p is low . It is shown in [20] that certain karyotypes promote cytokinesis failure and thus genome duplication . In particular , it is suggested that cells with 3 or more copies of chromosome 13 have a higher genome duplication rate . This phenomenon can be incorporated in our model by using different values of pgd in different rows of the matrix G . For example , making the value of pgd increase by a factor of 10 when the number of copies of chromosome 13 is at least 3 , the limiting distribution of the number of copies of chromosome 13 is shown in S4 Fig for different values of the parameters . We see that copy numbers 3 and above become more infrequent in this modified version , compared to the limiting distributions obtained when pgd is independent of karyotype . Unfortunately , when pgd is dependent on the number of copies of chromosome 13 , our model cannot keep track of the distributions of other chromosomes . As discussed above , the normalized rows of the powers of A describe the evolution over time of the distribution of the number of copies of a chromosome . This evolution is depicted in Fig 4E–4L for missegregation rates p = 0 . 0025 and p = 0 . 001 , a founder cell with 2 copies of the chromosome , and different values of μ . The number of generations that it takes for the distribution of chromosome copies to be close to the limiting distribution is determined by the mixing time of the Markov chain . This mixing time is roughly proportional to ( 1 - ρ ˜ / ρ ) - 1 , where ρ and ρ ˜ are the largest and the second largest eigenvalues of A , respectively . S2B Fig plots this quantity as a function of p for different values μ . Whereas the mixing time decreases for larger p , as expected , the dependence on μ is more subtle: values of μ further from 1 ( in either direction ) result in smaller mixing times . In the case μ = 1 , which corresponds to the basic model with no chromosome scores , we have ρ = 1 + pα and ρ ˜ = 1 + p α ˜ , where α and α ˜ are the two largest eigenvalues of J . The quantity ( 1 - ρ ˜ / ρ ) - 1 is plotted in S2A Fig for different values of N . Fig 8 shows the evolution of the average number of copies of each of the 23 human chromosomes ( with the scores from Table 2 ) , as well as the total average number of copies for a random cell , with missegregation rates p = 0 . 001 and p = 0 . 0025 , starting with a founder cell with 2 copies of each chromosome . If we instead use the modified Markov chain A X that incorporates the effects of aneuploidy in early tumor growth , the evolution over time of the distribution of chromosome copy numbers is shown in Fig 4M–4P for different values of the parameters p , mr and μ , when starting with a founder diploid cell with two active copies of gene X . These plots show that there is a sudden transition from the stage when most cells contain active copies of gene X ( that is , states σ and τ in A X ) , to the stage when most cells contain no active copies of gene X ( that is , states 1 , 2 , … , 8 ) . The value of g when this transition happens , which we call time to inactivation , is plotted in S5A Fig as a function of p and mr . We see that the time to inactivation is larger when p and mr are small . S5B Fig displays the fraction of surviving cells ( as a fraction of 2g ) over time , showing that the growth rate of the colony sharply increases when inactivation takes place . As we did in Fig 2B for the model without scores , we can compute the proportion of surviving cells , as a fraction of 2g , after g generations as a function of p . The corresponding graphs for different values of g are given in Fig 9A , starting from a cell with 4 copies of each chromosome and using the standard parameters ( that is , c and d from Eq ( 5 ) and the chromosome scores from Table 2 ) . The y-axis has been normalized for each graph so that the maximum surviving fraction occurs at the same height for each value of g . For g = 1000 , a very similar figure appears in [4] , where it was obtained by running lengthy computer simulations . The value of p that maximizes the fraction of cells that survive after g generations is just under 10−3 for g = 500 and g = 1000 . This optimal value of p decreases slowly as the number of generation g increases . Interestingly , a large surviving fraction of cells is obtained only in a very narrow interval of values of the missegregation rate p , and this fact is more pronounced for large g . Another important characteristic of the colony is its heterogeneity , which in [4] is measured as the Shannon diversity index of its cell scores . Here we propose another related measure of heterogeneity , based on the Shannon diversity of copy numbers of the different chromosomes . More precisely , if ak , j denotes the fraction of viable cells in the colony with j copies of chromosome k , we define its karyotype diversity index K = - ∑ k = 1 23 ∑ j = 1 N a k , j ln a k , j . In our model , the vector ( a k , j ) j = 1 N obtained after g generations starting with a founder cell with i copies of chromosome k can be easily computed by normalizing the ith row of Ag . Fig 9B plots the karyotype diversity index K as a function of p for the same colonies as in Fig 9A , as well as the karyotype diversity index in the limiting distribution . After g = 500 and g = 1000 generations , the karyotype diversity is maximized when p is near 10−3 , close to the value that maximizes the surviving fraction as well . For larger values of g , the curves in Fig 9B reach a local maximum that is not an absolute maximum , and this local maximum shifts to the left as g increases . The reason for this phenomenon is understood when considering K = K ( g , p ) as a function of two variables g and p . The graph of this function appears in Fig 9D . The cross sections for fixed g and varying p are the curves in Fig 9B , and the cross sections for fixed p and varying g are the curves in Fig 9C . For the latter curves , as g → ∞ , the karyotype diversity index K converges to that of the limiting distribution for the given missegregation rate p . As the colony evolves towards this limiting karyotype distribution , it can attain values of K that are higher than the limiting value . For each fixed p , if we let g ( p ) be the value of g that maximizes K ( g , p ) , then g ( p ) is a decreasing function of p . In other words , for smaller missegregation rates p it takes longer for the karyotype diversity to reach its maximum value . When fixing g and letting p vary , this effect translates into some of the curves in Fig 9B having a local maximum at the value of p such that g = g ( p ) . Fig 9D also illustrates that , in the region g ≤ 103 , the value of K ( g , p ) is nearly stable on the curves of the form pg = constant , attaining maximum values when this constant is close to 1 . Interestingly , such high values of K ( g , p ) are only attained for missegregation rates p ≥ 10−3 , after about g ≈ 1/p generations; in contrast , for lower missegregation rates , the karyotype diversity index does never reach such values , see Fig 9C . Finally , we observe that , even though large missegregation rates p yield a high karyotype diversity index K ( see Fig 9B ) , Fig 9A shows that the surviving fraction may be extremely low for such p . A measure of fitness is given by multiplying the surviving fraction from Fig 9A by the karyotype diversity index from Fig 9B . The value of p that maximizes this product is plotted in Fig 9E as a function of g . If one starts with a founder cell with 2 copies of each chromosome , instead of 4 copies , the resulting data is shown in Fig 9F–9I , in analogy to Fig 9A–9D , respectively .
This Markov chain has several advantages over the computational models used by Laughney et al . [4] and in the previous papers [9 , 10] . For example , it allows us to determine , without having to run lengthy computer simulations , the probability that a random cell after g generations has i of copies of a certain chromosome , for any given g , i and an initial distribution of karyotypes . It also yields the expected surviving fraction relative to an exponentially expanding population that does not undergo any cell death . From a theoretical point of view , the main advantage of the Markov chain is that its stationary distribution determines the exact expected distribution of copies of each chromosome as g tends to infinity . Note that the computational model can only make approximate guesses of the behavior in the limit . In this paper we compute the stationary distribution of the Markov chain , thereby obtaining a precise description of the limiting distribution of karyotypes , which agrees with prior observations [4] . This basic model is similar to the one considered by Gusev , Kagansky and Dooley [9 , 24] , which makes basic assumptions about how cells divide and missegregation events take place . Their stochastic model is developed whereby short-term simulations are run . That model uses a semianalytical approach to estimate the long-term behavior of the chromosome copy numbers in cancer cells . For this purpose , and to overcome some of the computational constraints of running the simulations , the authors develop a transition probability model similar to our Markov chain , which they run for as many as 500 generations , using the data to guess that there is a stable distribution in the limit . Let us point out the main differences between the transition probability model used by Gusev et al . [9] and our Markov chain . The first difference is our simplification 3 described in the Methods section , which neglects quadratic terms in p . This simplification , which does not noticeably affect the behavior of the random process for small values of p like the ones observed in experiments , allows us to give an accurate and simple mathematical description of the limit behavior of the Markov chain . Another difference is our simplification 2 , which allows us to interpret the entries of our transition matrix as probabilities of a Markov chain , and therefore apply theoretical results about Markov chains such as Lemma 1 . Finally , the model by Gusev et al . [9] does not impose a realistic upper bound on the number of copies of a chromosome that a viable cell can have , which further complicates the computations , although a variation that imposes an upper bound is considered as well . Based on the figures obtained from their simulations , Gusev et al . [9] observe that after a large enough number of generations ( and for large enough p ) , the fraction of viable cells with i copies of a chromosome seems to converge for each i , but they give no mathematical proof of this phenomenon . One consequence of the analysis of our Markov chain is that we provide a proof of its convergence , and determine exactly what the limit values are . We also prove that these values do not depend on the missegregation rate p ( in contrast to the “weak dependence on p” observed in [9] after 500 generations ) , or on the karyotype of the initial cell ( this is also mentioned with no proof in the Gusev et al . model ) , although they do depend on the upper bound N on the number of allowed copies in viable cells . In trying to remediate the fact that their model predicts a long-term distribution where the most likely number of copies of a chromosome is 1 , which seems to disagree with experiments , Gusev et al . [9 , §4 . 5 . 2] propose an alternative model which allows only one missegregation per chromosome type , as in simplification 3 above . However , this alternative model is significantly different from ours in that they consider the probability that a cell missegregates to be independent on how many copies of the chromosome it has . In practice , in a cell with more copies of a chromosome , it is more likely that some copy missegregates [25] . We remark that the basic model in this section also suffers from the same problem: it has a limiting distribution where the most frequent number of copies of a chromosome is 1 . However , once we incorporate chromosome scores in the full model , we will obtain different limiting distributions that match the experimentally observed ones . Finally , a continuous time model based on the one from Gusev et al . [9 , 24] was developed by Desper , Difilippantonio , Ried and Schäffer [10] . This model uses an exponential distribution for the time between cell divisions , and it allows to vary the cell division rates as a function of the number of copies of the chromosome . In this study , the authors consider the evolution of the average copy number , and obtain some analytic estimates for it . Predicting tumor behavior from single-cell data is critical to our ability to simulate complex processes such as therapeutic resistance . Significant effort has been devoted toward simulating mutational processes in cancer in an attempt to predict resistance to targeted therapies for example . However , these efforts have not incorporated numerical chromosomal instability , a major driver of therapeutic resistance . Our Markov chain can be integrated with other models to account for both mutational heterogeneity as well as chromosome copy number evolution . Integrated models that combine different modes of genomic instability would undoubtedly be better at predicting the process of therapeutic resistance . Such models would generate experimentally testable hypothesis in the laboratory and would be used as a guide to inform clinical management and the selection of anti-cancer therapies . | Chromosomal instability ( CIN ) is a hallmark of cancer and it results from persistent chromosome segregation errors during cell division . CIN has been shown to play a key role in drug resistance and tumor metastasis . While our understanding of CIN on the cellular level has grown over the past decade , our ability to predict the behavior of tumors containing billions of cells remains limited due to the paucity of adequate mathematical models . Here , we develop a Markov-chain model that is capable of providing exact solutions for long-term chromosome copy number distributions during tumor growth . Using this model we confirm the presence of optimal chromosome missegregation rates that balance genomic heterogeneity required for tumor evolution and survival . Interestingly , we show that chromosome copy number heterogeneity is primarily influenced by the rate of chromosome segregation errors rather than the age of the tumor . At chromosome missegregation rates frequently observed in cancer , tumors can acquire maximal genomic heterogeneity after a few hundred cell divisions . This model enables the integration of selection imparted by CIN into existing models of tumor evolution based on somatic mutations to explore their mutual effects . | [
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... | 2018 | A Markov chain for numerical chromosomal instability in clonally expanding populations |
Widespread success of the intracellular bacterium Wolbachia across insects and nematodes is due to efficient vertical transmission and reproductive manipulations . Many strains , including wMel from Drosophila melanogaster , exhibit a specific concentration to the germplasm at the posterior pole of the mature oocyte , thereby ensuring high fidelity of parent-offspring transmission . Transport of Wolbachia to the pole relies on microtubules and the plus-end directed motor kinesin heavy chain ( KHC ) . However , the mechanisms mediating Wolbachia’s association with KHC remain unknown . Here we show that reduced levels of the host canonical linker protein KLC results in dramatically increased levels of Wolbachia at the oocyte’s posterior , suggesting that KLC and some key associated host cargos outcompete Wolbachia for association with a limited amount of KHC motor proteins . Consistent with this interpretation , over-expression of KHC causes similarly increased levels of posteriorly localized Wolbachia . However , excess KHC has no effect on levels of Vasa , a germplasm component that also requires KHC for posterior localization . Thus , Wolbachia transport is uniquely KHC-limited because these bacteria are likely outcompeted for binding to KHC by some host cargo/linker complexes . These results reveal a novel host-symbiont interaction that underscores the precise regulation required for an intracellular bacterium to co-opt , but not disrupt , vital host processes .
The intracellular bacterium Wolbachia is a widespread vertically transmitted endosymbiont present in the majority of insect and filarial nematode species . In many of these associations , Wolbachia appears to confer little benefit to its host , while often incurring large costs [1 , 2] . Given that Wolbachia requires the host for reproduction , yet generally provides little incentive for the host to maintain it , the bacterium has evolved ways of ensuring its transmission through host populations [2] . Wolbachia is found in the germline stem cells of Drosophila ovaries and exhibits coordinated movements at specific developmental stages [3] . Early events are mediated by the microtubule minus-end directed motor dynein [4] and later events by the plus-end directed motor kinesin [5] . Starting in late stage 9 , the wMel strain uses kinesin heavy chain ( KHC ) proteins for transport to the posterior pole coincident with the assembling germplasm . This localization presumably confers efficient vertical transmission , as Wolbachia in this region become incorporated in the germline of the next generation [5] . Significantly , key components of the germplasm also rely on KHC for transport and concentration at the posterior pole [6] . The mechanisms used by Wolbachia to associate with KHC are unknown . Although KHC can bind cargo directly [7] , the linker protein , kinesin light chain ( KLC ) is thought to be necessary for much of KHC transport [8 , 9] . Previous studies of intracellular pathogens revealed evidence for association with both KLC [10] and KHC [11] . Thus , both mimicry of and direct binding to host linker proteins , such as KLC , are possible strategies for an intracellular bacterium to interact with host KHC proteins . The concentration of Wolbachia in the newly formed germplasm of the Drosophila oocyte enabled us to explore how endosymbionts engage host processes and integrate into core structures without disrupting function . In fact , Wolbachia concentrations must reach extremely high levels before disrupting development [12] . Here we investigate the basis of Wolbachia’s association with KHC in the developing oocyte of D . melanogaster , and reveal a novel molecular competitive interaction between host and symbiont . We provide evidence that Wolbachia achieves its normal posterior concentration by being a weak competitor for KHC and its linker proteins , thus ensuring that poleward transport of essential host germline components is not disrupted .
Unless otherwise indicated , the studies presented here focused on stage 10a of Drosophila oogenesis because this time point is well-defined , occurs after the beginning of Wolbachia localization at stage 9 [5] , and is before fast cytoplasmic streaming begins in stage 10b [13] . We quantified Wolbachia using fluorescence intensity of propidium iodide ( PI ) stained oocytes , as previously described [5] ( S1A–S1C Fig ) . Wolbachia were quantified in the whole oocyte , the oocyte posterior , and the posterior pole , adjacent to the cortex ( S1D and S1E Fig ) . In wild-type oocytes 16 . 4 +/- 14 . 4% of Wolbachia resided at the oocyte posterior and 12 . 3 +/- 12 . 6% at the pole ( n = 125 ) . The small differential between these numbers , 4 . 1% , reflects that the majority of fluorescence in the posterior region is associated with the pole cortex , with few fluorescent puncta in the space immediately anterior to the pole . See S1 Fig for method of scoring posterior-localized and cortex-associated Wolbachia . Throughout the rest of the oocyte cytoplasm Wolbachia were evenly distributed , approximately one to two microns apart . These wild-type data were aggregated from the wild-type controls run alongside each genotype dissection , fixation , staining , and imaging run , and are presented in the plots in Figs 1–4 and S4 Fig . Values for total , posterior , and pole fluorescence are presented in S1 Table . We tested the effect of KLC on Wolbachia transport , as it is involved in much of KHC-dependent transport in the host [14] . Unexpectedly , we found that the average oocyte posterior contained 62 . 4 +/- 20 . 5% and the pole contained 40 . 3 +/- 12 . 3% of total fluorescence in KLC RNAi knockdowns . These concentrations of Wolbachia are both significantly more than seen in wild-type oocytes ( 16 . 4 +/- 14 . 4% and 12 . 3 +/- 12 . 6% respectively ) ( n = 26 , p < = 1 . 70E-13 and 3 . 48E-12 , respectively , Fig 1A , 1B and 1D–1G ) . Furthermore , this pattern is dosage-sensitive , as heterozygotes for the null allele Klc[1] exhibited an intermediate phenotype ( Fig 1C and 1D–1G ) , with 40 . 0 +/- 19 . 4% of total Wolbachia fluorescence at the posterior and 30 . 1 +/- 15 . 8% at the pole , both significantly greater than wild-type ( n = 27 , p < = 6 . 91E-10 and 7 . 36E-09 , respectively ) . Importantly , the enrichment in Wolbachia localization was far more pronounced in the region near the oocyte posterior pole ( Fig 1D and 1F ) than at the pole itself ( Fig 1E and 1G ) , suggesting that this pattern is mediated by transport-specific processes rather than binding processes at the cortex . Total Wolbachia abundance in the oocyte was also not significantly different from wild-type in any of the KLC knockdown genotypes ( n = 26 and 27 , p < = 0 . 963 and 0 . 0722 for RNAi and Klc[1] , respectively ) . Furthermore , Wolbachia abundance in Klc RNAi nurse cells was also not significantly different from wild-type ( n = 6 , p < = 0 . 494; S2 Fig ) . One explanation for these observations is that Wolbachia competes with host cargo/KLC linker complexes in association with KHC and for subsequent KHC-driven transport to the oocyte posterior . Thus , reduction of KLC increases the effective amount of KHC available to Wolbachia for poleward transport . To ascertain whether Wolbachia transport is limited by KHC availability , we assayed Wolbachia localization when KHC is overexpressed using two transgenic Drosophila KHC overexpression constructs [15 , 16] . Fig 2A–2D depict and G-J quantify Wolbachia posterior localization in wild-type oocytes and oocytes with excess KHC . Quantification of posteriorly localized Wolbachia in KHC over-expression with the native promoter yields 63 . 6 +/- 15 . 9% at the posterior and 28 . 9 +/- 14 . 4% at the pole compared to wild-type levels of 16 . 4 +/- 14 . 4% and 12 . 3 +/- 12 . 6% respectively ( Fig 2B ) . In addition , oocytes derived from females homozygous ( C ) or heterozygous ( D ) for KHC overexpression driven by the ubiquitin promoter yield 80 . 6 +/- 14 . 7% and 60 . 6 +/- 31 . 1% of Wolbachia at the posterior , respectively , and 36 . 7 +/- 21 . 8% and 27 . 6 +/- 23 . 5% at the pole , respectively . All of these values are significantly greater than wild-type ( Wilcoxon Rank Sum p << 0 . 0001 for posterior % and p <<0 . 01 for pole %; see S1 Table ) . As with KLC knockdown , transport to the posterior region is more significantly increased than transport to the posterior pole cortex ( Fig 2I and 2J ) . Interestingly , in three of six stage 10b or older ubc-Khc++ oocytes examined , excess posterior Wolbachia appear to drift away from the posterior pole in aggregate ( see S3A and S3B Fig ) , presumably due to the onset of fast cytoplasmic streaming . This process may ameliorate the effects of excess transport , as embryonic pole cells appeared to contain equivalent amounts of Wolbachia in wild-type and KHC overexpression backgrounds ( S3C–S3F Fig ) , and neither Wolbachia transmission or host fecundity appear affected in a homozygous ubc-Khc++ overexpression stock after nine-months of infection ( personal observation ) . In addition to direct transport , Drosophila oocytes use KHC for cytoplasmic streaming , which distributes cytoplasmic components throughout the oocyte in late oogenesis [8 , 13 , 17] by two mechanisms: 1 ) the direct transport of cargo churns the cytoplasm [8 , 13] and 2 ) KHC-mediated microtubule sliding generates significant cytoplasmic flows [17] . Given that Wolbachia’s localization to the posterior pole begins at stage 9 , and fast streaming begins in stage 10b , KHC-mediated transport must be direct . Consistent with this prediction , oocytes homozygous for hypomorphic KHC alleles capable of direct transport , but not streaming , exhibit normal Wolbachia distributions [5] . While streaming may not play a role in Wolbachia posterior transport , this KHC-mediated function may limit Wolbachia’s access to the motor protein . To investigate this question , we took advantage of a KHC mutant that specifically disrupts microtubule binding sites in the tail region of kinesin heavy chain [17] . Interestingly , elimination of microtubule binding in the Khc[Kl , MutA] mutant largely recapitulates the excess posterior Wolbachia localization phenotype of KHC overexpression ( n = 17; Fig 2F and 2G–2J ) , with 35 . 7 +/- 19 . 5% of Wolbachia fluorescence at the posterior ( p < = 1 . 09E-05 ) and a less extreme , 18 . 2 +/- 14 . 7% at the pole ( p < = 9 . 36E-03 ) . Wolbachia localization was analyzed in stocks homozygous for a khc null mutation bearing a transgene containing the Khc[Kl , MutA] null microtubule-sliding mutation or a wild-type copy of khc ( Khc[Kl , WT] ) , which exhibited a wild-type Wolbachia localization pattern ( n = 8; Fig 2E and 2G–2J and S1 Table ) . We propose that a likely explanation for these results is that by eliminating the microtubule domain on KHC , more KHC is available for Wolbachia-binding , thus increasing the effective concentration of the motor protein for bacterial transport . To assess how the localization of specific germline determinants responds to variations in KHC dosage , we imaged Vasa as a proxy for the pole plasm via antibody-labelling and a GFP-Vasa fusion protein construct ( Fig 3 and S3G–S3I Fig ) . Vasa is a DEAD-box RNA-helicase protein involved in germ cell specification , oogenesis , transposon silencing , and posterior patterning [18] . In both antibody and GFP methods , a small amount of pole plasm in KHC-overexpressing oocytes appears to drift off of the posterior pole , whereas this is not observed in wild-type ( Fig 3A–3E ) , and is consistent with previous reports [8 , 19] . Despite this qualitative phenotype , the amount of Vasa fluorescence at the posterior pole of KHC-overexpressing oocytes is not significantly different from wild-type ( n = 9 , p < = 0 . 7962; Fig 3I and S2 Table ) . Consistent with prior reports [17 , 19] , deletion of the microtubule-sliding domain in Khc[Kl , MutA] had no effect on the total abundance of pole plasm , however it did appear to produce a more diffuse localization pattern ( Fig 3F ) . Also as expected ( see [19 , 20] ) , knockdown of KLC with RNAi disrupted Vasa localization , and increased its abundance relative to wild-type ( Fig 3G; n = 8 , p < = 9 . 32E-03 , S2 Table ) . However , unlike Wolbachia , the heterozygous Klc[1] allele did not produce a significant increase in pole plasm abundance ( Fig 3H ) . To assess the nature of the interactions between Wolbachia and other KHC linker proteins , we tested knockdowns of Pat1 and Milton , which function in linking KLC [20] and mitochondria [21] , respectively , to KHC . Oocytes expressing the insertion allele Pat1e02477 of the KLC-interacting protein Pat1 transported only slightly more Wolbachia to the posterior ( n = 25; 33 . 29 +/- 26 . 84% ( p < = 9 . 22E-04 ) ) and posterior pole ( n = 25 , 26 . 29 +/- 23 . 65% ( p < = 1 . 54E-03 ) ) than wild-type ( 16 . 4 +/- 14 . 4% and 12 . 3 +/- 12 . 6% respectively ) , whereas the null allele Pat1robin and the insertion allele Pat1EY15664 transported levels of Wolbachia to the posterior pole indistinguishable from wild-type ( Fig 4 and S1 Table ) . These data indicate that Wolbachia does not compete with this protein to the same degree it does with Pat1’s binding partner KLC . While the relative % posterior and % pole values were not significant , Pat1robin oocytes did exhibit significantly more total posterior and pole fluorescence than wild-type ( n = 18 , p < = 1 . 07E-04 and 3 . 50E-04 , respectively ) . Knockdown of Milton by both of the tested RNAi constructs ( see S1 Table ) significantly increased Wolbachia transport , but Val 22 produced more consistent results , so we acquired more data for that genotype . On average , the Milton Val 22 RNAi oocytes exhibited 43 . 54 +/- 19 . 51% at the posterior and 34 . 44 +/- 16 . 81% at the pole ( n = 23 , p < 7 . 03E-09 and 2 . 91E-08 , respectively ) , both significantly elevated relative to wild-type , consistent with a model whereby linker knockdown frees up KHC .
Efficient maternal transmission from one generation to the next necessitates that Wolbachia are transported through the oocyte to the posteriorly localized host germplasm . Transport from the nurse cells to the anterior region of the oocyte relies on the minus-end motor dynein , and transport from the anterior to the posterior oocyte relies on plus-end directed Kinesin ( Fig 5A [3 , 5] ) . To identify whether host KHC linker proteins are employed by Wolbachia to bind to KHC for posterior transport in the developing host oocyte , we screened mutants in candidate linker proteins for those that specifically disrupt Wolbachia posterior localization , finding a novel competitive interaction between host cargo and Wolbachia for KHC transport . Previous studies demonstrated that Wolbachia posterior localization requires KHC [5] , and KLC is required for much of KHC-dependent transport [8 , 9] . Thus , we examined Wolbachia localization in stage 10a Drosophila oocytes with severely reduced levels of KLC to test its necessity for transport of the bacteria . To our surprise , not only is Wolbachia able to localize to the posterior pole when KLC levels are reduced , but the level of Wolbachia localized at the pole increased . This result demonstrated that Wolbachia achieves its posterior localization through a mechanism independent of KLC . Wolbachia may bind KHC directly or through another linker protein [9] , potentially of bacterial origin [11] . An explanation for why knockdown of KLC levels results in an increase in posteriorly localized Wolbachia is that the bacteria compete with host cargos for access and association with KHC ( depicted in Fig 5B ) . Knocking down KLC reduces the amount of KHC in the form of the heterotetramer Kinesin-1 and the number of KLC-dependent cargos binding KHC . This may increase the amount of KHC available to interact with Wolbachia . To test this idea , we overexpressed KHC and found a similar , yet more intense posterior localization phenotype than when KLC is knocked down . The severity of the phenotype was proportional to the degree of overexpression ( see Fig 5C ) . Furthermore , selective knockdown of the microtubule-sliding function of KHC also recapitulates the overexpression phenotype . Together , these results indicate that Wolbachia competes with its host for use of KHC , and its transport is limited by the reduced concentration available for bacterial binding ( Fig 5B ) . Given that KLC may play a role in regulating KHC [23] , we note that this interpretation is consistent with an alternative mechanism in which KLC indirectly negatively regulates Wolbachia transport through signaling . That KHC overexpression has little effect on the localization of the pole plasm component Vasa indicates that this competitive interaction , resulting in KHC-limitation for Wolbachia , is not a general property of pole plasm components and may be specific to Wolbachia . Indeed , KHC is not thought to be limiting for most host processes . For example , it was not found to be dosage sensitive in direct cellular transport [24] , lipid droplet transport in early embryos [25] , or cytoplasmic streaming [13 , 17] . While pole plasm localization , as quantified via Vasa-labeling , is clearly altered qualitatively by KHC overexpression ( Fig 2B and 2H and [8] ) , our results show that the amount of pole plasm localized was not significantly different from wild-type . Furthermore , that KLC knockdown significantly alters pole plasm localization , whereas 9x KHC overexpression does not , also indicates that Wolbachia and transport of at least some pole plasm components are regulated differently . Given that in other insects , such as leafhoppers , posterior determinants associate and move with the endosymbiont , it is likely Wolbachia also tightly binds specific pole plasm components [12 , 26] . Identifying pole plasm components that are mislocalized with Wolbachia upon over-expression of KHC may provide a means of identifying putative host binding partners . Wolbachia are present throughout oocyte development , and thus must avoid disrupting normal processes . During this period of maturation , the oocyte dramatically increases in volume and key determinants establishing the anterior/posterior and dorsal/ventral axes and germplasm are transported , positioned , and activated [27] . Wolbachia must replicate , migrate , and concentrate at the posterior pole without interfering with host oocyte development . Previous studies demonstrated that an over-abundance of Wolbachia in the oocyte disrupts axis formation [12] , indicating that Wolbachia titer and localization must be strictly regulated . The studies here suggest that it is advantageous for Wolbachia , an obligate intracellular bacterium , to not disrupt key host cargo during the vertical transmission process , i . e . , host development . Furthermore , while KHC-dependent transport is generally not dosage-dependent , there is likely some lower-limit on the amount needed for normal host functions , making Wolbachia’s relative affinity for it more important in these limiting situations . Our finding that Wolbachia acts as a weak competitor for motor protein based transport contrasts with examples of viruses and other bacterial pathogens that outcompete host cargo for use of molecular motors [28 , 29] . It is likely that this difference is due to the fact that , despite being a manipulative , quasi-parasite , Wolbachia’s primary mode of transmission is vertical , so it is disadvantageous to be too effective at co-opting host proteins for its own use . Therefore , Wolbachia is likely to be strongly selected to compete poorly with host biology in order to avoid conflict and reduced host fitness ( Fig 5D and [30] ) . Future work on other strains of Wolbachia and species of hosts that exhibit different localization patterns during oogenesis and have been co-evolving for different lengths of time will help determine how much competitive interactions at subcellular levels between Wolbachia and its hosts drive vertical transmission strategies .
wMel Wolbachia were previously crossed into two D . melanogaster fly stocks , one carrying the markers and balancers w[1]; Sp/Cyo , Sb/Tm6 , Hu and the other carrying the germline double driver: P{GAL4-Nos . NGT}40; P{GAL4::VP16-Nos . UTR}MVD1 . These infected double balanced and ovary driver stocks were used to cross wMel into the null/hypomorphic mutants and RNAi TRiP lines to ensure that all wMel tested were of a similar genetic background . The D . melanogaster strains obtained from the Bloomington Drosophila Stock Center at the University of Indiana were: P{lacW}Klc59A P{FRT ( whs ) }2A/TM6B , Tb+ , y1 w67c23 P{EPgy2}Pat1[EY15664] , w[1118] PBac{RB}Pat1[e02477] , y[1] v[1]; y1 w* Pat1[robin] , y1 v1; P{TRiP . HMC02365}attP2 , P{TRiP . GL01515}attP2 . KHC-overexpression stocks w[1]; Sp/CyO; P{w+ Khc+}3 and w[1]; Sco/Cyo; P{w+ ub-Myc::Khc+}3 , which were obtained from Bill Saxton’s Lab at UCSC ( from [15 , 16] , respectively ) . The microtubule sliding mutant KHC and insertion control stocks , w; Khc[KI , WT]/ ( CyO , Kr-Gal4 , UAS-GFP ) and w; Khc[KI , mutA]/ ( CyO , twist-Gal4 ( w+ ) , UAS-2XEGFP ) , were obtained from Vladimir Gelfand [17] . We obtained the Vas-GFP stock , w[*]; P{w[+mC] = vas . EGFP . HA}2 , from the KYOTO Stock Center ( DGRC ) in the Kyoto Institute of Technology . All fly stocks and crosses were maintained at room temperature on white food ( BDSC Cornmeal Food ) , as the sugar/protein composition of host food affects Wolbachia titer [31] . Newly enclosed flies were transferred to fresh white food and aged 3–5 days . As described previously [5] , up to 10–15 flies from each cross were dissected , fixed , and stained with propidium iodide ( PI ) at a time . Briefly , ovaries were dissected , separating the ovarioles with pins , and fixed in 200 μl devitellinizing solution ( 2% paraformaldehyde and 0 . 5% v/v NP40 in 1x PBS ) mixed with 600 μl heptane for 20 min at room temperature , with agitation . Then , oocytes were washed 5x in PBS-T ( 0 . 1% Triton X-100 in 1x PBS ) , and treated with RNAse A ( 10 mg/ml ) overnight at room temperature . After washing six times in PBS-T , oocytes were incubated overnight in PI mounting media ( 20 μg/ml in 70% glycerol and 1x PBS ) , and mounted on glass slides . The Wolbachia-infected double balancer or nos-Gal4 driver stocks were used as wild-type controls . Experimental samples and control samples were processed simultaneously to minimize batch effects . Slides were imaged immediately or stored at -20°C degrees for no more than a week . While wild-type controls were processed alongside every experiment , oocyte localization variation within a run was no different from variation between runs , for wild-type and mutant genotypes , so these samples were pooled across processing runs to increase sample size and statistical power . Furthermore , no more than about five genotypes with 10 flies each could be dissected and processed at a time , making large , single-experiment sample sizes infeasible . Ovaries were dissected and fixed as described for PI staining , except PBS-Tw ( 0 . 2% Tween 20 in 1x PBS ) was used in the wash steps , and no more than five ovaries were prepared at a time to ensure proper Vas protein fixation . Following RNase A treatment overnight and washes , oocytes were blocked in 1% bovine serum albumin in PBS-Tw for one hr at room temperature . Then the ovaries were incubated in PBS-Tw containing anti-Vas antibody ( Developmental Studies Hybridoma Bank ) at a 1:200 dilution overnight at 4°C . The next day , following four washes in PBS-Tw , oocytes were incubated in secondary antibody ( Alexa 633 conjugated goat anti rat , Invitrogen ) at a 1:500 dilution in PBS-Tw overnight at 4°C . Following four washes in PBS-Tw , ovaries were incubated overnight in PI mounting media and then mounted on glass slides , as described above . KHC homozygous virgins ( males and females ) were collected for 1–3 days before being crossed . Flies laid embryos for 3 hrs at room temperature on grape juice containing agar plates topped with white food . The collection period lasted 2–4 days . Embryos were collected and dechorinated in a 50% bleach solution for 1–2 min , and rinsed in DI water for 1 min . Embryos were then transferred into a 1:1 volume ratio of heptane and 37% formaldehyde for 5 min . The formaldehyde was removed and embryos were placed in a 1:1 solution of heptane and methanol . Heptane was removed and embryos were stored in methanol at 4 degrees . The embryos were rehydrated in PBTA ( phosphate-buffered saline [PBS] + 0 . 1% Triton X-100 + 0 . 05% sodium azide ) before staining . The embryos were then incubated in RNAse at 37°C for 2 hrs . After 4 washes of PBTA , the embryos were mounted in mounting media containing propidium iodide ( PI ) and viewed with confocal microscopy . Slides were stored at -20°C degrees . Following [32] , oocytes of 5-day old females were dissected in cold 1x PBS and ovarioles were separated . Oocytes were incubated on slides in a mixture of 1:100 Syto 11 ( 5 mM; Invitrogen ) and 1:1000 MitoTracker Red ( Molecular Probes , M-7512 ) in 1x PBS on ice for 20 min in the dark . Coverslips were applied , slides were stored on ice , and oocytes were imaged on an SP5 confocal microscope before 145 total minutes had elapsed . Oocytes were imaged on a Leica SP5 confocal microscope with a 63x objective . Optical sections were taken at the Nyquist value for the objective , every 0 . 38 μm , at a magnification of 1 . 5x . Propidium iodide was excited with the 514 and 543 nm lasers , and emission from 550 to 680 nm was collected . GFP was imaged with the 488 nm laser , and emission from 488 to 540 nm was collected . Alexa 633 was imaged with the 633 laser , and emission from 606 to 700 nm was collected . Approximately one μm thick sections were 3D reconstructed from three sections near the middle plane of the oocytes in ImageJ . For fluorescence quantification , the selection tool in ImageJ was used to isolate the oocyte and then the brightness/contrast tool was used to increase the threshold on the image to the point that only white puncta corresponding to Wolbachia staining were retained , and all background noise was rendered black ( see S1 Fig for a visual explanation ) . Fluorescence intensity was measured by setting ImageJ to measure: AREA , INTEGRATED DENSITY and MEAN GRAY VALUE , and measuring: three sample background selections , the whole oocyte , the posterior region , and the posterior pole . Then the corrected total cell fluorescence ( CTCF ) was calculated for each region of the oocyte as follows: CTCF = Raw Integrated Density–Area of selected cell X Mean fluorescence of background readings The resulting fluorescent intensities and relative proportions of fluorescence intensity were plotted , analyzed , and statistics were calculated in R . Total and relative fluorescence intensities between oocyte genotypes were compared with the nonparametric Wilcoxon Rank Sum test . See Figs 1–4 , Fig 5C , S2 Fig , S1 Table , S2 Table | The intracellular bacterial symbiont Wolbachia uses host motor proteins for microtubule-based transport to the posterior pole of the developing host oocyte , coincident with the future germline , and yet it does not interfere in this process . We present evidence here that Wolbachia competes poorly with key host cargos for access to one of these motor proteins , making Wolbachia transport limited by its availability . Given that intracellular pathogens tend to be effective competitors for their host proteins , these results suggest that the vertically transmitted bacterium Wolbachia has evolved as a weak competitor , potentially to mitigate its impact on normal host biology , and thus increase its own reproductive success . | [
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"experimen... | 2018 | Wolbachia and host germline components compete for kinesin-mediated transport to the posterior pole of the Drosophila oocyte |
Small globular proteins and peptides commonly exhibit two-state folding kinetics in which the rate limiting step of folding is the surmounting of a single free energy barrier at the transition state ( TS ) separating the folded and the unfolded states . An intriguing question is whether the polypeptide chain reaches , and leaves , the TS by completely random fluctuations , or whether there is a directed , stepwise process . Here , the folding TS of a 15-residue β-hairpin peptide , Peptide 1 , is characterized using independent 2 . 5 μs-long unbiased atomistic molecular dynamics ( MD ) simulations ( a total of 15 μs ) . The trajectories were started from fully unfolded structures . Multiple ( spontaneous ) folding events to the NMR-derived conformation are observed , allowing both structural and dynamical characterization of the folding TS . A common loop-like topology is observed in all the TS structures with native end-to-end and turn contacts , while the central segments of the strands are not in contact . Non-native sidechain contacts are present in the TS between the only tryptophan ( W11 ) and the turn region ( P7-G9 ) . Prior to the TS the turn is found to be already locked by the W11 sidechain , while the ends are apart . Once the ends have also come into contact , the TS is reached . Finally , along the reactive folding paths the cooperative loss of the W11 non-native contacts and the formation of the central inter-strand native contacts lead to the peptide rapidly proceeding from the TS to the native state . The present results indicate a directed stepwise process to folding the peptide .
In recent years , extensive investigation has been undertaken of the folding of small proteins and peptides that can be approximated as two-state folders . In these systems , only two stable populations are detected ( folded and unfolded ) , separated by a single effective free energy barrier with only one kinetically important transition state ( TS ) , the reaching of which can be considered as the rate limiting step [1] . The determination of the transition state ensemble ( TSE ) of these two-state systems and how it is traversed are , therefore , fundamental to our understanding of the physicochemical basis of protein folding [2] . Recent research on the TSE has been based on results from simulations [3]–[7] , experimental mapping of site-specific contacts in the TSE by protein engineering [8] , [9] and mixed experimental/computational approaches [10]–[20] . To obtain insight at residue-level detail , -values are commonly used to investigate the formation of side-chain interactions in the TSE by mutating residues and assessing the effect on folding kinetics . While -values do not , by themselves , directly provide structural information on the TS , they have been broadly used as structural restraints on a range of computational models ( e . g . , Go models [10] , [20] , Monte Carlo simulations [11] , high-temperature , biased molecular dynamics ( MD ) [14] , with both implicit solvent [15] , [16] and all-atom MD representations [6] ) . However , it has been shown that not all conformations obtained in MD simulations by using the -value as a restraint belong to the TS [21] . An alternative approach is to obtain the transition state ensemble from the maximum of the free energy surface projected onto selected folding coordinates , as has been done in many previous studies e . g . , [6] , [22]–[27] . The choice of proper reaction coordinates for protein folding is non-trivial , and whether a transition state identified using any given pair of progress variables corresponds to the transition state using another pair is not known a priori . In many of the above studies it was concluded that global coordinates based on the native topology of two-state proteins or peptides fully satisfy the criteria needed to accurately identify and describe the TSE . However , the need for further analysis of the folding/unfolding probabilities to validate the transition state ensemble has been emphasized [6] , [14] , [24] , [28] . To address this , the identification of a TSE from free energy surfaces combined with validation of the TS structures found provides a secure way to characterize the transition state for folding . Various hypotheses have been made concerning TS structures , varying from fully native [29] and partly native [17] , [30] to denatured topologies [31] . Apart from the topology , there is debate as to whether the TS represents an ensemble with a single , unique nucleus [31]–[33] or a heterogeneous population of conformations , e . g . , some with structure formed and others with structure absent [16] , [20] , [34] . On the one hand , it has been suggested that CI2 [32] and other proteins [35] contain specific contacts in the TS which are crucial to folding . On the other , heterogeneous TS theory involves the existence of multiple transition state ensembles through which parallel folding paths pass [20] . Arguably even more challenging than the structural characterization of the folding TS is the characterization of the TS from a dynamical point of view . Information on the mechanisms by which the TS can be reached and left along reactive folding trajectories , commonly named transition-path trajectories , is scarce . For this purpose , transition path sampling [4] , [5] is often used , in which , given an initial reactive path , a shooting algorithm is employed to collect transition paths by perturbing the initial path . Although this method can generate an ensemble of transition paths , an initial reactive trajectory is needed , which is commonly generated through high-temperature unfolding simulations [5] . However , as the potential energy surface sampled at higher temperature is formally different from that visited at room temperature , unfolding through high temperature may well occur through pathways that are very different from the folding routes at lower temperatures . For example , a comparison between unfolding simulations performed at elevated temperature and folding simulations at room temperature has revealed that unfolding pathways lack important intermediates and often resemble œfast-track of folding [36] . Thus , an increase in temperature may actually change the folding process rather than simply accelerating it [37] . Hence , there remain significant advantages to characterizing folding processes using long-timescale simulations at room temperature . In the present study , we examine the transition state ensemble and folding dynamics of a model system , Peptide 1 , a -hairpin peptide of 15 residues ( Figure 1a ) [38] . Although Peptide 1 is a designed peptide , the turn sequence , NPDG , is statistically the most abundant type I turn in proteins , enhancing the relevance of the study of its folding mechanism for natural proteins [39] . This peptide has been found to fold via a two-state mechanism in 0 . 8 s , as determined by the T-jump technique in combination with IR [38] , [40] . In our previous work , the folding kinetics of this peptide was examined using multiple independent s-timescale all-atom MD trajectories in explicit solvent , yielding a folding time in accord with the above experimental datum [41] . Here , we derive the configurations of the TSE from the free energy folding landscape of Peptide 1 generated by multiple atomistic MD simulations over a total simulation time of 15 s . The trajectories were started from fully unfolded structures and several spontaneous folding events to the NMR-derived [38] -hairpin were observed , thus enabling a dynamical characterization of the evolution of the peptide through the folding TS . To our knowledge , this is the first time that multiple s-long explicit solvent , unbiased simulations with clear unfolding-folding transitions have been used to characterize structurally and dynamically the TS in folding studies . The MD trajectories allow determination of the mechanism by which the TS is reached and subsequent events in folding pathways . The role of non-native interactions is characterized . It is found that , rather than being reached and crossed by highly-random fluctuations ( i . e . , through very different and heterogeneous pathways ) , folding is characterized by a directed , stagewise process involving the formation of specific structures before , during , and after the transition state for folding , corresponding to a structured folding pathway .
Six s-timescale atomistic MD simulations of Peptide 1 in explicit solvent ( total of 15 s ) were performed , starting from unfolded structures , and folding to the native NMR-derived conformation [38] was observed in all of them [41] . The six trajectories were used to evaluate the free energy landscape of the system using two progress variables based on the native topology of the peptide: the R parameter , containing information on the backbone , and the fraction of native contacts ( ) containing information on the sidechain packing ( Figure 1b ) . These two order parameters were chosen in order to capture most of the information relevant to folding and are based on the native topology . Indeed , it has been shown that for two-state peptides , such as the present , global coordinates based on the native topology fully satisfy the criteria needed to accurately identify and describe TSEs [25] . The corresponding free energy map can be divided into three distinct regions: the folded state F , the unfolded state U and a single barrier , the transition state TS ( see caption of the figure for the state definitions ) . Concerning the sensitivity of TS structures derived from the selected reaction coordinates , an additional free-energy landscape was calculated as a function of the RMSD- pair of variables . TS structures derived from the RMSD- landscape were compared with the original TS structures of the R- plane . The overlap between the two TSE is approximately 70 , which is acceptable and further supports the choice of the original progress variables . The TSE was slightly refined . We have noted that in one of the folding transitions ( along trajectory 6 ) that was initially used to define the TSE , the peptide actually folds to a conformation that falls into the F state definition ( R = 4 . 8 , = 0 . 65 ) , but is actually non-native , having a clearly non-native turn . This conformation does not populate the free energy minimum , but rather a region with a A value of 10 kJ/mol ( see Figure S1 ) . We , thus , excluded the TS structures sampled along this transition from the final TSE . To estimate the reliability of the free energy surface the convergence of the free energies associated with individual grid cells of the plane was examined ( note that free energy values are defined with respect to the grid cell corresponding to the global minimum ) . Figure 2a shows a typical convergence plot for two given grid cells , one in the TS ( Figure 2a shows a typical convergence plot for two given grid cells , one in the TS ( 14 kJ/mol ) and the other in the local minimum of the unfolded state ( 2 kJ/mol ) . After about 5–10 of sampling for all grid cells , the values are rather stable ( within 1–2 kJ/mol ) . Figure 2b shows the probability distribution of the free energy standard deviations , ( see Methods section for the estimate of the errors ) , for all the grid cells . Again , these show relatively small statistical errors in the free energy values ( again within 2 kJ/mol ) . Overall , the free energy landscape of Peptide 1 represents a typical two-state folder , consistent with the mono-exponential folding kinetics observed in both laser-induced temperature-jump experiments [40] and in our s MD simulations as reported previously [41] . Thus , the following minimal mechanism can be assumed: Four possible dynamical scenarios emerge ( see Figure 3a ) : the peptide climbs from the unfolded basin to the TS and either descends forward to the folded state ( forward reactive path UTSF ) or falls back to the unfolded basin ( non-reactive path UTSU ) ; or the peptide climbs from the folded basin to the TS and either descends to the unfolded state ( backward reactive path FTSU ) or falls back into the folded basin ( non-reactive path FTSF ) . The occurrence of conformations belonging to the TS , F and U states can be followed in time along the six unbiased MD trajectories . Examples of the dynamical evolution of the peptide through the three states are shown in Figure 3b . All four cases described above occur . In all four scenarios , once the TS is reached , fast recrossings of the TS surface are observed before the final descent to the end-state . Particularly remarkable is the fact that many of the TS structures , which were extracted based on a thermodynamic criterion , indeed occur right at the folding and unfolding transitions along the ( unbiased ) trajectories ( top and middle panels of Figure 3b ) , thus confirming the validity of the thermodynamic TS selection . In addition , TS structures occurring in non-reactive paths ( bottom panels of Figure 3b ) are also observed , corresponding to very short excursions into the TS compared to reactive paths . In what follows , the kinetics and structural features of the TSE are presented . Furthermore , to determine what triggers the folding events , the TS structures visited along forward reactive paths are examined in detail . To characterize the TS kinetics , the TS lifetime ( ) , here defined as the mean residence time in the TS , was evaluated . The distribution of the residence times ( Figure 4 ) was fitted by a monoexponential function ( dotted line ) yielding a of 10 . 20 . 5 ps ( for the estimate of the error see Methods ) , with 52 . 7% and 47 . 3% of the TS population ending up in the folded and unfolded states , respectively . The correlation coefficient is higher than 0 . 9999 , showing the goodness of the fit . These data are consistent with a two-state kinetic model [42] , thus further indicating that the selected TSE is reliable . The relatively short ( ps-timescale ) TS lifetime obtained , showing that the TS is a short-lived state , is consistent with the fast recrossings of the TS observed prior to the final descent ( see Figure 3b ) . In order to examine the degree of native topology in the TSE , the formation of turn , middle and end-to-end inter-strand contacts was evaluated and compared with the corresponding contacts in the folded conformations . Figure 5a shows the distributions of the distances between the C atoms of residues S1-E15 ( end ) , I4-T12 ( middle ) and N6-G9 ( turn ) in the TSE . The distributions are quite narrow , indicative of a rather homogeneous topology of the TSE population . However , a minor peak is present in the middle distribution at 0 . 55 nm that will be discussed below . The “end” and “turn” distributions in the TSE , peaked at 0 . 42 and 0 . 55 , respectively , are native-like , the only slight difference being a shift of the “turn” peak maximum to longer values ( 0 . 55 nm versus 0 . 60 nm in F and TSE , respectively ) . In contrast , the middle-contacts distribution differs significantly in the two states , the peak maximum being at a much longer distance in the TSE ( 0 . 72 nm ) than in F ( 0 . 54 nm ) . Thus , the distinct feature of most of the TSE is the formation of native-like end-to-end and turn contacts , while the central parts of the strands are disordered . The presence of the end-to-end contact is consistent with recent experimental results on several proteins suggesting that the TS exhibits an overall native-like topology in which the N-terminal and C-terminal regions are in close proximity [17] , [32] , [43] . As was found for the backbone topology , the native sidechain contacts at the end of the hairpin are present in the TSE ( data not shown ) . Contrarily , the sidechains of the central parts exhibit some persistent non-native interactions . In particular , W11 , the bulkiest of the sidechains , forms non-native contacts with two residues of the turn , namely P7 and G9 , in a kind of key-lock configuration ( see Figure 5b , in which the distributions are plotted of the minimum distances between the W11 sidechain and the P7 and G9 sidechains , together with representative configurations ) . To analyze the origin of the above-mentioned minor peak in the “middle” distribution , possible differences in the topological features of TS structures sampled along reactive ( UTSF and FTSU ) and non-reactive ( FTSF and UTSU ) pathways were investigated . Separating the TSE into UTSF , FTSU , FTSF and UTSU structures is also useful for identifying the presence , if any , of a specific folding nucleus , i . e . , a nucleus of contacts resulting in rapid assembly of the native state [24] , [44] , thus triggering the descent of the peptide from the TS to the folded basin . The folding nucleus is more likely to be retained in the FTSF conformations than in the UTSU conformations [24] , [44] . Therefore , we calculated the distribution of “turn” , “middle” , “end-to-end” and W11-P7/W11-G9 contacts in the TS structures belonging to UTSF , FTSU , FTSF and UTSU paths separately ( see Figure 6 ) . It is found that , TS structures apart from the FTSF population are very homogeneous , with the topological features described above ( i . e . , the turn and end-to-end contacts are formed , the central part of the backbone is disordered and the W11 sidechain is positioned in the middle of the turn at around 0 . 55 nm from P7 and G9 ) . In contrast , however , in about 65 of the FTSF structures ( brown color in Figure 6 ) , which correspond to the minor peak mentioned above , the middle backbone contacts are formed , the end-to-end contacts is slightly looser and the W11 sidechain is closer to P7 ( at a distance of 0 . 3 nm ) than to G9 . Hence , according to the definition given above , the folding nucleus is likely to be characterized by an almost-native backbone topology and a persistent , non-native sidechain contact between W11 and the P7 turn residue . The mechanism by which the peptide crosses the TS is now examined . To determine whether the end and turn contacts are formed only at the TS or prior to it , the time evolution of the corresponding C-C distances was calculated . Figure 7 shows these time series for four representative forward reactive paths . Clearly , prior to TS the turn is already formed , while the end-to-end contact forms only at the TS . These results indicate that end-to-end contact formation is a discriminating feature for the TSE of Peptide 1 . This trend is observed in all forward reactive paths . The final stage of the folding process , i . e , the descent of the peptide from the TS to the F state , involves the correct arrangement of the middle part of the hairpin . Figure 8 plots the C-C distances of the end ( S1-E15 ) , middle ( Y4-T12 ) and turn ( N6-G9 ) residues again as a function of time for four representative reactive folding paths . The end and turn regions of the hairpin remain in contact during the final stage , while the middle part comes into contact . Finally , the time evolution of the non-native contacts formed by the W11 sidechain with the turn is analyzed . These contacts are present already prior to the reaching the TS , locking the P7-G9 residues into the “reactive” turn conformation ( see Figure 9 ) . In the final stage of the process , the W11 sidechain loses the non-native contacts with the turn , allowing a concomitant rearrangement of the central segments of the strands into the native -hairpin conformation ( see Figure 9 ) . The above results indicate a clear TS folding mechanism that is summarized in Figure 10 . In this mechanism , the TS is reached with the formation of end-to-end and turn contacts , with the turn appearing prior to the TS and the end-to-end interaction appearing only at the TS . Reaching the folded state from the TS requires W11 sidechain repositioning and concomitant native rearrangement of the central segment of -hairpin . However , despite a common TS topology and a single , directed folding-transition mechanism was observed from extensive MD simulation , the existence of minor folding pathways cannot be excluded .
A variety of computational models and methods has been applied to extract information on the TSE for protein folding , including Go models [6] , [10] , [45] , high temperature [14] and implicit solvent [15] , [16] MD simulations , and transition path sampling [4] , [5] . However , the uncertainty involved in these methods ( e . g . , unnaturally high temperature or absence of explicit water ) leads to uncertainty in the interpretation of the data [46] . To our knowledge , no simulation studies exist that have characterized the TSE using unbiased atomistic simulation in explicit solvent , in which folding events from fully unfolded conformations occur . In the present work , we have used this information to directly evaluate TS structure and kinetics from multiple s-timescale all-atom explicit MD simulations of the -hairpin Peptide 1 . The main findings of the study can be summarized as follows . Structurally , the TS consists of a rather homogeneous loop-like topology characterized by native end-to-end contacts and specific non-native interactions in the middle region of the loop characterized by the W11 sidechain centrally locked in between the turn residues P7 and G9 . A specific folding nucleus , i . e . , the nucleus of contacts resulting in rapid assembly of the native state , was also identified . This is characterized by loose end-to-end contacts and the presence of native contacts in the middle part of the hairpin , with a concomitant shift of the W11 sidechain towards the sidechain of P7 . Concerning the folding mechanism , while the turn and its non-native contacts with the W11 sidechain are already formed prior to the TS , the end-to-end interaction appears only at the TS , making it a unique feature of the TS . The TS is short-lived , with a mean lifetime on the ps-timescale . The final stage in reaching the native state , occurring after crossing the TS , involves the cooperative loss of the non-native contacts and formation of the native inter-strand contacts in the central part of the hairpin . These last events are those committing the reactive trajectories to very rapidly proceeding from TS to F . The present results support previous experimental findings on other systems suggesting that the formation of end-to-end contacts in the TS may be a fairly general phenomenon in the folding of small proteins [17] , [32] , [43] . In the present case , there exists an additional structural feature specific to -hairpins that further restricts the conformational variety of the TS structures , namely the formation of the turn . From a dynamical point of view , W11 acts as a chaperone for reaching the TS through the formation of non-native interactions with the turn . The W sidechain locks the turn in place prior to TS , allowing the ends to subsequently come into contact , i . e . , the TS to be reached . Hence , also from a dynamical point of view our results are in favour of a “directed” , stagewise process [11] , rather than a large number of heterogeneous pathways [47] , [48] characterizing the reaching and crossing of the TS . Further studies on other peptides and proteins will clarify the generality of the present observation of the structuring of the pathway across the transition state for folding .
A series of six 2 . 5 s-long atomistic MD simulations of Peptide 1 ( SESYINPDGTWTVTE ) in explicit solvent was performed [41] . Six starting structures representing the unfolded state were extracted randomly from a simulation of 50 ns that was started from a fully extended conformation of the peptide . The MD simulations were performed with the program GROMACS [49] with the OPLS-AA all-atom force field [50] for the peptide . The water was modelled using the TIP4P representation [51] . Each of the six starting conformations was placed in a dodecahedral water box large enough to contain the peptide and at least 1 . 0 nm of solvent on all sides ( volume48 nm ) . Each simulation box contained 6647 atoms . Periodic boundary conditions were applied and the long range electrostatic interactions were treated with the Particle Mesh Ewald method [52] using a grid spacing of 0 . 12 nm combined with a fourth-order B-spline interpolation to compute the potential and forces in-between grid points . The real space cut-off distance was set to 0 . 9 nm and the van der Waals cut-off to 1 . 4 nm . The bond lengths were fixed [53] and a time step of 2 fs for numerical integration of the equations of motion was used . Simulations were performed in the NVT ensemble with isokinetic temperature coupling [54] keeping the temperature constant at 300 K . Three Na counterions were added , replacing three water molecules , so as to produce a neutral simulation box . All the starting structures were subjected to a two-stage energy minimization protocol using the steepest descent method . The first minimization was performed with the coordinates of the peptide held fixed , allowing only the water and the ions to move , and the second was performed on the atoms of both the peptide and the solvent molecules . The temperature of the system was then increased from 50 K to 300 K in 500 ps of MD before the 2 . 5 production simulations were started . For all the analyses conformations saved every 10 ps were used . The trajectories were analyzed using order parameters that capture principal aspects of the folding process of the peptide . A robust parameter for identifying conformational transitions is ‘R’ [55] , calculated as follows:where R is the i inter-strand C-C distance in the native NMR structure and R is the same distance in the MD . The five inter-strand C-C pairs in the Peptide 1 hairpin are the following: N6-T10 , I5-W11 , Y4-T12 , S3-V13 and E2-T14 . A value of R5 indicates formation of the native -hairpin . Sidechain packing was quantified via the fraction of native sidechain contacts , ( a contact between two sidechains is considered to be formed when the minimum distance between the atoms belonging to the sidechains is 0 . 55 nm ) . Given a system in thermodynamic equilibrium , the change in free energy on going from a reference state , ref , of the system to a generic state , i , ( e . g . , from unfolded to folded ) at constant temperature and constant volume was evaluated as ( 1 ) where R is the ideal gas constant , T is the temperature and p and p are the probabilities of finding the system in state i and state ref , respectively . We describe the free energy surface as a function of two order parameters , namely the fraction of native contacts , , and the R parameter . A grid 40×40 was used to divide this plane in 1600 cells and for every cell the number of points counted and the relative probability calculated , allowing A to be determined . The reference state was chosen to be the grid cell with the highest probability , which corresponds to folded -hairpin structures . The statistical error in different properties evaluated from the simulations , such as the TS lifetime or the free energy values , was estimated through the standard error of their mean , , calculated over subsets of the trajectories: ( 2 ) ( 3 ) ( 4 ) where is the mean of the given parameter evaluated in the i subset , and and are the mean value over the samples and the sample standard deviation , respectively . Here we used 3 independent subsets of the trajectories ( , i . e . 3 groups each consisting of two trajectories ) , which was found to be a good compromise between the statistics within each subset and the sample size , . Assuming , as usual , a normal distribution of the mean value , , the expected value of lies with 95% confidence inside the interval . | The folding dynamics of many small protein/peptides investigated recently are in terms of simple two-state model in which only two populations exist ( folded and unfolded ) , separated by a single free energy barrier with only one kinetically important transition state ( TS ) . However , dynamical characterization of the folding TS is challenging . We have used independent unbiased atomistic molecular dynamics simulations with clear folding-unfolding transitions to characterize structural and dynamical features of transition state ensemble of Peptide 1 . A common loop-like topology is observed in all TS structures extracted from multiple simulations . The trajectories were used to examine the mechanism by which the TS is reached and subsequent events in folding pathways . The folding TS is reached and crossed in a directed stagewise process rather than through random fluctuations . Specific structures are formed before , during , and after the transition state , indicating a clear structured folding pathway . | [
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] | 2011 | Structured Pathway across the Transition State for Peptide Folding Revealed by Molecular Dynamics Simulations |
Regulation of gene expression via specific cis-regulatory promoter elements has evolved in cellular organisms as a major adaptive mechanism to respond to environmental change . Assuming a simple model of transcriptional regulation , genes that are differentially expressed in response to a large number of different external stimuli should harbor more distinct regulatory elements in their upstream regions than do genes that only respond to few environmental challenges . We tested this hypothesis in Arabidopsis thaliana using the compendium of gene expression profiling data available in AtGenExpress and known cis-element motifs mapped to upstream gene promoter regions and studied the relation of the observed breadth of differential gene expression response with several fundamental genome architectural properties . We observed highly significant positive correlations between the density of cis-elements in upstream regions and the number of conditions in which a gene was differentially regulated . The correlation was most pronounced in regions immediately upstream of the transcription start sites . Multistimuli response genes were observed to be associated with significantly longer upstream intergenic regions , retain more paralogs in the Arabidopsis genome , are shorter , have fewer introns , and are more likely to contain TATA-box motifs in their promoters . In abiotic stress time series data , multistimuli response genes were found to be overrepresented among early-responding genes . Genes involved in the regulation of transcription , stress response , and signaling processes were observed to possess the greatest regulatory capacity . Our results suggest that greater gene expression regulatory complexity appears to be encoded by an increased density of cis-regulatory elements and provide further evidence for an evolutionary adaptation of the regulatory code at the genomic layout level . Larger intergenic spaces preceding multistimuli response genes may have evolved to allow greater regulatory gene expression potential .
The regulation of gene expression has evolved in cellular organisms as a major adaptive mechanism to respond to environmental changes [1–5] . How the apparent diversity of responses is encoded in an organism's genome is a central question in understanding transcriptional regulation induced by different environmental and extracellular conditions [6–10] . The induction or repression of particular genes in response to specific environmental challenges is primarily controlled by the recognition and binding of transcriptional regulator proteins ( transcription factors ) to cis-regulatory elements constituted by short DNA sequence motif sites located in the upstream regions of genes [11–13] . Under the simplest scenario of transcriptional regulation , distinct external challenges are matched by specific cognate regulatory sites in upstream regulatory regions of genes that have evolved to respond to the particular perturbation . Genes that are differentially expressed in response to a large number of different external stimuli ( multistimuli response genes ) are therefore expected to contain more distinct cis-regulatory elements in their upstream regions than are genes that respond to only few environmental cues . There are two plausible strategies of how evolution may have shaped the noncoding , regulatory segments of genomes to encode a greater capacity of downstream genes to respond to a wider range of different stimuli by differential gene expression . A broader response spectrum may have evolved via an increased density of regulatory motifs or via an enlarged size of regulatory intergenic regions to accommodate more elements ( or both ) . In analyzing expression patterns of Caenorhabibditis elegans and Drosophila melanogaster genes in different developmental and tissue differentiation stages , Nelson and coworkers [10] observed that indeed there exists a significant positive correlation between the complexity of a gene's expression , that is , to be expressed in a number of different tissues and developmental stages , and the size of its flanking noncoding , intergenic sequence , suggesting that regulatory requirements may have played a significant role in shaping the architecture of genomes . The association of promoters harboring multiple different regulatory sites with differential responses of their downstream genes to varied growth conditions has also been conceptualized by Harbison and coworkers using ChIP-chip yeast data [8] . They distinguished four types of motif arrangements in promoters: single regulators—associated with genes of common functions , repetitive motifs—allowing graded transcriptional responses , multiple regulators—allowing responses in diverse conditions , and co-occurring regulators—for physically interacting regulators . In this study , we test and quantify the strength of the association of the presence of multiple different regulatory motifs in promoters with the breadth of differential gene expression response to external stimuli in Arabidopsis thaliana . For this purpose we use the available compendium of gene expression profiling data in the public AtGenExpress Arabidopsis thaliana gene expression repository ( http://www . arabidopsis . org/info/expression/ATGenExpress . jsp ) alongside previously characterized cis-element motifs mapped to upstream gene regions . Arabidopsis is an ideal model system for the investigation of the regulatory code in higher eukaryotic organisms due to its complete genome sequence [14] , the availability of public domain resources of known cis-elements in upstream gene regions such as Athena ( http://www . bioinformatics2 . wsu . edu/cgi-bin/Athena/cgi/home . pl ) [15] , and genome-wide expression profiling data for a large and diverse collection of treatment-control experiments . These experiments encompass a wide range of abiotic and biotic treatments , the application of plant hormones and other chemical treatments performed , and were designed to enable comparative studies by using the same technology platform and reference conditions . We define the property of genes to be differentially regulated in response to many or few conditions as their “breadth of response . ” It is this quantity and its relation to gene regulatory motifs and other genome structural properties that form the main focus of this study . Rather than internal gene expression regulation during tissue differentiation and organism development for sets of genes with available profiling data in developmental expression series and literature information as used by [10] , the available Arabidopsis data allow us to assess systematically the differential gene expression response breadth and for nearly all genes more directly by measuring gene expression in response to external stimuli . Furthermore , with the mapping information of previously characterized regulatory motifs , albeit the set may likely not be considered complete , to the Arabidopsis genome at hand , we are able to associate gene expression complexity directly to cis-elements , their identity , frequency , and spatial distribution and in conjunction with genomic layout properties , such as distances between neighboring genes and gene size . Transcriptional response programs to external stress have been studied in microorganisms , yeast in particular [3] . Studying the association of regulatory capacity and the breadth of transcriptional response in a higher , multicellular and multitissue organism such as Arabidopsis will allow comparison and an assessment of the generality of the observed mechanisms . Our results obtained in Arabidopsis lend further support to the notion that larger intergenic regions may have evolved to allow broader differential gene expression capacity [10] . Arabidopsis genes showing differential gene expression in response to a greater range of external stimuli are flanked by larger intergenic regions . In addition , increased breadth of gene expression response was observed to correlate with an increased density of motifs in upstream promoter regions , most pronounced in segments immediately upstream of the transcription start site . Among the various cis-elements analyzed , the TATA-box motif appears to play a unique role . We observed TATA-box–containing genes to possess a significantly increased breadth of response and to be associated with significantly longer upstream intergenic regions compared to TATA-less genes , thereby possibly allowing for greater regulatory capacity . Identified correlations of several fundamental genome architectural properties with the observed breadth of differential gene expression response are discussed in the context of evolutionary forces shaping the structure of eukaryotic genomes .
Applying a noise-filtered threshold of 2-fold up-regulation or down-regulation in 43 treatment-control experiments , we observed a nearly exponential decrease in the number of genes with increasing cumulative numbers of experiments with differential expression ( Figure 1 ) . Most genes were found to be differentially expressed in only few experiments , whereas only a small number of genes were observed to respond to many different external stimuli . Genes involved in stress response , cell growth , and lipid transport are particularly overrepresented in the set of multistimuli-sensitive genes ( Table 1 ) , whereas the housekeeping functions , protein catabolism and synthesis , RNA processing , and DNA repair , as well as uncharacterized genes with as-of-yet-unknown function , are more associated with the group of genes with narrow breadth of differential gene expression response , indicating that they are constitutively expressed . Genes involved in embryonic development were also grouped with narrow response genes . However , this may primarily be explained by the absence of embryonic development samples in the analysis . The gene AtEXPA8 ( At2g40610 ) , a member of the α-expansin gene family and involved in cell wall modification and loosening , was observed to show the greatest response breadth and was differentially expressed in 22 different experiments . With the mapping information for 93 previously characterized cis-element motifs to all Arabidopsis upstream gene regions up to a length of 3 , 000 nucleotides available from the Athena database [15] ( see Methods ) , it is now possible to correlate the breadth of differential gene expression response to the number of cis-regulatory elements for all genes . If a broader differential gene expression response is reflected and even encoded by an increased number of cis-elements , we expect a positive correlation . Indeed , very significant positive correlations can be observed for both the number of total and unique elements in promoters of defined lengths ( Figure 2 ) , i . e . , multistimuli response genes harbor more cis-elements in their upstream regions ( higher density of elements ) than do genes with a narrower scope of responses . This positive correlation was particularly significant and pronounced for an assumed promoter length of 500 nucleotides immediately upstream of the presumed site of transcription initiation . While the significance of the observed correlations was high , absolute motif counts increased only moderately corresponding to an increase of approximately 13% in the 500 nucleotides immediately upstream over the observed range of response breadth . For promoter segments farther upstream , the significance of the observed correlations was weaker , in part explained by fewer observations , albeit detectable , and the relative increase in motif counts smaller with increasing breadth of response . The number of experiments with differential expression , the breadth of response , includes counts for both up-regulation and down-regulation in treatment-control samples . When only up-regulation or only down-regulation responses were considered differential gene expression events , we observed that the obtained positive correlations of motif counts with breadth of response was primarily caused by up-regulation rather than by down-regulation events for which no significant correlation were obtained to the number of upstream motifs in promoter segments of defined length ( Figure S2 ) . Determining the correct length of gene promoters , i . e . , to assess whether a putative cis-acting regulatory site will exert an influence on its downstream gene depending upon its distance from the site of transcription initiation , is difficult experimentally , even more so if only in silico mapping information is available . Allowing variable promoter segment lengths of up to 3 , 000 nucleotides , but excluding cis-elements that overlap with neighboring upstream genes and only considering cis-elements that map to intergenic upstream regions , the magnitude of the correlation of motif counts to breadth of response increased significantly ( r ≈ 0 . 2 obtained for motif counts in variable-length promoter segments [Figure 3] compared to r ≈ 0 . 1 for fixed promoter lengths [Figure 2] ) . Genes that are differentially regulated in nine different experiments ( the greatest observed breadth of response value in the set of experiments analyzed here with more than associated 100 genes ) have 50% more cis-elements for both overall cis-element counts and unique counts versus genes with no detectable differential response in the experiments included in this study . This result suggests that the length of the upstream intergenic region , too , is positively correlated with the breadth of differential gene expression response . Such a positive correlation has already been reported in analyses of gene expression profiles in different developmental and cell differentiation stages in C . elegans and D . melanogaster [10] . Indeed , we found a very significant positive correlation of intergenic upstream sequence length and the breadth of response ( r = 0 . 19 , p = 1 . 2E−89; Figure 4A ) also in response to external stimuli . On average , highly multistimuli response genes have approximately double the intergenic upstream space compared to genes with no differential response . This trend was observed equally strong for both differential up-regulation or down-regulation events ( Figure S3 ) . This finding prompted us to relate other gene properties to the breadth of response . The length of downstream intergenic segments was also found positively correlated with breadth of response , albeit at a less significant level and smaller magnitude ( r = 0 . 08 , p = 4 . 6E−18; Figure 4A ) than for the corresponding upstream segment lengths . Downstream intergenic segments are also significantly shorter than upstream segments . We observed that multistimuli response genes are , on average , significantly shorter , with shorter gene length ( r = −0 . 19 , p = 1 . 2E−95 [Figure 4B] ) as well as shorter cDNA length ( r = −0 . 12 , p = 2 . 2E−42 [Figure 4E] ) and have shorter 5′ ( r = −0 . 08 , p = 5 . 8E−16 ) and , to a lesser degree , 3′ untranslated regions ( r = −0 . 05 , p = 5 . 1E−9 [Figure 4C] ) . Commensurate with shorter gene length , the number of introns was also observed to be negatively correlated with breadth of differential expression ( r = −0 . 2 , p = 2 . 3E−110 [Figure 4D] ) , as was the mean length of intronic segments ( r = −0 . 2 , p = 6E−114 ) . In addition to the analyzed gene size–related parameters , multistimuli response genes are also more likely to contain additional paralogs in the Arabidopsis genome than are narrow-response genes ( r = 0 . 17 , p = 9 . 4E−77 [Figure 4F] [30% amino acid sequence identity] , and r = 0 . 09 , p = 3 . 1E−24 for the more stringent paralog settings of requiring 70% amino acid sequence identity [see Methods] ) . Among the various properties analyzed , the length of the upstream region and gene length and the associated number of introns were found most strongly correlated with breadth of differential gene expression response . The sum of the intergenic upstream and downstream distances measuring the size of a gene's intergenic flanking region was not observed to be negatively correlated with the size of the flanked gene ( r = 0 . 014 , p = 0 . 14 ) ; i . e . , genes are not distributed evenly such that longer intergenic regions follow from shorter genes . Applying a multiple linear regression approach , we analyzed what level of correlation with breadth of response can be achieved by combining the various and largely independent properties depicted in Figure 4 . Performing a stepwise-forward multiple linear regression and using nonunique motif counts in the 500-nucleotide upstream regions ( motif density ) , the three most significant regressors were ( 1 ) number of introns ( partial correlation coefficient , β = −0 . 23 ) , ( 2 ) distance to next upstream gene ( β = 0 . 13 ) , and ( 3 ) motif density ( β = 0 . 09 ) , yielding a combined level of correlation of r = 0 . 28 , r2 = 0 . 08 ( p ≪ 0 . 01 , n = 6 , 976 genes with complete information and upstream intergenic region longer than 500 nucleotides ) . Adding more properties ( downstream region length , UTR lengths , etc . ) did not result in significantly increased correlation levels . We investigated what gene families and functions are associated with long upstream intergenic segments . By sorting all Arabidopsis genes according to their upstream intergenic length and comparing the Gene Ontology ( GO ) annotations of a subset of genes with longest upstream segments to a set of genes with shortest upstream segment lengths using Fisher exact statistical tests , we found that transcription factors , transcriptional regulatory functions , and genes involved in signaling processes are particularly overrepresented among genes with long intergenic upstream segments , while ribosomal genes involved in protein biosynthesis and genes involved in other housekeeping functions such as glycolysis are relatively overrepresented among genes with short upstream segment lengths , as are genes with currently unidentified function ( Table 2 ) . The found association of functional categories with intergenic upstream distances in A . thaliana agrees well with very similar observations reported for C . elegans and D . melanogaster [10] . We analyzed which gene families and biological processes are associated with genes with the greater number of cis-elements in their upstream promoter region . When counting regulatory elements in upstream segments of up to 3 , 000 nucleotides and truncating them at neighboring upstream genes , i . e . , variable promoter length , the GO processes and gene families characteristic for genes with many motifs largely coincided with the profile obtained for genes with long upstream segments ( Table 2 ) . Likewise , profiles for genes with few motifs matched profiles obtained for genes with short upstream regions . As the motif density was observed to be relatively constant across intergenic regions ( Figure S4 ) , this agreement is not surprising because motif counts are positively correlated with intergenic distances . When we confined the promoter count to upstream regions of 500 nucleotides , i . e . , analyzing the density of motifs in 500 nucleotides , only one GO process ( response to wounding ) was borderline significant ( false discovery rate [FDR] p-value = 0 . 09 ) . No other GO process category or gene family association was found to be significantly correlated with either high or low motif density ( FDR p-value > 0 . 1 for all categories and gene families ) . However , “response to wounding” and “response to cold” were ranked highest among GO processes associated with high motif density . Several other response-related categories were also contained among the top-ranked processes , such as “response to oxidative stress” ( rank 6 ) and “response to gibberellic acid stimulus” ( rank 16 ) and , as a single-word category , “response” was very significantly associated with high motif density ( FDR p-value = 5 . 1E−7 ) , suggesting that higher motif densities are indeed associated with genes that are involved in environmental response pathways . Well-known stress response elements in plants such as the ABRE-like , ABF , and DRE binding site motifs [16] are among the motifs found most frequently in genes with large breadth of response ( Table 3 ) . The occurrence of the TATA-box motif , a commonly found eukaryotic core promoter element involved in transcription initiation and usually located approximately 25 to 30 nucleotides upstream of the transcription start site [17] , also appears to be preferentially associated with multistimuli response genes . Genes with a putative TATA-box motif present in the 60 nucleotides upstream of the transcription start site had a significantly greater breadth of response ( mean number of experiments with differential expression = 3 . 4 , n = 2 , 002 ) than did genes lacking a TATA-box motif in their 60-nucleotide upstream region ( mean number of experiments with differential expression = 1 . 9 , n = 9 , 795 , p = 5 . 0E−113 ) . Consistent with increased intergenic upstream distances for multistimuli response genes , TATA box–containing genes were also observed to have longer upstream regions ( 2 , 370 nucleotides versus 1 , 737 nucleotides for TATA-less genes , p = 3 . 4E−34 ) . Motifs reported to control housekeeping genes ( TELO-box promoter motif [18]; hexamer promoter motif [19] ) or implicated to confer tissue-specific expression ( LEAFYATAG [20] ) were found to be more associated with genes with narrow range of differential expression response , i . e . , their expression is constitutive or their differential expression response is very specific . It is conceivable that in transcriptional regulatory signaling cascades , early responders evolved a greater sensory capacity ( i . e . , respond to many different transcription factors ) and then channel the response to common effector genes . In our framework , equating sensory potential to the number of cis-elements , we then expect early responders to be associated with a greater number of regulatory elements . The 18 AtGenExpress abiotic stress time series datasets ( nine for root and shoot tissue , respectively ) allowed testing of this hypothesis . Comparing genes that are differentially expressed during the first 3 h after stimulus to genes that respond at later time points and assuming a fixed promoter length of 500 nucleotides , early-response genes were associated with only marginally or no increased motif count densities with 3% ( 2% ) more ( nonredundant [nr]-unique ) motifs in 500 nucleotide–upstream regions compared to late genes ( 7 . 5 versus 7 . 3 , p = 5 . 9E−12; 5 . 8 versus 5 . 7 , p = 3 . 4E−6 for nr-unique motifs , respectively ) . However , in allowing promoter lengths of up to 3 , 000 nucleotides and truncating them at neighboring upstream gene sites ( variable maximal promoter lengths ) , early genes were observed to harbor 20% ( 16% ) more ( nr-unique ) motifs than late genes ( 34 . 4 versus 28 . 5 , p = 2 . 1E−170; and 13 . 2 versus 11 . 4 , p = 1 . 2E−179 for nr-unique motifs , respectively ) . As was observed before , an increased number of cis-elements for a specific group of genes may originate from two sources: higher motif density and more different motifs and , apparently more significantly , longer upstream regions providing more space for potential regulatory sites . Early-response genes have , on average , 30% longer intergenic upstream segments than do late-response genes ( 2 , 668 nucleotides versus 2 , 064 nucleotides , p = 2 . 9E−115 ) and are more likely to contain TATA-boxes ( 25% versus 19% , Fisher exact p-value = 1 . 4E−12 ) . Consistent with a transcriptional regulatory signaling cascade , genes involved in the regulation of transcription , signaling , as well as stress response genes are overrepresented among early-responder genes , whereas ribosomal genes and genes generally involved in protein biosynthesis , metabolism , and other nonsignaling processes are more characteristic of late-response genes ( Table 4 ) .
In this study , we followed the notion that regulatory capacity is associated with information-bearing properties of the intergenic region upstream of the transcription start site of the regulated genes . The simplest and most accessible measure of information content was to correlate cis-regulatory motif counts to the breadth of differential gene expression response . We found that increased breadth of response is indeed positively correlated with greater motif density ( Figure 2 ) . While the observed correlations were highly significant , their magnitude was generally low . The positive correlation between breadth of response and motif count was relatively strongest for the first 500 nucleotides upstream compared to the other investigated regions , suggesting that this interval may generally be the most relevant promoter segment to control gene expression . Apart from greater density , more motifs and generally greater information content can also arise from more available intergenic space . Analyzing gene expression profiles in different developmental and cell differentiation stages in C . elegans and D . melanogaster , Nelson and coworkers [10] observed that genes with more complex expression profiles were indeed associated with larger flanking intergenic intervals . Genes with regulatory functions were shown to generally have longer upstream regions , whereas genes involved in housekeeping functions have shorter upstream segments . The results reported here for Arabidopsis and analyzing differential expression data for transcriptional response to external stimuli confirm these findings . The requirements for encoding regulatory response complexity to external environmental challenges also appear to have played a role in shaping the layout of the Arabidopsis genome . Not conflicting with this result , we saw that motif density also is varied in correlation with different complexity of transcriptional response ( Figure 2 ) , suggesting that both principal mechanisms ( greater density and/or more space ) were employed by evolution to encode complex transcriptional response patterns . While both flanking sequences ( upstream and downstream ) showed positive correlation of their length to the breadth of response , we found that in Arabidopsis , upstream distances were more strongly correlated with response diversity and were generally longer ( Figure 4A ) than downstream intervals , suggesting that the information content upstream of a gene may be more relevant to the encoding of regulatory properties than downstream segments . Apart from transcription factor binding sites , other mechanisms and motifs , such as histone binding sites , chromatin structural changes , enhancers , silencers , and insulators , as well as sites of epigenetic regulation , may also constitute regulatory elements contained in intergenic segments that can contribute to more complex regulatory properties encoded in longer upstream intervals . Alternative to our assumption that increased breadth of response correlates with increased regulatory capacity , it might be speculated that because genes that respond to many different stimuli in a similar manner , they are regulated by a common regulatory mechanism and therefore should be expected to be regulated by fewer rather than more cis-elements . However , data presented by Gasch et al . [3] on the gene expression response to environmental stress in yeast and the results presented here indicate that multistress response genes appear to be controlled by different and condition-specific regulatory mechanisms . It has been recognized that often multiple factors are involved in the initiation of transcription , suggesting a combinatorial control of transcription initiation [26 , 27] . A combinatorial use of the repertoire of cis-regulatory elements enlarges the regulatory coding capacity tremendously . Conceptually , the basic premise of this investigation—a positive correlation between encoded and observed differential gene response—also applies in the case of combinatorial control . The correlation between expression level and gene size and associated properties such as number and length of introns has been investigated in several studies [28–34] . Surprisingly , while highly expressed genes were found to be shorter in animals and to harbor fewer and shorter introns [30] , they appear least compact and relatively largest in plants [33] and C . elegans [29] . Several evolutionary scenarios such as selection for economy and genomic design have been discussed to explain the observed trends [31 , 34] . Using the AtGenExpress dataset analyzed in this study , we also observed a general increase in gene size , intron length , and intron number with expression level in Arabidopsis , albeit for very highly expressed genes , the trend was reversed ( Figure S7 ) . The focus of this study has been on the correlation between breadth of differential genes expression response , not its absolute level , and genome architectural properties . It can be assumed that differential gene expression is largely independent of expression level with the obvious boundary effects that lowly expressed genes are more likely to be up-regulated and highly expressed genes have a greater chance of being down-regulated as certain expression levels cannot be exceeded . For medium expression levels , indeed no such correlation was found in our dataset and deviations at low levels likely caused by noise ( Figure S1 ) . Interestingly , we observed that properties related to gene size ( gene length , cDNA length , number of introns ) are strongly and negatively correlated with breadth of differential gene expression response ( Figure 4 ) . While this may reflect a coincidence that informative ( involved in regulation or signaling ) gene families are generally smaller , the intriguing question arises of whether transcriptional efficiency may have played a role during evolution . Shorter genes with fewer introns may be produced more economically and thereby quicker in response to sudden external stimuli . In fact , the number of introns was the strongest , albeit marginally , of all properties analyzed to correlate with breadth of response ( Figure 4 and multiple linear regression results ) . Informative molecules may also be smaller as they need to diffuse within the cell or tissues to carry their information to the place of perception rather than being constituents of larger and more static cellular machineries . Clearly , comparing our findings in Arabidopsis with those in warm-blooded animals will shed further light on the generality of our observations . Among the cis-regulatory motifs considered in the analysis , the preferential association of the TATA-box motif—a commonly found eukaryotic core promoter element involved in transcription initiation and usually located approximately 25 to 30 nucleotides upstream of the transcription start site [17]—with multistimuli response genes is of particular interest . In the gene set studied here , 17% of all genes contained TATA-box motifs within the first 60 nucleotides upstream of the transcription start . TATA-box genes were found associated with stress response and were shown to be subject to chromatin remodeling factors consistent with their regulation by nucleosomal mechanisms [35] . The TATA-box motif was also described recently to confer increased interspecies gene expression variation of the corresponding genes [36] . Our observation that TATA-box–containing genes have longer intergenic upstream regions is consistent with their chromatin and nucleosomal regulation and also suggests that the expression of TATA-box genes may evolve at higher rates , causing increased variation across species because their upstream regulatory potential is greater and , therefore , more amenable to change and modulation . Interestingly , presence of the TATA-box also strongly correlates with a decreased number of introns in the downstream gene ( number of introns for genes with TATA: 3 . 7 , without: 5 . 9; p = 1 . 9E−93 ) . It is presently not clear whether this correlation indicates a direct coupling between transcription and splicing [37] or whether it originates from an indirect correlation via a common principle shaping the genome , such as possibly the breadth of response and the role of both properties in defining it as reported here . Assuming that multistimuli response or “informational hub” genes play more critical roles than genes that have a narrower response scope , it can be speculated that failures of such central genes—by spontaneous disruptive mutations , for example—may be more detrimental to the organism . Therefore , functional backup genes may have evolved to safeguard against such failures . A plausible evolutionary solution would be to retain copies or paralogs of hub gene in the genome from gene or segmental duplication events , i . e . , genes with identical or similar function [38 , 39] . The observed increase of the number of paralogs with increasing breadth of response is consistent with the concept of selection for functional backup ( Figure 4F ) . However , results reported in yeast suggest that rather than redundancy provided by duplicated genes , interactions between unrelated genes appear to be responsible for robustness against mutations [40] . Furthermore , rather than a static robustness provided by “replacement parts , ” a dynamic reprogramming of the transcriptional regulatory network may be employed during “fail-safe” scenarios [41] . We saw that multistimuli response genes were generally associated with environmental response processes ( Table 1 ) . As the number of different external stimuli is large and fine-tuning the perception as well as signal transduction depending upon the stimulus will likely be beneficial , it may have been evolutionarily advantageous to use duplication events to evolve genes ( paralogs ) with shifted and novel response scope , also explaining a greater number of paralogs for multistimuli response genes [42] . Alternatively , paralogs may allow a greater dynamic range of the response to external stimuli . Corresponding dosage effects and their role in the retention of paralogs have recently been discussed in the literature [43] . We observed that genes implicated in the response to specific stresses ( e . g . , cold ) are also among the genes with a very broad range of differential gene expression in response to various environmental changes ( Table 1 ) . However , some of the applied external changes correspond to very similar environmental cues ( salt and osmotic stress , for example ) and common transcriptional response programs will likely be triggered . Ideally , only very different and unrelated external stimuli would be used in our analyses , but imposing such requirements would reduce the number of experiments to very low numbers . Therefore , it needs to be borne in mind that the similarity between different external conditions and the resulting relative overrepresentation of particular types of external stimuli may cause a bias toward certain gene functional categories ( salt stress response , for example ) . Interestingly , when the abiotic stress series was excluded from the list of experiments ( about half of all experiments , Table S1 ) , general stress response GO categories , including abiotic stress response , were still overrepresented among the genes with high breadth of response ( “response to wounding , ” FDR p-value = 3 . 7E−7; “response to oxidative stress , ” FDR p-value = 5 . 5E−4; “response to heat , ” FDR p-value = 0 . 04 ) , suggesting that some stress response genes may truly display a large and diverse range of differential response . If , as observed , longer upstream regions are associated with greater gene expression complexity of downstream genes , the question emerges of whether there are characteristic sequence motifs in longer regions that are not found in short intergenic upstream segments . Such motifs may help identify and elucidate new regulatory elements and even mechanisms , DNA structural properties , and their influence on gene regulation , for example . Recent reports that large fractions of the nontranslated segments of genomes are functionally important based on analyses of mutation rates [44] and the greater-than-expected occurrences of specific sequence patterns in noncoding DNA segments associated with genes involved in signaling and transcription regulation processes [45] strongly encourage the pursuit of further research in this direction . As sessile plants cannot evade changing and adverse environments , they may rely more strongly on elaborate transcriptional response programs and may thus serve as ideal model systems for the study of the regulatory code in the genomes of higher organisms .
Gene expression information was obtained from AtGenExpress . Profiling data based on the ATH1 Affymetrix GeneChip microarray platform [46] from the abiotic stress , biotic , nutrient , hormone , and chemical treatment-control series have been used . A detailed list of the 43 experiments , included samples , and a brief description of the various conditions , is available in Table S1 . Raw expression data files were obtained for treatment and associated control samples . All samples associated with a particular treatment-control experiment were preprocessed and normalized together using the Affy-package [47] available in the Bioconductor software suite [48] . Raw expression level data were normalized applying three different methods: RMA [24] , GCRMA [23] , and MAS 5 . 0 ( Affymetrix ) . Unless noted otherwise , results are based on RMA normalization as the default normalization method . Genes were considered differentially expressed if |mT − mC| > 1 and s > 0 . 5 where s = |mT − mC|/ ( σT + σC ) and mT , mC denote the mean logarithm-base-2 transformed expression levels for associated gene probes across the available treatment ( t ) and control ( c ) replicate samples , and σT , σC are the associated standard deviations of the log-2 expression levels . The first condition corresponds to a threshold of minimally 2-fold up-regulation/down-regulation of treatment expression levels relative to the levels in the associated control samples . The second criterion was introduced as a simple statistical significance measure similar to a simplified t-test . By normalizing to the standard deviation , rather than the standard error as done in the t-test , the risk of biasing the results to experiments with more repeats was reduced . The test metric s was introduced by Golub et al . [49] in their seminal study on cancer classification based on gene expression . To test whether the results are robust with regard to the chosen threshold values , other , more stringent , parameter values were applied . Qualitatively similar results were obtained ( Figure S6 ) . All mitochondrial and chloroplastidial gene probes were discarded . Only probes that map uniquely to annotated genes and with available cis-regulatory motif information have been considered . To ensure independence of the frequency of detected differential expression events on the absolute expression level and to avoid possible normalization artefacts , we examined for all gene probes the number of experiments in which a gene probe was observed to be differentially expressed as a function of the median rank of this probe across all control samples in the dataset and compared the results for all three normalization methods ( Figure S1 ) . For probe ranks greater than 10 , 000 , no significant influence of the absolute expression level on the frequency of differential expression was observed . Therefore , we discarded the 10 , 000 probes with lowest median rank across all control samples from the analysis . Analyses using all probes have also been conducted and confirm the presented results . Applying the above criteria , 11 , 797 Arabidopsis genes were used in the expression data analysis using their unambiguously mapping array probes , i . e . , every probe mapped to only one gene and every gene mapped to only one probe . Mapping information was obtained from The Arabidopsis Information Resource ( TAIR ) [50] and the ATH1-chip annotation file provided by Affymetrix . We define the term “breadth of response” for every gene as the cumulative number of treatment-control experiments in which the gene was found to be differentially expressed . Transcription factor binding site location information was obtained from the Athena promoter annotation resource [15] . In Athena , transcription factor site coordinates are obtained by sequence mapping of consensus motif sequences imported from the Plant cis-acting regulatory DNA elements ( PLACE ) [51] and Arabidopsis Gene Regulatory Information Server ( AGRIS ) [52] . The dataset contained mapping information for 93 different and previously characterized binding site motifs in the 5′ upstream gene segments of up to 3 , 000 nucleotides in length associated with genes and corresponding probes present on the ATH1 microarray platform . Mapping the 93 Athena motifs to gene promoter regions results in several redundancies and motif overlaps that were eliminated in order to construct truly unique sets of motifs and corresponding motif counts associated with every gene . Motifs were sorted alphabetically for every gene and assessed for uniqueness in consecutive order . Motifs whose mapping location either fully or only partially overlaps with already accepted motifs were not included in the unique set of motifs . Thus , redundancy was eliminated based on motif sequence information . The obtained set of unique cis-elements comprised a total of 76 different motifs , i . e . , 17 motifs were excluded as they always overlapped with other motifs . For example , the motif DREB1A/CBF3 ( consensus sequence “RCCGACNT” ) contains the DRE core motif ( consensus sequence “RCCGAC” ) . Thus , the DREB1A/CBF3 element was never included as a separate motif in the unique set as it always contains the DRE core motif which is sorted alphabetically before it . Multiple occurrences of the same motif at different locations in gene promoters were collapsed to only single counts in the nr-unique cis-element set . The strength and magnitude of the association of the various investigated gene properties to the number of experiments in which gene probes were observed differentially regulated ( breadth of response ) was quantified using linear regression and the linear Pearson correlation coefficient , r . Two types of correlation statistics have generally been computed: ( 1 ) the correlation of all value pairs for all probes and ( 2 ) for mean values only , for example , the mean number of cis-regulatory elements for genes differentially expressed in n different experiments . A minimum of at least 100 probes was required for mean values to be included in the latter , thus excluding mean values of lesser confidence . The significance of the correlation was assessed using standard p-value calculation for correlation coefficients based on the t-statistic with t = r × sqrt[ ( N − 2 ) / ( 1 − r2 ) ] , and corresponding two-tailed p-values were computed from the t-distribution where N is the number of value pairs to be correlated . In addition to this parametric significance testing , a nonparametric method based on data shuffling was implemented . The pairing of data from two data vectors that were to be tested for correlation was randomly shuffled 10 , 000 times; i . e . , one vector was repeatedly randomly shuffled . The count , CS , how often the magnitude of the correlation coefficient exceeded the actually observed coefficient for the unshuffled data , divided by the total number of shufflings , NS , served as a nonparametric p-value estimate , pS , for the correlation coefficients such that pS = CS/NS . In almost all cases , the obtained pS-value was zero , i . e . , no shuffled correlation coefficient was obtained with greater correlation than the actually observed one . We therefore only list pS-values in the few cases with nonzero values ( Figure 2 ) . All p-values reported for t-test comparisons of mean values correspond to the two-tailed value . GO information was obtained from TAIR [50] . Association tests of categorical gene classification data ( GO annotations or cis-regulatory elements ) with two gene sets were performed using the Fisher exact test . The two one-tailed Fisher exact p-values corresponding to overrepresentation or underrepresentation of categories in the two sets relative to one another have been calculated based on counts in 2 × 2 contingency tables . Counts n11 , n12 , n21 , and n22 in the contingency table refer to n11 , number of observations of a particular category in the first gene set; n12 , number of other categories in the first gene set; n21 , number of observations of category in second gene set; and n22 , number of observations of other categories in the second gene set . Listed p-values correspond to multiple testing-corrected Fisher exact p-values using the FDR method ( [53] ) . Gene and genomic sequence information and gene mapping information to the Arabidopsis genome sequence , including intergenic distances , number of introns , gene , cDNA , and 5′/3′ UTR length information , and all sequence information , were obtained from TAIR database release 6 [50] . For 2 , 238 of the 11 , 797 genes , more than one transcript sequence was contained in the TAIR dataset ( annotated splice variants ) . For those genes , always the first instance was taken ( “ . 1”-transcript ) for the study of transcript-related properties such as UTR length or cDNA length . We verified that similar results were obtained when taking different instances and that the number of splice variants per gene was not correlated with the breadth of response . It should be borne in mind that the ATH1 probes were designed to target the 3′ end of gene transcripts [46] and , therefore , identification of various splice forms of genes is difficult or impossible . Duplicated genes within the Arabidopsis genome were identified following a method introduced by Gu and coworkers [54 , 55] . This method is based on an all-against-all sequence comparison of protein sequences that are not splice variants of the same gene and require at least 30% amino acid sequence identity with adjusted higher thresholds for short sequences . A second grouping of genes into paralogous gene families was generated applying a more stringent threshold of 70% amino acid sequence identity . Multiple linear regression was performed using Statistica 7 . 1 ( StatSoft , http://www . statsoft . com ) . Forward stepwise regression was applied with F to enter set to 1 and casewise missing value deletion . | The induction or repression of specific genes has evolved in living organisms as a mechanism to respond to environmental changes . At the molecular level , this process is mediated via molecular switches , so-called regulatory elements , generally located in the genomic region adjacent to the gene they control , the gene promoter . Upon environmental change , specific proteins bind to such regulatory elements , thereby turning on or off the associated genes . As this molecular response is often specific to the external signal , genes that respond to a large number of different external stimuli should harbor more distinct regulatory elements in their promoter regions than should genes responding only to few environmental challenges . In analyzing data for the plant Arabidopsis thaliana , we observed that indeed an increased number of regulatory elements is associated with a broader range of responses . Several other genome structural properties , such as gene size , the occurrence of similar genes in the Arabidopsis genome , and the distance between genes , were also observed to be correlated with a broader breadth of response . The results suggest that greater regulatory complexity appears encoded by an increased density of regulatory elements and provide further evidence for an evolutionary adaptation of the regulatory code at the genomic architectural level . | [
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Neurodegenerative lysosomal storage disorders ( LSDs ) are severe and untreatable , and mechanisms underlying cellular dysfunction are poorly understood . We found that toxic lipids relevant to three different LSDs disrupt multiple lysosomal and other cellular functions . Unbiased drug discovery revealed several structurally distinct protective compounds , approved for other uses , that prevent lysosomal and cellular toxicities of these lipids . Toxic lipids and protective agents show unexpected convergence on control of lysosomal pH and re-acidification as a critical component of toxicity and protection . In twitcher mice ( a model of Krabbe disease [KD] ) , a central nervous system ( CNS ) -penetrant protective agent rescued myelin and oligodendrocyte ( OL ) progenitors , improved motor behavior , and extended lifespan . Our studies reveal shared principles relevant to several LSDs , in which diverse cellular and biochemical disruptions appear to be secondary to disruption of lysosomal pH regulation by specific lipids . These studies also provide novel protective strategies that confer therapeutic benefits in a mouse model of a severe LSD .
Lysosomal storage disorders ( LSDs ) represent some of the most difficult of medical challenges , with poorly understood pathologies and only rare treatment options . Despite having the common property of being caused by mutations in lysosomal enzymes , leading to accumulation of substances that would normally be degraded and to more generally compromised lysosomal function , the more than 40 different LSDs differ greatly in their primary tissue pathology , their severity , and in the specific substances that accumulate within compromised cells . The individuality of these diseases is mirrored by the dominant therapeutic strategies , which are generally focused on replacement of missing enzyme activity ( by protein administration or gene expression ) or on substrate reduction therapies that have the goal of decreasing availability of a precursor for the substance whose degradation is compromised by enzyme mutation [1–39] . Such therapies have proven useful in rare cases [40–43] , but progress on therapeutic advances is infrequent and essentially nonexistent for LSDs exhibiting damage to the central nervous system ( CNS ) [44–46] . In addition , progress has tended to be disease specific rather than providing principles that may apply more broadly . Despite extensive study of LSDs , many critical questions remain unanswered about these diseases . For example , little is known about the biochemical linkage between any particular mutation and lysosomal dysfunction , or even whether there is a direct correlation between accumulation of particular substances and lysosomal dysfunction . In addition , although both lysosomal dysfunction and cellular dysfunctions occur in these diseases , it remains unclear how—or even if—these changes are functionally connected . Moreover , it is unclear whether principles that might be relevant to an individual disease are relevant to the pathology of diseases caused by different mutations . To attempt to discover principles that might be relevant to LSDs caused by different mutations , we have focused on diseases associated with accumulation of lipids that are able to cause a variety of cellular dysfunctions , up to and including cell death , when applied to cells in vitro . Such diseases include Krabbe disease ( KD ) , metachromatic leukodystrophy ( MLD ) , and Gaucher disease [22 , 31 , 47–55] . Although each of these diseases is associated with accumulation of a different lipid ( or lipids ) and with different disease pathologies , the effects of these lipids on cellular function are severe enough to suggest that such toxicities may contribute to disease pathogenesis . We now show that a structurally related subset of lipids that accumulate in KD , MLD , or Gaucher disease all induce multiple lysosomal dysfunctions ( along with other cellular dysfunctions ) , thus providing a direct link between enzymatic mutations and lysosomal abnormalities . We further show that it is possible to use drug-repurposing assays to discover single compounds that block a wide range of lipid-induced toxicities . Analysis of the properties of toxic lipids and of protective compounds reveals a previously unsuspected role of lysosomal pH and re-acidification as a potentially valuable therapeutic target . We further provide proof of principle that selecting potential therapies based on their ability to improve lysosomal function without correcting a genetic defect can reveal compounds that offer clinically relevant benefits in a mouse model of a severe LSD .
We began our studies with an analysis of psychosine ( Psy , also referred to as galactosylsphingosine ) , a lipid that is thought to be of central , and potentially causal , pathogenic importance in KD [56–60] . Psy is one of the most extensively studied of all the lipids known to accumulate in LSDs and is known to exhibit toxicity for multiple cell types in vitro [61–75] and to cause extensive damage when injected intracranially in wild-type ( WT ) mice [59] . Psy accumulates in tissues of individuals with KD due to galactocerebrosidase ( [GALC] , EC 3 . 2 . 1 . 46 ) mutations that cause abnormal processing of lipids that are important components of myelin , the insulating material that enwraps axons in the CNS and peripheral nervous systems ( PNS ) , a primary target of damage in KD . Psy also accumulates in tissues of the naturally occurring , severe murine model of KD , the twitcher mouse [76–80] , which also harbors GALC mutations and recapitulates most human pathologies . As a prelude to analyzing the ability of Psy to alter cellular function , we first determined which CNS cells were most vulnerable to this lipid and found that the most sensitive cells were primary oligodendrocyte ( OL ) /type-2 astrocyte progenitor cells ( [O-2A/OPCs] , also referred to as OL precursor cells ) . These progenitors , which give rise to the myelin-forming OLs of the CNS during development and in response to myelin damage , were killed by pathophysiologically relevant low-micromolar ( 3 μM ) concentrations of Psy [77] that had no effect on hippocampal and cortical neuron survival ( see also , e . g . , [65 , 81] ) and were as toxic to OLs ( Fig 1A ) . The vulnerability of primary O-2A/OPCs to Psy was also an order of magnitude greater than seen in immortalized CNS glial progenitor cell lines ( e . g . , [82] ) and in Schwann cells of the PNS [83] . This level of sensitivity falls well within the reported Psy concentrations in the CNS of symptomatic ( postnatal day [P]25 ) and moribund ( P40 ) twitcher mice , which are between 15 μM and 34 μM , respectively [77] . Similar concentrations in the twitcher CNS have been reported by other investigators , ranging from 0 . 7 μM ( P10 ) , 4 μM ( P16 ) , and 4 . 5 μM ( P20–P25 ) to as high as 27–50 μM ( P30 ) [78–80] . Comparable concentrations have been reported in postmortem Krabbe patient CNS tissue , ranging from 2 . 7 μM to 45 μM in cortical grey and white matter , respectively [84 , 85] . Further studies revealed that O-2A/OPCs exposed to still lower ( 1 μM ) levels of Psy exhibited multiple abnormalities of potential relevance to understanding the decreased myelination and apparent failure to repair myelin damage seen in KD . In the absence of cell death , 1 μM Psy suppressed both cell division ( Fig 1B ) and differentiation of O-2A/OPCs into OLs ( S1A Fig ) . It also disrupted cytoskeletal integrity and caused decreased cell migration ( S1B and S1C Fig ) . Such sensitivity places these cells among those most sensitive to the effects of Psy exposure . We next discovered that exposure to 1 μM Psy has the previously unrecognized ability to cause multiple alterations in lysosomal function , indicating that this lipid may provide a direct link between enzymatic mutation and lysosomal dysfunction in KD . Exposure to Psy caused abnormalities in lipid homeostasis , endolysosomal transport , and cathepsin activity . Exposure to 1μM Psy disrupted lipid homeostasis , causing the intracellular accumulation of both neutral triglycerides and phospholipids ( Fig 1C , S1D Fig ) . Endolysosomal transport was also compromised by exposure to 1 μM Psy , as shown by a decreased rate of endocytic import of fluorescently labeled polystyrene nanobeads ( time to half-maximal staining intensity: 4 . 6 ± 1 . 0 min for vehicle-treated control versus 22 . 2 ± 5 . 7 min for Psy , p < 0 . 05; Fig 1D , S1E Fig ) . Psy exposure also increased the activity of resident lysosomal proteases cathepsin D and B , which can cause cellular damage or death upon export to the cytoplasm and the activities of which are known to be elevated in a number of LSDs ( Fig 1E ) [86–88] . Psy’s ability to disrupt lysosomal function was as great as that seen with bafilomycin A ( BafA ) , which disrupts lysosomal function by antagonizing the lysosomal vacuolar-type H+-ATPase [89] . Exposure of O-2A/OPCs to 1 μM Psy significantly increased intralysosomal pH from 4 . 88 ± 0 . 04 to 5 . 62 ± 0 . 08 after 24 h , an increase maintained for at least 48 h after a single exposure ( p < 0 . 001; Fig 1F ) . This elevation in lysosomal pH was observed in both fixed ( Fig 1F ) and live ( S1F Fig ) O-2A/OPCs . Psy exposure was as potent at increasing lysosomal pH as multiple compounds well known to exert such effects , including BafA , chloroquine , or the weak base NH4Cl ( Fig 1F ) [90] . This increase was evident within minutes of exposure to Psy and was comparable to treatment with BafA ( S1G Fig , S1–S3 Movies ) , and the effects on lysosomal pH were sustained over 24–48 h after Psy exposure . To identify potential means of preventing Psy-induced toxicities that might be suitable for eventual clinical utilization , we conducted unbiased analysis of multiple concentrations of 1 , 040 mostly United States Food and Drug Administration ( FDA ) -approved small molecules [91] and a custom panel of 12 growth factors with known neuroprotective activity . We examined prevention of Psy-induced suppression of O-2A/OPC division in these analyses ( Fig 2A ) . We found 16 structurally and functionally diverse compounds ( S2A Fig , S1 and S2 Tables ) , in addition to 4 growth factors , that had the unexpected properties of rescuing cell division ( Fig 2B and 2C , S2B Fig ) . Eight of the 9 most protective agents were effective at rescuing cell division even when their administration was delayed 48 h after Psy exposure ( Fig 2D ) . Importantly , all small molecules were optimally protective at physiologically achievable concentrations ( i . e . , nanomolar to low micromolar ) , and most are approved for use in humans ( 94% ) and are blood–brain barrier permeable ( >80% ) ( Fig 2E ) . Five agents ( chlorotrianisene [1G05] , NKH-477 [ ( 9C06 ) , also known as colforsin] , clofoctol [8D08] , tulobuterol [9E07] , and insulin-like growth factor [IGF-1] ) revealed by our screens not only significantly rescued cell division but were also able to reduce cell death caused by exposure to higher concentrations of Psy ( Fig 2F ) . With the exception of IGF-I , none of our compounds of interest were previously identified as being able to protect against toxicity of Psy ( or of other lipids accumulating in LSDs ) . Even in the case of IGF-I , previous studies reported that supraphysiological ( >10 μg/mL ) concentrations decreased Psy-induced apoptosis in OLs [92] and in an O-2A/OPC cell line [82] . In our studies , by contrast , 100 ng/mL IGF-1 shifted Psy’s cytotoxicity curve by an order of magnitude and significantly deceased Psy-dependent suppression of self-renewal ( Fig 2G and 2H ) . Further examination of three of the most protective agents—clofoctol , NKH-477 , and IGF-I—demonstrated that these compounds also prevented Psy-induced alterations in lysosomal function . All three suppressed Psy-induced increases in lipid accumulation and cathepsin activity and restored normal endocytosis ( Fig 2I–2K ) . Moreover , all three compounds significantly decreased Psy-dependent increases in pH ( Fig 2L ) . In contrast , five randomly selected molecules that did not significantly reduce any Psy toxicities in our screens ( caffeine [1E04] , acetarsol [5F05] , mepartricin [9A06] , avobenzone [9H10] , and neurotrophin-3; S1 Table ) had no effect on Psy-induced increases in lysosomal pH . As the protective compounds we discovered are structurally and functionally diverse , we next attempted to define regulatory pathways on which these agents might converge to confer their protective activity . To do this , we focused on multiple signaling pathways previously described to be antagonized by exposure of cells to Psy ( e . g . , mitogen-activated protein kinase [MAPK] , phosphoinositide-3-kinase [PI3k]/Akt , protein kinase C [PKC] , cyclic Amp ( cAMP ) -dependent signaling [51 , 74 , 82 , 93–95] ) , as well as other proteins that have been implicated in mediating stress responses in O-2A/OPCs , including Jun N-terminal kinase ( Jnk ) [74] , mammalian target of rapamycin ( mTOR ) [96–98] , and estrogen receptor [99–101] . We next generated a secondary screen consisting of pharmacological inhibitors to components of these various signaling pathways . In these experiments , O-2A/OPCs were exposed to Psy; a combination of Psy and a protective agent; or a combination of Psy , a protective agent , and one of 15 pharmacological inhibitors targeting important signaling pathways and proteins ( S3 Table ) . This allowed for the generation of a compound-specific “fingerprint of protection” that revealed putative signaling pathways used by the candidate compound to overcome Psy-induced suppression of division ( e . g . , Fig 3A and 3B ) . We generated fingerprints for 12 of the most efficacious compounds in reducing Psy-induced suppression of division across 5 d using this approach . The results were then hierarchically clustered to identify similarities and dissimilarities between individual compound fingerprints and between the signaling pathways implicated in protection ( Fig 3C ) . Despite structural and functional diversity among candidate protective agents , there was striking similarity in the signaling pathways needed for protection . The activity of the diverse protective agents was antagonized by pharmacological inhibition of the Ras/rapidly accelerated fibrosarcoma gene ( Raf ) /MAPK pathway , Akt , estrogen receptor , protein kinase A ( PKA ) , and geranylgeranyl transferase ( [GGT] , which is needed for activation of small GTPases that are involved in cell division and migration ) . Despite their structural diversity , there was a surprisingly high degree of correlation between groups of small molecules; the cluster of structurally and functionally unrelated drugs 2G08 ( ethopropazine , an antiparkinsonian drug ) , 2F11 ( estradiol valerate , a synthetic estrogen ) , and 8D08 ( clofoctol , an antibiotic ) , for example , showed the highest degree of similarity ( correl . = 0 . 97 ) ( S3 Fig ) . Lysosomal ion homeostasis , maintained through the activity of several channels and transporters , is critical to the normal function of lysosomes . For example , H+ import is necessary for the maintenance of an acidic pH [102] and is achieved through the activity of the V-ATPase , Ca2+ is important for vesicle trafficking [103] and fusion [104] , Na+ and K+ are required for the regulation of membrane potential [105 , 106] , and Cl− serves as a counterion to regulate lysosomal membrane potential and to facilitate the acidification of the lysosome lumen [107–109] . Although any of these may be potential therapeutic targets , we focused our attention on identifying those channels or transporters regulated by signaling pathways uncovered through our fingerprinting analysis . Of the several pathways that are required for activity of our protective agents , the one for which there is a clearly established linkage to at least one aspect of lysosomal function is the requirement for PKA activity . Previously , it has been shown that cAMP can promote lysosomal re-acidification [110] , as can PKA , which is activated by cAMP [111] . In addition , we found that increases in cAMP not only normalized lysosomal pH but also prevented Psy-induced decreases in O-2A/OPC division ( Fig 4A and 4B ) , raising the theoretical possibility that intervention at this point would provide additional benefits beyond that of pH restoration . We therefore focused attention on the role of lysosomal re-acidification as a potential therapeutic target . The most attractive explanation for how cAMP/PKA activity could restore lysosomal pH would be through activation of the cystic fibrosis transmembrane conductance regulator ( CFTR ) , a PKA-activated transmembrane chloride channel that promotes lysosomal re-acidification [112] . Unlike the CLC-7 Cl−/H+ antiporter , another chloride channel that is localized to the lysosomal membrane and thought to play a role in the basal maintenance of lysosomal pH [113] , the CFTR channel appears only to be critical for re-acidification . Moreover , although the CFTR can be activated by PKA and cAMP , there is no evidence for such activation of CLC-7 . In addition , specific agonists and inhibitors exist for the CFTR , enabling a direct test of whether promoting re-acidification can prevent Psy-induced toxicity . As we predicted , treatment of cells with the cAMP-independent CFTR agonist RP-107 [114] restored lysosomal pH in cells exposed to Psy . Although control of lysosomal pH and/or lysosomal re-acidification has not been thought to have any upstream role in the multiple cellular dysfunctions caused by Psy exposure , we nonetheless found that RP-107 protected against Psy-induced suppression of division , as well as elevated storage of both neutral lipids and phospholipids ( Fig 4C–4E ) . To test the hypothesis that these benefits were not due to off-target effects of RP-107 , we also co-exposed cells to CFTR-inhibitor 172 ( CFTRi-172 ) [115] , which attenuated the protective effects of RP-107 treatment ( Fig 4C–4E ) . The effects of RP-107 were CFTR dependent , as determined by knockdown of CFTR in O-2A/OPCs using small interfering RNA ( siRNA ) pools targeting rat CFTR , as well as a pool of nontargeting ( NT ) siRNAs as a control for transfection; the reduction in CFTR protein levels was confirmed by western blot analysis . Knockdown of CFTR did not significantly affect lysosomal pH when compared to cells exposed to NT controls ( 4 . 96 ± 0 . 13 versus 4 . 81 ± 0 . 11 , respectively ) . Moreover , in the presence of Psy , in both NT and CFTR siRNA pools , there was a significant increase in lysosomal pH ( 5 . 47 ± 0 . 08 versus 5 . 61 ± 0 . 09 , respectively ) , with no significant difference between these two treatment groups . However , when we tested the effect of RP-107 , a specific CFTR agonist , we found that lysosomal pH was significantly reduced in cells exposed to NT siRNA but that CFTR knockdown attenuated RP-107’s protective effect ( 5 . 18 ± 0 . 09 versus 5 . 62 ± 0 . 03 , p < 0 . 01; S4A Fig ) . Thus , as with our pharmacological experiments , genetic loss of CFTR does not appear to significantly affect basal lysosomal pH in untreated cells . However , the protective capacity of RP-107 is CFTR dependent . These results are consistent with the original studies demonstrating the role of the CFTR in control of lysosomal re-acidification [112] . Moreover , we found that the most effective protective agents identified in our studies did not themselves reduce the basal acidic pH of lysosomes in the absence of Psy ( S4B Fig ) but instead seemed to work to promote re-acidification . Indeed , their ability to normalize lysosomal pH , as well as rescue cell division , in cells exposed to Psy was blocked by co-exposure to inhibitors of PKA ( Fig 4F and 4G ) . As these protective agents are able to rescue cells even when applied 48 h after Psy exposure ( Fig 2D ) , it appears that their protective activity is not mediated simply by blocking lysosomal alkalization . The observations that multiple Psy-induced lysosomal and cellular dysfunctions can be prevented by lysosomal re-acidification with RP-107 ( Fig 4C–4E ) and that Psy exposure causes rapid increases in lysosomal pH ( Fig 1F , S1D Fig ) , raise complementary questions about how Psy causes such changes . One possibility is that Psy disrupts the function of particular proteins involved in lysosomal re-acidification , but another possibility is that structural features of Psy itself are directly relevant to understanding effects on lysosomal pH . Although multiple studies have attempted to understand the molecular mechanisms underlying Psy’s toxicity [61–75] , we noted that Psy has unusual physicochemical features that might be of relevance to understanding its effects on lysosomes . Psy is unusual as a cationic , weakly basic lipid , carrying a net positive charge at physiological pH . With a pKa value of 7 . 18 [116] , Psy is predicted to be 99 . 9% protonated in the acidic pH of the lysosome . If this aspect of Psy’s structure is important in altering lysosomal and cellular function , then the protonatable free amine group on Psy should be critical in mediating the changes in lysosomal pH that we observed . We therefore tested whether removing this free amine group altered effects on lysosomal pH and on other outcomes of Psy exposure . We found that the free amine group on Psy is critical in its ability not only to disrupt lysosomal pH but also to cause other toxic effects . We compared Psy toxicity to that of N-acetyl-Psy ( N-AcPsy ) , a structural derivative containing an amide-linked acetyl group , rendering it no longer protonatable ( Fig 5A ) . Unlike Psy , N-AcPsy did not induce cell death or alter O-2A/OPC self-renewal at similar concentrations ( Fig 5B and 5C ) . Moreover , N-AcPsy did not elevate neutral lipid and phospholipid storage , increase endocytic transport time , increase cathepsin activity , or elevate lysosomal pH ( Fig 5D–5G ) . Thus , the positively charged free amine group present on Psy was critical to increasing lysosomal pH and also to the subsequent lysosomal and cellular impairments observed after exposure in O-2A/OPCs . To further test the hypothesis that the presence of free amine group on a cationic lipid is critical to lipid-induced toxicities , and that such lipids provide a direct link between enzymatic mutation and lysosomal disruption , we examined a series of lipids known to accumulate in other LSDs . Other lipids of potential interest include lyso-sulfatide ( lyso-SF ) ( which accumulates in MLD [49] ) , glucosylsphingosine ( GlcSph ) and glucosylceramide ( GlcCer ) ( which accumulate in Gaucher disease [47] ) , and lactosylsphingosine ( LacSph ) and lactosylceramide ( LacCer ) , which accumulate in several LSDs ( Fig 6A ) [78 , 117 , 118] . Some of these lipids appear to have been only rarely studied for their effects on cell function in vitro ( lyso-SF , GluSph , LacSph , LacCer ) [119] . In the case of Gaucher disease , the majority of previous in vitro studies appears to have focused on GlcCer , and studies on both GlcCer and GlcSph often have required lipid concentrations severalfold greater than those at which Psy’s effects were observed ( e . g . , [22 , 50 , 51 , 53 , 55 , 120–122] ) . In order to eliminate differences in cell types as potential contributors to different outcomes , we examined the survival and self-renewal of O-2A/OPCs exposed to lyso-SF , GlcSph , GlcCer , LacSph , and LacCer . Use of these cells also provided a test of the hypothesis that the structure of a lipid is of primary importance in determining toxicity . We also examined the effects of N-acetyl-sulfatide ( N-AcSF ) as a direct comparison with N-AcPsy . We found that sphingosine-derived lipids that accumulate in different LSDs and that contain a free amine group ( and thus are structurally similar to Psy ) caused significant cell death and suppression of self-renewal ( Fig 6B and 6C ) at similarly low lipid concentrations as we observed with Psy . In contrast , exposure to their ceramide-based counterparts GlcCer and LacCer , or to N-AcSF , did not cause cellular toxicities at comparable or 10-fold higher concentrations ( Fig 6B and 6C ) . We also found that lysosphingolipids accumulating in other LSDs [22 , 31 , 47–55 , 123–127] had similar effects as Psy on lysosomal function . Exposure to sublethal concentrations of GluSph , lyso-SF , or LacSph caused increases in neutral lipid and phospholipid accumulation , endocytic transport time , cathepsin activity , and lysosomal pH . In contrast , exposure to their non-lyso counterparts did not have such effects ( Fig 6D–6G ) . If the hypotheses are correct that other toxic lysosphingolipids that accumulate in LSDs work through similar mechanisms as Psy , and that such mechanisms are relevant to understanding the efficacy of our protective agents , then our protective agents also should rescue cells from the toxic effects of lipids from other LSDs . If correct , such findings would provide both the first structural predictors of toxicity and the first example of protective agents of potential relevance in different LSDs . We found that our candidate protective agents also reduced the toxic effects of GlcSph , lyso-SF , and LacSph ( Fig 7A ) . Three of our most effective agents—IGF-1 , clofoctol ( 8D08 ) , and NKH-477 ( 9C06 ) —prevented lipid-induced suppression of division and also attenuated increases in neutral lipid and phospholipid accumulation , endocytic import time , cathepsin activities , and lysosomal pH in rat O-2A/OPCs exposed to sublethal concentrations of GlcSph , lyso-SF , or LacSph ( Fig 7B–7G , S5A Fig ) . These agents also rescued cell division in cells exposed to GlcSph or Lyso-SF for 48 h before addition of protective agents ( S5B Fig ) . We next examined the question of whether the principles revealed in our studies on cells derived from the CNS were applicable to human cells . In these experiments , we used an anti-CD140a ( PDGFRα ) antibody to enrich for a population of human O-2A/OPCs from the corpus callosal field of mid-gestation fetal tissue ( S6 Fig ) [128] and exposed cells to Psy and potential protective agents as for rat-derived cells . Exposure to lysosphingolipids caused death in human cells at concentrations comparable to those used in rat progenitor cells ( S4 Table ) , as well as suppression of cell division and elevation of lysosomal pH at sublethal concentrations , whereas their non-lyso counterparts did not cause similar toxicities ( Fig 8A–8C ) . Notably , cell division and normalization of lysosomal pH were restored in cells exposed to Psy with clofoctol , NKH-477 , and IGF-I , as we observed for rat O-2A/OPCs ( Fig 8D and 8E ) . In the final section of our studies , we asked whether discoveries made on WT cells exposed exogenously to Psy in vitro revealed principles applicable to cells with an LSD-relevant mutation , both in respect to cellular pathologies and to rescue of lysosomal function . These studies were carried out using twitcher mice , a naturally occurring model of KD that recapitulates most human symptoms . Multiple studies have demonstrated that this mouse is a reliable model of KD in respect to enzymatic dysfunction and tissue pathology [77–80 , 129 , 130] and is also one of the most useful models for studying LSDs in general . In particular , twitcher mice progress from a lack of apparent pathology to severe disease over a relatively rapid time course , with function appearing to be normal at birth , followed by disease symptoms manifesting about 20 d after birth and with death ensuing at about 42 d after birth . This time course allows pathology and the effects of treatment to be studied at different stages of disease progression . In our studies on twitcher mice , we first determined that changes in O-2A/OPC function were like those induced by exposure to low doses of Psy in vitro . We found significant reductions in both myelin content and OL cell number ( OLs; Olig2+/GST+ ) in the corpus callosum—the major myelinated tract of the CNS—at P40 when compared to age-matched WT littermates ( Fig 9A and 9B ) , consistent with previous analyses of human and twitcher tissue [76 , 131] . We also observed a significant reduction in the percentage of dividing ( Ki67+ ) O-2A/OPCs ( 54 . 0% ± 1 . 9% of WT , p < 0 . 01; Fig 9C ) at this late time point , during which time O-2A/OPCs should be undergoing rapid expansion through cell division to replace damaged OLs and myelin . We additionally found that O-2A/OPC function was compromised in presymptomatic twitcher mice in ways similar to those induced by Psy exposure . We isolated O-2A/OPCs from presymptomatic P15 twitcher mice and examined their self-renewal capacity in vitro . These cells showed impaired self-renewal in comparison with cells of age-matched WT cells when maintained in vitro for 5 d ( Fig 9D ) . Such findings were mirrored by significant reductions in the pool of dividing O-2A/OPCs in vivo at P15 ( Fig 9E ) . To determine whether cells harboring a mutant GALC gene exhibit changes in lysosomal pH , we examined the endolysosomal pH of corpus callosal O-2A/OPCs acutely isolated from presymptomatic twitcher mice ( P17 ) . We found that the lysosomal pH was significantly less acidic than that of cells isolated from age-matched WT littermates ( Fig 9F ) , similar to what was observed in vitro with exogenous Psy treatment ( Fig 1F ) . Thus , O-2A/OPCs isolated at developmental time points in which symptoms are not obvious ( prior to P18–20 ) show altered lysosomal pH and alterations in critical cellular behaviors like those induced by exposing WT cells to Psy in vitro . We next investigated whether the analytical approach employed in our in vitro studies could identify compounds able to provide clinically relevant benefits in vivo . We focused our studies on NKH-477 ( 9CO6 ) , a water-soluble derivative of forskolin that is approved for treatment of acute heart failure in Japan [132] , as this agent is known to be CNS penetrant and elevates cAMP levels ( through direct activation of adenylyl cyclase ) in brains of rats after systemic administration [133] . Moreover , unlike the other identified protective agents , the linkage of NKH-477 to PKA regulation ( and thus to lysosomal re-acidification ) is both defined and mediated through widely expressed proteins , consequentially not requiring cells to express specialized drug-targeted receptors in order to be responsive . We initiated treatment at P10 , a time when CNS concentrations of Psy are already approaching the range at which we see effects on O-2A/OPCs [78–80] , using once-daily intraperitoneal ( IP ) injections ( 1 mg/kg; Fig 9G ) . This is a point in time when disruptions in neuronal function can already be observed in twitcher mice [134] , raising the possibility of initiating treatment only after subtle clinical changes are first observable . This delayed initiation of treatment is in marked contrast with the well-studied need to initiate the application of bone marrow transplantation and/or gene therapy in the first few days after birth in order to obtain benefit [1 , 10 , 16 , 40 , 130] . The primary endpoints of interest in our in vivo studies were whether we could rescue lysosomal and cellular function in O-2A/OPCs and whether once-daily treatment with NKH-477 is sufficient to provide benefit on both parameters . O-2A/OPCs were isolated at P35 to examine the effects of NKH-477 treatment on lysosomal pH , and we found a normalization of pH in cells isolated from treated twitcher mice when compared to vehicle-treated mice ( Fig 9H ) . NKH-477–treated twitcher mice also showed an increase in the numbers of dividing O-2A/OPCs at P35 to near-normal levels , as well as increases in myelin content and increased OL cell numbers at moribund ages , when compared to vehicle-treated twitcher mice , again to levels not significantly different from WT mice ( Fig 9I–9L ) . Remarkably , we also found that NKH-477 treatment provided significant lifespan extension that was comparable to published single-therapy treatments aimed at restoring GALC activity , including bone marrow transplantation ( the current standard of care in patients ) or viral-mediated gene therapy ( Fig 9M ) [1 , 2 , 10 , 13 , 15 , 16] . Moreover , twitcher mice that received daily injections of NKH-477 also showed significantly improved locomotor and gait function ( Fig 9N–9P , S7 Fig ) and significantly improved weight gain throughout their lifespan when compared to vehicle-treated twitcher littermates ( Fig 9Q ) . These benefits were observed despite the fact that we were not correcting the genetic defect; indeed , we did not find that NKH-477 treatment reduced the overall tissue burden of Psy in the CNS ( Fig 9R ) .
Our studies provide multiple novel findings related to the biology of LSDs . We found in both rodent and human cells that structurally related sphingolipids that accumulate in these disorders appear to directly cause multiple lysosomal dysfunctions . We also discovered multiple pharmacological agents , previously approved for other clinical purposes , that prevent all of the sphingolipid-induced lysosomal and cellular toxicities we analyzed , apparently by promoting lysosomal re-acidification . In vivo studies in the twitcher mouse model of KD demonstrated the ability of one of the agents we identified , which is known to be CNS penetrant , to correct lysosomal pH in O-2A/OPCs , as well as to provide multiple therapeutically relevant benefits in the absence of correcting the underlying genetic mutations implicated in the disease . The finding that pathophysiologically relevant low levels of four different sphingolipids known to accumulate in different LSDs are each sufficient to compromise multiple lysosomal functions appears to provide the first evidence that substances created due to mutations of lysosomal enzymes may be directly responsible for initiating the metabolic dysfunctions that characterize such diseases . Previous studies have speculated that lysosomal dysfunction is caused by such events as intralysosomal accumulation of substances that are not properly degraded ( e . g . , [19 , 20 , 24 , 26 , 28 , 30 , 32 , 135–137] ) , but we could find no prior demonstration—or suggestion—that a specific substance known to accumulate in LSDs is able to simultaneously alter lysosomal pH , endolysosomal trafficking , lipid degradation , and cathepsin activation . Based on the comparative structures of toxic and nontoxic lipids , we hypothesize that toxicity is caused by disruption of lysosomal pH . All of the four toxic lipids we studied share the presence of a theoretically protonatable free amine group , raising the possibility that their accumulation increases the net positive charge in the lysosomal lumen , altering ion homeostasis and decreasing acidification by suppressing proton influx through the V-type H+ ATPase . In contrast , such effects were not caused by other lipids that accumulate in these disorders and that lack this free amine group ( i . e . , GlcCer , LacCer ) , or by lysosphingolipids with an acetylated amine group ( N-AcPsy and N-AcSF ) attenuated toxicity . The only remotely comparable studies we could find to our own were those of Sillence and colleagues [120 , 121] , who reported that exposure of the virally transformed tumorigenic RAW murine macrophage cell line to 40 μM GlcCer for 48 h altered trafficking of boron-dipyrromethene ( BODIPY ) -labeled LacCer to the lysosome , and that an unspecified concentration of GlcCer caused modest increases in lysosomal pH in these cells . However , such studies also demonstrated that exposure to 20 μM GlcCer or GlcSph decreased lysosomal pH in RAW cells exposed to a GlcCer synthase inhibitor , that such effects were not caused by exposure to Psy , and that these GlcCer and GlcSph concentrations caused negligible cell death [120 , 121] . Thus , these previous results differ markedly from those obtained in our studies examining effects of 10- to 20-fold lower concentrations of Psy , GlcSph , lyso-SF , and LacSph and also do not indicate that lipids with similar structures cause similar lysosomal or cellular pathologies . In addition , although studies on sphingosine ( applied at 10-μM concentrations ) suggested the free amine group on this lipid is important for its toxicity , these studies considered the role of the amine group was to confer detergent-like properties on sphingosine and did not consider potential relevance to control of lysosomal pH [138 , 139] . The possibility that changes in lysosomal pH may be of particular importance in understanding the effects of exposure to toxic sphingolipids , and that lysosomal neutralization may be upstream of multiple lysosomal and cellular dysfunctions and may provide a novel therapeutic target , was strongly supported by our findings that we rescued O-2A/OPCs from adverse effects of lipid exposure by activation of the CFTR ( which promotes lysosomal re-acidification [112] ) . Exposure to RP-107 , a chemical activator of CFTR [114] , prevented alterations in lysosomal pH and also rescued cells from adverse effects on division in a CFTR-dependent manner . In addition to CFTR , there are likely several other lysosomal targets that may be relevant for treatment . Accumulation of undegraded sphingomyelin , for example , has been shown to alter membrane trafficking and lysosomal calcium homeostasis through the impairment of the TRPML1 channel [140] . We think it is also important not to interpret our findings as indicating that activation of chloride flux via the CFTR will be the sole mechanism available for promoting lysosomal re-acidification , or that regulation of chloride flux is the only possible way to promote restoration of a normally acidic pH . The CFTR provides a well-studied protein for which there is strong data indicating a role in re-acidification [112] , for which useful experimental drugs are available , and for which a role of PKA in activation has been identified . But it seems likely there will be other proteins that offer potential entry points for promoting re-acidification . In respect to the much more studied problem of the control of basal lysosomal pH , there is strong disagreement on whether chloride ion flux through the CFTR or through CLC chloride channels or Cl−/H+ exchangers is central to controlling basal lysosomal acidification . Expression of mutant CFTR in alveolar macrophages was reported to be associated with a lack of proper acidification of their degradative compartments [141 , 142] . In contrast , other authors found that the CFTR was not required for phagolysosomal acidification in macrophages [143] or respiratory epithelial cells [144] , with other investigators also questioning the importance of CFTR in promoting lysosomal acidification [145] . These disagreements also extend to the CLC chloride channels or Cl−/H+ exchangers , and some investigators have reported that loss of CLC-7 is not associated with alterations in lysosomal acidification [107 , 108 , 146] . Moreover , there are also intriguing observations that cation transport may also be important in the regulation of basal lysosomal pH [102 , 143] . Although it may be that some of these disagreements arise due to use of different techniques [113] , it may also be the case that there are nuances of lysosomal regulation that differ in different cell types , and also that lysosomal re-acidification may be regulated by flux of cations or anions other than chloride . The extent to which controversies regarding control of basal lysosomal pH are pertinent to studies on control of lysosomal re-acidification is not yet known , however . We hope that the results of our present studies will further increase interest in this important problem and will lead to identification of other regulatory pathways of potential therapeutic relevance . Although some of the effects of individual toxic sphingolipids that we studied have been observed previously with other cell types ( although usually at higher lipid concentrations than we utilized , e . g . , [51 , 61–75 , 93 , 95 , 119–122 , 147–154] ) , there is no previous indication that all of these forms of damage may ultimately be controlled by a single metabolic parameter or that such a parameter might control lysosomal pH . It is also worth noting that , although multiple mechanisms have been observed to contribute to particular effects of Psy or of other toxic lipids [51 , 61–75 , 82 , 120 , 123 , 147–150 , 152–187] , none has demonstrated the ability to correct the multiple dysfunctions prevented by promotion of lysosomal re-acidification . Additional support for the hypothesis that control of lysosomal pH is of central importance in understanding the pathology of toxic lipid exposure was provided by the identification , by unbiased drug screening , of novel protective agents that show no known prior overlap in function but that all converged on promoting lysosomal re-acidification . We found that clofoctol , NKH-477 , and IGF-1 all restored lysosomal pH in lipid-exposed cells , despite having no known common properties . Restoration of lysosomal pH appears to be due to promotion of re-acidification , as none of these protective agents acidified lysosomes in the absence of Psy . As the only known convergence of the protective substances we identified ( including RP-107 ) is a common ability to promote lysosomal re-acidification , it currently is most likely that it is this aspect of their effects that is most important . The possibility that regulation of lysosomal pH and re-acidification could represent a convergence point of disease pathology and therapeutic intervention for LSDs appears to be novel . Interest is emerging in the possibility that promoting lysosomal re-acidification may offer therapeutic benefit in situations of lysosomal dysfunction , but studies thus far have been focused only on the possibility that restoring normal lysosomal pH will enhance normal protein degradation [32 , 110 , 111 , 188–192] . Nonetheless , the possibility that regulation of lysosomal pH could represent a central mechanism in disease pathogenesis and treatment is consistent with the dependence of normal lysosomal function on an acidic pH ( as summarized in Fig 10 ) . For example , neutralization can cause release of lysosomal Ca2+ and cathepsins [32 , 192]: Ca2+ release could compromise cytoskeletal function [193] and hence cell division , whereas cathepsin release and activation could initiate cell death [194] . In addition , increasing lysosomal pH would be predicted to decrease function of any lysosomal enzymes evolutionarily optimized for function in an acidic environment . The question of whether it was possible to modify lysosomal and cellular function in an animal model of a severe LSD was studied by administering NKH-477 to twitcher mice , a severe murine model of an LSD that exhibits a pattern of disease progression similar to that seen in KD patients [195] and that is the most widely used model for studying possible disease interventions [1–17] . NKH-477 was the logical choice for such studies , as it was the one small molecule protective agent identified thus far with known CNS penetrance [133] and with a well-defined drug target . In contrast with studies on gene therapy and/or bone marrow transplantation , both of which would be constant in their effects and generally must be initiated shortly after birth when animals are presymptomatic to obtain benefit in experimental models [1 , 2 , 10 , 13 , 15 , 16] , we only administered NKH-477 once daily beginning 10 d after birth . This starting point was chosen both because this is a time when CNS concentrations of Psy first begin to approach those utilized in our in vitro studies and to test the hypothesis that our approaches could identify interventions able to provide benefit even when initiated after it might be possible to detect early changes in neuronal function [65–67] . The primary goal of our in vivo studies was to determine whether NKH-477 administration could be used to normalize lysosomal pH and improve O-2A/OPC function . After first confirming that O-2A/OPCs isolated from twitcher mice showed similar abnormalities as WT progenitor cells treated with Psy in vitro , we found that daily treatment with NKH-477 normalized lysosomal pH ( as analyzed ex vivo in O-2A/OPCs isolated from treated and control twitcher mice ) and rescued O-2A/OPC division in vivo . Benefits of daily NKH-477 treatment extended far beyond rescue of O-2A/OPC division and lysosomal pH and offered several clinically relevant improvements . At the cellular level , daily NKH-477 administration rescued OL numbers and myelin content even at a time when vehicle-treated littermates were moribund . Moreover , mice treated with NKH-477 showed improved motor behavior and weight gain ( suggesting that cell types other than O-2A/OPCs also benefitted from this treatment ) . In addition , survival was significantly extended , even to the same degree previously reported with gene therapy alone ( and exceeding that obtained with bone marrow transplants alone ) [1 , 2 , 10 , 13 , 15 , 16] . These multiple benefits were obtained even though we did not correct the underlying genetic defect nor decrease the overall Psy tissue burden , and there was no prior information on using this compound ( or any related compounds ) in the context of LSDs . Although NKH-477 increases cAMP levels in the CNS [133] and could thus have other effects beyond promotion of lysosomal re-acidification , the fact that NKH-477 shares the property of promoting re-acidification with the other compounds we identified , and was indeed able to rescue lysosomal pH and O-2A/OPC division in vivo , makes it seem likely that this aspect of drug action is at least partially relevant to the benefits observed . Even if some of the in vivo benefits we observed were due to other activities of NKH-477 than promotion of lysosomal re-acidification , this would not decrease interest in this agent as a potential candidate for further analysis . Recent reviews of the outcomes of implementing newborn screening ( NBS ) for detection of early infantile KD ( EIKD ) in New York state [196] have led to the conclusion that , “in addition to the potential harm to families receiving false-positive test results , NBS for EIKD appears to have resulted in a reduction in survival in individuals who have the disease . The data from the New York program suggest that NBS for EIKD should be abandoned , pending the development of improved screening or therapies shown to confer both survival and quality-of-life benefits over supportive care . The results of this experience suggest that research efforts should be focused on improving presymptomatic treatment outcomes in children identified by NBS prior to the redeployment of mandatory presymptomatic screening" [197] . As treatment with NKH-477 confers both survival and quality-of-life benefits in the established animal model for KD and already has been approved ( in Japan ) for use in humans [132] , this may provide an attractive starting point for thinking about new approaches to some of the devastating LSDs with severe neuropathology . Moreover , the discovery of mechanisms and protective strategies that apply to distinct lipids accumulating in three different LSDs provides hope that these same general principles will apply to other LSDs characterized by lysosphingolipid accumulation , and perhaps also in other LSDs ( such as the neuronal ceroid lipofuscinoses/Batten disease ) in which lysosomal pH is abnormally more alkaline [198] . In addition , the ability of re-acidification to rescue a diverse range of lysosomal and cellular dysfunctions raises the question of whether similar strategies might provide broadly useful effects in important diseases in which lysosomal dysfunction has also been implicated , such as Alzheimer’s and Parkinson’s disease ( e . g . , [199–212] ) .
The University of Rochester RSRB has reviewed this study and determined that based on federal ( 45 CFR 46 . 102 ) and University criteria the study does not qualify as human subjects research and has waived the need for consent ( RSRB#00024759 ) . All animal procedures were performed under guidelines of the National Institutes of Health and approved by the Institutional Animal Care and Utilization Committee ( IACUC ) of the University of Rochester Medical Center , Rochester , NY ( UCAR#2001–140 ) . Lipids used in this study were purchased from Matreya and were of highest purity . All lipids were resuspended in anhydrous dimethyl sulfoxide ( DMSO ) to 10 mM , stored at -20°C or -80°C , and resuspended in media before use . Comparable results for OPC division and survival were obtained with Psy purchased from Sigma-Aldrich and Santa Cruz Biotechnology . Corpus callosa of P7 Sprague-Dawley rats ( Charles River ) were micro-dissected , finely minced with a sterile blade , and digested for 20 min in 2 . 2 mg/mL collagenase ( Worthington #4189 ) , 20 Kunitz/mL DNase ( Sigma D4263 ) in HBSS ( Gibco #114170–161 ) supplemented with Sato medium ( see below ) . Collagenase-containing medium was then replaced with 20 Kunits/mL DNase and papain solution ( 1:40 , activated per manufacturer’s directions; Worthington #LS003127 ) in HBSS/Sato for 20 min . Tissue was then sequentially triturated with 21- , 25- , and 26-guage needles in 35 Kunits/mL DNase in DMEM:F12 complete media ( see below ) before dissociated cells were plated on tissue culture plastic for 10 min ( 37°C , 7% CO2 ) . Nonadherent cells were pelleted by centrifugation ( 5 min , 500 xg ) , resuspended in degassed HBSS/Sato supplemented with 1% BSA Fraction V and anti-A2B5 MACS beads ( 1:50; Miltenyi Biotec #130-093-388 ) , and incubated on ice for 20 min . Cells were pelleted and sorted with MACS columns as per manufacturer’s directions ( Miltenyi ) . A2B5+ cells were plated on tissue culture plastic coated with poly-L-lysine ( 1 μg/cm2 for 20 min; Sigma #P1274 ) in DMEM:F12 ( Gibco #11330–057 ) supplemented with 10 μg/mL insulin ( Sigma #I5500 ) , 100 μg/mL holotransferrin ( Sigma #T2252 ) , Sato media ( final concentration: 0 . 03% BSA Fraction V [Sigma #A7979-50ML] , 10 μM putrescine [Sigma #P7505] , 200 nM progesterone [Sigma #P0130] , 235 nM sodium selenite [Sigma #S1382] ) , 50 μg/mL gentamycin ( Gibco #15750–060 ) , 10 ng/mL PDGF-AA ( R&D #221-AA ) , and 5 ng/mL basic FGF ( Miltenyi #130–093 ) and maintained at 37°C ( 7% CO2 ) . Cells were passaged with 0 . 05% trypsin-EDTA ( Gibco #2300 ) , neutralized with 80 Kunitz/mL soybean trypsin inhibitor ( Sigma #T9003 ) , and replated in DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA ( and without basic FGF ) . Purified O-2A/OPCs were passaged no more than once and were maintained in culture for at most 7–9 d in vitro for all experiments . To generate OLs , purified O-2A/OPCs were maintained in DMEM:F12 containing Sato components , transferrin , insulin , 100 pg/mL PDGF-AA , and 45 nM T3/T4 ( Sigma #T6397/#0397 ) for 5 d before initiation of experiments . Human cells were isolated from corpus callosal fields of fetal week 18–21 de-identified tissue , purified with anti-CD140a-coupled magnetic beads ( 1:100; BD Biosciences #558774; Miltenyi ) , and maintained as above . Embryonic neurons were isolated from E18 Sprague-Dawley rats ( Charles River ) and maintained as previously described [213] . Briefly , isolated tissue was digested in papain solution ( 1:50 , activated per manufacturer’s directions; Worthington #LS003127 ) in HBSS/Sato for 20 min at 37°C ( 7% CO2 ) . Pelleted tissue was then triturated with a pulled glass Pasteur pipet in 80 Kunitz/mL DNase ( HBSS; Sigma #D4263 ) , and dissociated cells were pelleted through a 0 . 5 M sucrose cushion ( 10 min , 500 xg ) . Immature neurons were plated on poly-L-lysine-coated tissue culture plastic ( 1 μg/cm2 for 20 min; Sigma #P1274 ) in NeuroBasal media ( Gibco #21103–049 ) with 50 μg/mL gentamycin ( Gibco #15750–060 ) , 2 mM L-glutamine ( Gibco #25030–081 ) , and 1 X B27 serum-free supplement ( Gibco #17504044 ) . Neurons were allowed to mature for 7 d at 37°C ( 7% CO2 ) before initiation of experiments , with a 50% media change every third day . Analysis of cell survival was determined with calcein-AM/propidium iodide , as described below . Purified ( A2B5+ ) O-2A/OPCs were isolated from P7 rat corpus callosa as above and plated at a density of 25 cells/cm2 in poly-L-lysine-coated 24-well plates in DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA immediately after purification . Experiments were initiated after 24 h . Cells were fixed after 5 d in 4% paraformaldehyde , stained with antibodies against A2B5 ( 1:4; in-house hybridoma , ATCC ) and GalC ( 1:4; in-house hybridoma , ATCC ) , and counterstained with DAPI ( 1 μg/mL; Invitrogen #D1306 ) , and the size and composition of each clone was then scored . Spontaneous generation of GalC+ OLs ( i . e . , in the absence of differentiation conditions ) or Type 2 astrocytes was not detected in this paradigm , and so only the number of progenitor cells ( A2B5+GalC− ) per clone is reported . Cell migration with agarose drops was performed as previously reported [214 , 215] . Briefly , purified ( A2B5+ ) O-2A/OPCs were isolated from P7 rat corpus callosa as above , resuspended in 0 . 3% low-melt agarose ( at 37°C; Sigma #A0701 ) , and diluted in DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA at a density of 4 x 104 cells/μL , and 1 . 5 μL of the cell-agarose mixture was plated in the center of a poly-L-lysine-coated 24-well plate . The agarose was allowed to gel at 4°C for 10 min before DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA , with and without Psy . ( PDGF-AA was omitted in some wells as controls for migration , as O-2A/OPC motility is stimulated in vitro by PDGF ) . Half of the media , with and without Psy , was replaced daily for 3 d , after which point the cells were loaded with calcein-AM ( 200 nM ) and imaged . The distance migrated from the agarose drop to the leading edge was quantified as reported [215] . Cells were fixed with 4% paraformaldehyde for 20 min before permeabilization for 10 min with 0 . 5% Triton X-100 ( Sigma #X100 ) in blocking media ( Earl’s Balanced Salt Solution [Gibco] with 5% calf serum and 1% BSA Fraction V ) . Permeabilized cells were then blocked for 1 h at 25°C before overnight incubation with primary antibodies ( A2B5 hybridoma [IgM , 1:4] , GalC hybridoma [IgG3 , 1:10] , Olig2 [1:500; Millipore #MABN50] , Ki67 [1:1000 , BD Pharmingen #550609] , GFAP [1:2000; DAKO #Z0334] , Tuj1 [1:2000; Abcam #14545] ) diluted in blocking media at 4°C . After washing , cells were incubated with species- and isotype-matched Alexa Fluor-conjugated secondary antibodies ( 1:2000; Invitrogen ) and counterstained with DAPI ( 1 μg/mL; Invitrogen #D1306 ) for 30 min at 25°C before final washes with PBS and ddH2O . Mice were transcardially perfused with 4% paraformaldehyde/PBS . Isolated tissue was post-fixed for 24 h in 4% paraformaldehyde and normalized for 48 h in 20% sucrose . Brains were sectioned at 15-μm thickness in OCT ( Tissue Tek ) using a cryotome and immunostained with Ki67 ( 1:250; BD Pharmingen #550609 ) , Olig2 ( 1:500; Millipore #MABN50 ) , GST-pi ( 1:500; BD Biosciences #610718 ) , Fluoromyelin ( Invitrogen ) , and DAPI ( Invitrogen ) . Mosaic images were acquired using a Leica TCS SP5 laser confocal microscope with a 40x oil immersion lens . Data represent analyses of the corpus callosa of at least three WT and three twitcher brains from separate litters . Cells were incubated with 200 nM calcein-AM and 1 μg/mL prodium iodide for 30 min at 37°C to determine the proportion of live and dead cells , respectively , in an experimental condition . Single-cell analysis was performed using a Celigo cytometer ( Nexcelom ) and the %Live corrected values are reported ( calcein+PI− ) . The total number of cells per well was determined using Brightfield analysis with a Celigo cytometer ( Nexcelom ) daily across 5 d , and cell numbers were normalized to the number of cells at the beginning of the experiment ( Day 0 ) . The proliferation rate was calculated as the fold change in cell number per unit time ( days ) using linear regression ( 0 . 95 < R2 < 1 . 0 over 5 d ) for each well . To induce differentiation , purified ( A2B5+ ) O-2A/OPCs were exposed to DMEM:F12 complete media supplemented with 1 ng/mL PDGF-AA and 45 nM T3/T4 mixture ( Sigma #T6397/#0397 ) and allowed to differentiate for 5 d . Cells were fixed in 4% paraformaldehyde , stained with antibodies against A2B5 ( 1:4; in-house hybridoma ATCC ) and GalC ( 1:4; in-house hybridoma , ATCC ) , and counterstained with DAPI ( 1 μg/mL; Invitrogen #D1306 ) , and the numbers of GalC+ OLs and A2B5+GalC− progenitor cells per condition were quantified . For analyses of cell division , purified ( A2B5+ ) O-2A/OPCs were exposed to 1 μM Psy with each of the 1 , 040 compounds in the NINDS II Custom Collection library ( Microsource ) diluted to a final concentration of 0 . 2 , 1 , and 5 μM , each in duplicate , 15 compounds per plate . Each plate had control wells ( in triplicate ) of cells exposed to either vehicle ( 0 . 01% DMSO ) or Psy ( 1 μM ) alone . The proliferation rate was determined by daily cell counting using a Celigo cytometer ( Nexcelom ) across 5 d , as outlined above , and the increases in cell number within each well were internally normalized to the number of cells in that well at Day 0 ( before the addition of Psy/compounds ) . The calculated proliferation rates were then normalized to the mean proliferation rate of in-plate vehicle-treated controls . Selected “hits” in both screens ( 15 for survival and 36 for proliferation ) were rescreened at 0 . 2 , 1 , and 5 μM for their ability to significantly reduce Psy-induced suppression of division across 5 d . Those compounds that significantly reduced Psy toxicities with at least one of the three selected concentrations ( 22 in total ) were rescreened using nine-point dose-response curves , ranging from 1 nM to 10 μM , to identify optimally protective concentrations to significantly reduce Psy-induced cell death at 5 d and/or suppression of division across 5 d . The list of 22 compounds that significantly reduced Psy-induced suppression of division was shortened to 15 by elimination of those minimally protective compounds for which commercial sources were cost prohibitive or unavailable . For analyses of cell division , cells were exposed to 1 μM Psy with each growth factor ( including BSA , with which all growth factors were diluted ) diluted to a final concentration of 10 , 33 , and 100 ng/mL , each in triplicate , six growth factors per plate . Each plate had control wells ( in triplicate ) of cells exposed to either vehicle ( 0 . 01% DMSO ) or Psy ( 1 μM ) alone . The proliferation rate was determined by daily cell counting using a Celigo cytometer ( Nexcelom ) across 5 d , as outlined above , and the increases in cell number within each well were internally normalized to the number of cells in that well at Day 0 ( before the addition of Psy/compounds ) . The calculated proliferation rates were then normalized to the mean proliferation rate of in-plate vehicle-treated controls . Concentrations of small-molecule inhibitors of signaling proteins were selected based on their ability to reduce phosphorylation of target proteins when analyzed by immunoblot when possible; the selected concentrations minimally enhanced—or did not alter—Psy’s effects on cell division when examined in the absence of protective agents . Purified ( A2B5+ ) O-2A/OPCs were pretreated with the small-molecule inhibitors for 1 h before the addition of protective agent and Psy ( each in triplicate ) . All plates had vehicle- and Psy-treated wells ( each in triplicate ) , as well as Psy combined with the protective agent of interest , wells as controls . The proliferation rates were analyzed and normalized against vehicle-treated controls , as outlined above . Data were hierarchically clustered with an unweighted Euclidean distance similarity metric ( complete linkage clustering ) using Cluster 3 . 0 and visualized using TreeView . For experiments in which the administration of candidate protective agents was delayed , purified ( A2B5+ ) O-2A/OPCs were exposed to the indicated concentrations of Psy for 2 d before each small molecule or IGF-1 , prepared at a 10X concentration in DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA , was diluted to 1X so as to minimize dilution of Psy and perturbation of cells . An equal volume of DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA was added to untreated and Psy-only control wells . The proliferation rate was calculated from the daily change in cell number from time of administration ( Day 2 ) for three days ( Day 5 ) , as outlined above . Purified ( A2B5+ ) O-2A/OPCs were plated on poly-L-lysine-coated glass-bottom microwell dishes ( MatTek Co . , Ashland , MA; #P35G-1 . 5–14 ) in DMEM:F12 complete media supplemented with 10 ng/mL PDGF-AA after passaging . Cells were loaded with 500 μg/mL LysoSensor Yellow/Blue Dextran ( Invitrogen ) in complete media with PDGF for 24 h prior to treatment . After 24 h , the cells were fixed in 4% paraformaldehyde and were imaged using a Leica TCS SP5 laser confocal microscope with a 63X oil immersion lens . Using an excitation wavelength of 335 nm ( 405 diode ) , emission spectra at 450 nm ( acidic ) and 521 nm ( alkaline ) were quantified , and the ratio of these emissions was calculated using the Leica Advanced Fluorescence software . Live-cell imaging was performed as above , with the exception that cells were not fixed in PFA prior to imaging and analysis . To generate the lysosomal pH calibration curve , the pH of pre-loaded O2A/OPCs was measured as previously described [112] . Briefly , the cells were incubated in calibration buffers ( 20 mM MES , 110 mM KCl , and 20 mM NaCl containing 10 μM monensin and 20 μM nigericin; Sigma ) adjusted to known pH values between 4 . 0 and 6 . 0 at 0 . 5 increments using HCl/NaOH for 1 h prior to imaging , and ratiometric quantification , as above . Calibration curves were generated using both fixed and live cells . Purified ( A2B5+ ) O-2A/OPCs were exposed to indicated conditions for 24 h . Cells were trypsinized and resuspended at a density of 106/mL in conditioned ( treated ) medium and 1:1000 FluoSpheres polystyrene beads ( Invitrogen ) . The cell:bead suspension was incubated at room temperature and gently inverted every 5 min . At indicated time points , 10 μL ( 10 , 000 cells ) were transferred to 1 mL of ice-cold PBS and pelleted at 13 , 000 rpm at 4°C for 5 min . Cell pellets were resuspended in ice-cold 2% paraformaldehyde/PBS and transferred to PLL-coated 96-well dishes to adhere during fixation . The integrated fluorescence intensity per cell was measured using a Celigo cytometer ( Nexcelom ) and plotted over time; the time-to-half-maximal intensity was calculated using curve-fitting software ( Prism ) . Cathepsin B activity was measured as per manufacturer’s instructions ( MagicRed Cathepsin B substrate , ICT ) . Briefly , purified ( A2B5+ ) O-2A/OPCs were treated as indicated for 24 h before exposure to cell-permeant CathB substrate ( 1 μM ) ; substrate cleavage occurred at 37°C for 1 h before the integrated fluorescent intensity per cell was quantified with a Celigo cytometer ( Nexcelom ) . For cathepsin D measurements , fluorescently labeled , cell-permeant CathD active-site inhibitor ( BODIPY-FL Pepstatin A; 10 μM ) was added to O-2A/OPCs that had been treated as indicated for 24 h; active CathD labeling occurred at 37°C for 1 h before the integrated fluorescent intensity per cell was quantified with a Celigo cytometer ( Nexcelom ) . Neutral lipid and phospholipid accumulation were quantified with the HCS LipidTox Phospholipidosis and Steatosis Detection Kit ( Invitrogen ) as per the manufacturer’s directions . Positive controls cyclosporin A ( 10 μM ) and propranolol ( 10 μM ) were used , respectively . Rat O-2A/OPCs were exposed to either 50-nM pools of four siRNA constructs targeting rat CFTR or 50-nM pools of four control siRNA constructs that do not target the rat genome with DharmaFECT-1 transfection reagent ( 1:1000 ) , as per manufacturer’s directions ( Dharmacon ) , for 24 h . Four d post transfection , cells were passaged and either lysed for western blot analysis or plated on glass-bottom dishes for analysis of lysosomal pH , as outlined above . Western blot analysis was performed as previously reported [216] using an anti-rabbit CFTR antibody ( Cell Signaling ) and HRP-conjugated beta-actin ( Santa Cruz ) . Mice were killed at P35 and transcardially perfused with ice-cold PBS . Isolated tissue was flash-frozen in liquid nitrogen and stored at -80°C until analysis . Analysis of sphingolipids was performed by Dr . Jacek Bielawski from the Lipidomics Core at the Medical University of South Carolina ( MUSC ) using liquid chromatography-mass spectrometry ( LC-MS/MS ) and supercritical fluid chromatography-mass spectrometry ( SFC-MS/MS ) methodologies , as described previously [217] . Adult heterozygote ( Galctwi/+ ) C57Bl/6J ( B6 . CE-Galctwi/J ) mice were originally obtained from Dr . Ernesto Bongarzone ( University of Illinois at Chicago , Chicago , IL ) and used as breeder pairs to generate homozygous ( twi; Galctwi/twi ) twitcher mice and WT ( Galc+/+ ) C57Bl/6J mice . All animal procedures were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the University of Rochester School of Medicine and Dentistry and conformed to the requirements of the Animal Welfare Act . In total , three cohorts of aged-matched mice from different litters received daily IP injections beginning at P10: WT mice receiving saline ( n = 6 ) ; twi receiving saline ( n = 6 ) ; twi receiving 1 mg/kg NKH-477 ( Tocris ) in saline ( n = 8 ) . Mice were euthanized at P35 and tissue was isolated after transcardial perfusion with 4% paraformaldehyde . Immunohistochemical analyses were completed as outlined above . For survival analysis , animals were provided moistened chow and hydragel water packs and monitored daily for weight gain . Animals were euthanized when moribund , as assessed by when the animals could no longer ambulate to maintain food and water intake or exhibited clinical signs of pain such as hunched posture and ruffled fur , as determined on a daily basis . Animals were euthanized using CO2 exposure and cervical dislocation . Aged-matched mice from different litters received daily IP injections beginning at P10: WT mice receiving saline ( n = 4 ) ; twi receiving saline ( n = 3 ) ; twi receiving 1 mg/kg NKH-477 ( Tocris ) in saline ( n = 4 ) were analyzed for locomotive ability and gait using the Phenoscan suite and Runwayscan software ( CleverSys , Inc ) at P25 . Multiple parameters , including stance , stride , swing , brake , and propulsion time ( milliseconds ) , stride length ( millimeters ) , and average speed ( millimeters/s ) were collected for each animal over three compliant trials and averaged for both front and rear paws . Bar graphs are plotted as mean ± SEM and represent at minimum three independent biological replicates performed in triplicate , except where noted . Two-group comparisons were analyzed using a Student’s t test , and multiple-group comparisons were analyzed using an ANOVA with Bonferroni post-hoc test . Prism ( v5 . 0; GraphPad ) was used for data analysis and presentation . | Neurodegenerative lysosomal storage disorders ( LSDs ) are severe and untreatable recessive genetic disorders that cause devastating damage to the nervous system . These diseases exhibit severe disruption of lysosomes ( a cellular organelle that breaks down lipids and proteins ) and other aspects of cell function . However , the means by which mutations cause these dysfunctions are poorly understood . By studying different lipids that accumulate in three different LSDs , we found that lipids with specific shared structures are sufficient to cause multiple lysosomal and cellular dysfunctions , including an abnormal alkalization of the lysosomal pH . We prevented all of these dysfunctions by promoting lysosomal re-acidification and discovered several drugs—already approved for other purposes—with unexpected abilities to restore lysosomal pH and rescue cells . In a genetic mouse model of a severe LSD , one of these compounds decreased tissue damage , improved quality of life , and extended survival . In contrast with previous studies on individual disorders , our study provides novel shared principles relevant to several LSDs and uncovers relevant compounds able to provide multiple benefits in a disease-relevant model in vivo . | [
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... | 2016 | Lysosomal Re-acidification Prevents Lysosphingolipid-Induced Lysosomal Impairment and Cellular Toxicity |
Computational tools are widely used for interpreting variants detected in sequencing projects . The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment . The performance of the methods varies widely in different performance assessments , for example due to the contents and sizes of test datasets . To address this issue , we obtained 63 , 160 common amino acid substitutions ( allele frequency ≥1% and <25% ) from the Exome Aggregation Consortium ( ExAC ) database , which contains variants from 60 , 706 genomes or exomes . We evaluated the specificity , the capability to detect benign variants , for 10 variant interpretation tools . In addition to overall specificity of the tools , we tested their performance for variants in six geographical populations . PON-P2 had the best performance ( 95 . 5% ) followed by FATHMM ( 86 . 4% ) and VEST ( 83 . 5% ) . While these tools had excellent performance , the poorest method predicted more than one third of the benign variants to be disease-causing . The results allow choosing reliable methods for benign variant interpretation , for both research and clinical purposes , as well as provide a benchmark for method developers .
Next Generation Sequencing ( NGS ) is widely used in clinical diagnosis as well as in population genetics to investigate patterns of genetic variants in healthy individuals . The large numbers of variants , millions per genome in comparison to reference sequences , pose challenges for detecting disease-causing variants . There are on average about 10 , 000 variants per genome that cause amino acid substitutions [1] . Several databases enable annotation of disease relevance of variants and frequencies among healthy individuals . These include numerous locus specific variation databases ( LSDBs ) that are curated by experts in the genes and diseases . While LSDBs typically concentrate on individual genes and proteins or diseases , the general databases have much wider scope such as ClinVar [2] , Online Mendelian Inheritance in Man ( OMIM ) [3] and the UniProt Knowledgebase ( UniProtKB ) [4] . The most harmful variants confer adverse impacts and reduce the fitness of the carrier , and are therefore selected against and removed from the population . On the other hand , the benign variants are tolerated and are inherited through the generations . Therefore , variants occurring at high frequencies in a population are likely benign . Information for variants and their frequencies in various populations are available e . g . in the database of short genetic variations ( dbSNP ) [5] , the 1000 Genomes Project [6] , the Exome Sequencing Project ( ESP ) Exome Variant Server ( EVS ) [7] , and recently in the Exome Aggregation Consortium ( ExAC ) database [8] . These resources are widely used to filter out likely benign variants as well as for training and testing computational tools . Variants with allele frequencies ( AFs ) ≥1% are generally assumed to be benign , assumption widely used by e . g . predictor developers [9–12] . There are some exceptions e . g . in late onset diseases or due to incomplete penetrance . We are not aware of reliable estimates of such cases . Sickle cell anemia-causing E6V substitution in β-globin is probably the best known example . The number of such cases is so low that it does not affect results based on large scale studies , as in here . Most variants in these databases are rare , for example in the ExAC database , 99% of the variants have AF below 1% [8] , and have unknown clinical relevance . Prediction tools are instrumental for variant effect interpretation in personalized and precision medicine since experimental methods cannot deal with the amounts of variation data generated in sequencing projects . The American College of Medical Genetics and Genomics ( ACMG ) and the European Society of Human Genetics ( ESHG ) guidelines recommend using computational predictions as one of several lines of evidence for variant interpretation [13 , 14] . Similarly , the joint consensus recommendation for the interpretation of variants in cancer by the Association for Molecular Pathology , American Society of Clinical Oncology , and College of American Pathologists include the use of computational predictions [15] . Numerous computational tools based on different principles have been developed to predict the tolerance and pathogenicity of genetic variants [16–19] . The performance of these tools varies widely [16 , 20–23] . Even a minor difference in the performance leads to misinterpretation of large numbers of variants in genome or exome-wide scale . Hence , the choice of the tools is critical for reliable variant interpretation . The assessment of method performance requires benchmark datasets with known outcomes . In this field , such datasets are available at VariBench [24] and VariSNP [25] . Further , the assessment has to be made in a systematic way and reporting the full performance of the analyzed methods [26 , 27] , which unfortunately is often not the case , especially for commercial products [28] . In addition to pathogenicity/tolerance method assessment , the performance of some other predictor classes have been assessed including alternative splicing [29 , 30] , protein stability [31 , 32] , protein solubility [33] , and protein localization [34] . A comprehensive predictor assessment requires a benchmark with both positive ( showing the effect ) and negative ( not having an effect ) variants . Here , we tested the predictor specificity i . e . the capability to recognize variants not having phenotypic effect using the largest available dataset of likely benign variants . Recently , the ExAC database that has been carefully curated and contains quality-controlled data for altogether 60 , 706 exomes was released [8] . The database contains the overall frequencies of variations across all the individuals as well as the frequencies for several populations . We obtained the common variants from the ExAC database and identified those leading to amino acid substitutions ( AASs ) . In total , 63 , 160 AASs had AF ≥1% and <25% in at least one of the cohorts in the dataset . These AASs are widely considered as benign and therefore were used to assess the performance of the prediction tools . We investigated the performance of 10 widely used prediction methods and found that the best tools are excellent while some others have poor performance .
The variation data were obtained from the ExAC database ( release 0 . 3 . 1 ) [8] in a Variant Call Format ( VCF ) file . We identified the variants leading to amino acid substitutions ( AASs ) by using the annotations from the Variant Effect Predictor ( VEP ) [35] included in the downloaded VCF file . The amino acid substitutions were further filtered by using the AFs in the whole dataset as well as in different populations . The VCF file contained AFs for various datasets and populations . The adjusted AF ( AF for all individuals with genotype quality ( GQ ) ≥20 and depth ( DP ) ≥10 ) as well as the AFs in all geographical populations ( African , American , East Asian , Finnish , non-Finnish European , South Asian , and Other ) were used in the analysis . In addition , we defined the AFs for variants in males and females . Variants having AFs ≥1% and <25% in any of the 9 populations were included to the study . We set an upper threshold of AF to 25% , so that the AFs represented the minor alleles . If the four nucleotides have a random distribution in a position , a minor allele cannot have a frequency >25% without becoming the major allele . In total , there are 63 , 197 variants that meet these criteria . The dataset is available at VariBench ( http://structure . bmc . lu . se/VariBench/exac_aas . php ) . The predictions were obtained from the dbNSFP database ( version 3 . 2a ) [36] for several tools . The database contains annotations and predictions for all potential single nucleotide substitution-caused AASs . We obtained the predictions for Combined Annotation Dependent Depletion ( CADD ) [37] , Functional Analysis through Hidden Markov Models ( FATHMM ) [38] , Likelihood Ratio Test ( LRT ) [39] , MutationAssessor [40] , MetaLR [9] , MetaSVM [9] , MutationTaster2 [41] , Polymorphism Phenotyping v2 ( PolyPhen-2 ) [42] , Protein Variation Effect Analyzer ( PROVEAN ) [43] , Sorting Intolerant From Tolerant ( SIFT ) [44] , and Variant Effect Scoring Tool ( VEST ) [45] . If there were multiple predictions for a variant from the same tool , we took the most frequent classification . If two classes were equally frequent , then the classification was considered as ambiguous . In addition , we obtained predictions for PON-P2 [22] by using the tool’s Application Programming Interface ( API ) . Training datasets were obtained for FATHMM , MetaLR , MetaSVM , PolyPhen-2 , VEST , and PON-P2 and cases in them were excluded from assessment of those tools . Since no variations were left for Meta-LR and Meta-SVM after excluding the training data , we could not evaluate these methods . Variants with AF ≥1% and <25% in a specific population are considered as common for that population . This criterion was used to obtain 10 subsets of variation data ( Adj , AFR , AMR , EAS , FIN , NFE , SAS , OTH , MALE , and FEMALE ) . For the six geographical populations: African/African American ( AFR ) , Latino ( AMR ) , East Asian ( EAS ) , Finnish ( FIN ) , Non-Finnish European ( NFE ) , and South Asian ( SAS ) , the datasets were further partitioned into population-specific unique and non-unique datasets . The unique dataset contains variants with AF ≥1% and <25% in the specific population but <1% in all other populations and the non-unique dataset consists of the remaining variants . For example , the variants with AF ≥1% and <25% in AFR population are indicated as common variants for AFR population . From those , the variants with AF <1% in all the five other geographical populations are unique variants for the AFR population . The remaining common variants in the AFR population are non-unique variants . To exclude misclassified pathogenic variants in the dataset filtered with the AF threshold , we obtained from ClinVar all the 24 , 232 variants that lead to AASs and were annotated as pathogenic or likely pathogenic ( 13 July 2018 ) [2] . There were 37 variants which had AF ≥1% and <25% , some of which had been used for predictor training: FATHMM ( 14 variants ) , PON-P2 ( 14 ) , PolyPhen-2 ( 4 ) , and VEST ( 6 ) . The reason at least for some of these variants to be included into the training datasets is that more data may have accumulated to reclassify variants after the methods were trained . Except for CADD and VEST , the investigated methods classify the variants into harmful and benign . We used these classifications for the method performance assessment . For CADD , we classified the variants based on the phred-like score with a cutoff 20 , below which the variants were classified as benign and otherwise harmful , as suggested by the authors . For VEST , we classified the variants based on the VEST score with a cut-off 0 . 5 , below which the variants were classified as benign and otherwise harmful . The terms deleterious , damaging , probably damaging , possibly damaging , disease-causing , functional , and pathogenic were all considered to be harmful and the terms tolerated , benign , neutral , non-functional , and polymorphism were all considered to be benign . MutationTaster2 provides automatic annotations for harmful and benign variants based on annotations in variation databases and predicts the impacts for others . In this study , the automatic annotations of MutationTaster2 were excluded to test the actual prediction capability of the tool . PON-P2 and LRT classify variants into three classes , the third class being variants of unknown significance . The variants classified as unknown were excluded . Several measures are needed to describe the overall performance of prediction methods [26 , 27] . Since we investigated only one type of variants , the benign ones , it was possible to calculate only a single measure , the specificity . Specificity is the proportion of correctly predicted benign variants , Specificity=NumberofpredictedbenignvariantsTotalnumberofpredictedvarianteffects ( harmfulorbenign ) . The scores can be multiplied by 100 to show results in percentages .
To assess the quality of variant pathogenicity/tolerance prediction methods we collected from the ExAC database all variants that had AF ≥1% and <25% . Because of their high frequency , these variants are usually considered to be neutral and were used in here to assess the specificity of prediction methods . We tested whether 10 widely used methods having different background and design principles showed differences in their performance for benign variants . The predictions for 9 tools were collected from the dbNSFP database [36] . For PON-P2 [22] , we run the predictions using the Application Programming Interface . We could not evaluate four tools MetaLR [9] , MetaSVM [9] , M-CAP [46] , and REVEL [11] . MetaLR and MetaSVM are meta predictors , after excluding the training datasets of their constituent tools no variants were left for evaluation . REVEL has been trained with several datasets including Exome Sequencing Project and The 1000 Genomes project that form a substantial part of the ExAC dataset that we used for testing . Thus , analysis of the performance with ExAC data would introduce circularity and not indicate true performance , instead denote how well the methods have learned the training data . M-CAP is aimed for rare variants , therefore predictions for common variants were not available and the method performance could not be assessed . The tools are based on different principles and include those based on evolutionary information only , LRT [39] , PROVEAN [43] , and SIFT [44] , and those combining different types of features , CADD [37] , FATHMM [38] , MutationAssessor [40] , MutationTaster2 [47] , PolyPhen2 [42] , PON-P2 [48] , and VEST [45] . Most of the investigated tools have been trained with known disease-causing and benign variants . The methods that use only sequence conservation information have not been trained . If variants used for training are used for assessing the methods , the obtained performance measures are likely inflated [20 , 26 , 49] . Hence , we excluded the training datasets for FATHMM , PON-P2 , PolyPhen-2 , and VEST . The remaining tools were either not trained or the training datasets were not available . All the tested tools classify variants into pathogenic and benign classes except for CADD and VEST . CADD predicts a continuous phred-like score that ranges from 1 to 99 , higher values indicating more deleterious cases . The score for VEST indicates benign when 0 and pathogenic when 1 . For CADD we used the highest phred-like score cutoff recommended by the authors i . e . 20 . For VEST , we classified the variants into two classes using VEST score cutoff of 0 . 5 . To evaluate usability of the CADD and VEST cutoffs , we analyzed the sensitivities and specificities of the tools at different cutoffs which showed that the optimal VEST score cutoff is between 0 . 45 and 0 . 5 and phred-like score cutoff is between 20 and 25 ( S1A and S1B Fig ) . The performances of some of these tools have been assessed previously several times , however not with this kind of high-quality and large dataset for benign variants . It is important both in research and clinical practice to be able to sort out variants that have no relevance for the condition under investigation . The specificities of the methods range from 0 . 63 for SIFT and 0 . 64 for MutationTaster2 to 0 . 96 for PON-P2 ( Table 1 ) . FATHMM and VEST have the second and third highest performance i . e . 0 . 86 and 0 . 84 , respectively . It should be noted that variants are classified into three classes by PON-P2 and two classes by FATHMM , and VEST , and CADD does not group variants into pathogenic and benign categories , instead predicts continuous probabilities . For VEST , we classified the variants into two classes using a cutoff of 0 . 5 . The methods that use evolutionary data only are towards the end of the list ( Table 1 ) . Their specificities are 0 . 724 for LRT , 0 . 774 for PROVEAN and 0 . 634 for SIFT . Machine learning methods populate both ends of specificity spectrum . PON-P2 , FATHMM and VEST have the highest scores while the specificities for MutationTaster2 and CADD are 0 . 640 and 0 . 643 , respectively . It is not possible to draw definitive conclusions from the ways methods have been implemented , except saying that machine learning methods can reach much higher specificities in the best installations . To systematically assess the performance of prediction tools , it would be important to include both pathogenic and benign variants . However , since there is no dataset of pathogenic variants that has not been used for training any of the tools , we could not perform a similar analysis for the pathogenic variants . Therefore , we used a small set of pathogenic and likely pathogenic variants from ClinVar to compare sensitivities of the tools side by side with the specificities ( S1C Fig ) . Since we could not filter out variants used for training of all the tools , we did not do this for any of the methods . High sensitivities indicate that the tools with high specificities are not overfitted towards predicting all the variants as benign . Apart from that , we do not recommend to use the sensitivity scores presented here as reliable estimates of performance . S1D Fig shows almost identical results to those in S1C Fig when the ClinVar variants were evaluated together with the variants predicted by all the methods . PON-P2 had the highest proportion of unclassified variants , however with far better specificity compared to the other tools ( Fig 1 and Table 1 ) . The end users have to decide what is most relevant for them—large coverage with additional false positives or lower coverage but highly reliable predictions . One percent difference in specificity means >100 false positives more or less per exome , thus the differences accumulate very fast . To compare the performance of tools on the same set of variants , we computed the specificities of the tools on variants for which all tools made predictions ( Table 1 ) . The scores are somewhat different for all the methods and that is normal for different test datasets . The largest difference is seen for PON-P2 , however , it is still the best predictor also on this dataset . The number of variants predicted by all the tools , 7 , 268 , is only 11 . 5% of the total number of cases . There are various reasons for differences in coverage , some data items may be missing , some sequences are unique for human and may therefore not be evaluated , etc . All the methods have their limitations . Comparison of both the sets in Table 1 shows that the ranking order of the methods remains practically the same . The major differences are that FATHMM has higher specificity than VEST , and CADD has the lowest specificity of all , for the variants that all the tools can predict . The other analyses are reported for all the variants that each method predicted to cover as many variants as possible . Next we investigated whether the differences in specificities could be due to certain types of variations or whether they were due to differences in the methods . To assess the performance of tools for variants with different AFs , we divided the dataset into groups based on adjusted AF on the whole dataset . The predictor performance is higher for variants with higher AFs for all the tools ( Fig 1 ) . The specificity differences between the AF bins are the smallest for PON-P2 and FATHMM while several other methods , including CADD , LRT , PolyPhen , SIFT and VEST , had very strong correlation between specificity score and allele frequency . As mentioned above , 1% difference in specificity means a difference of over 100 false classifications in an exome . Since the dataset is so large even a small difference in specificity is statistically significant . Results for Fisher exact tests between the pairs of tools show that the differences are significant for all variants ( S2A Fig ) as well as for variants predicted by all the tools ( S2B Fig ) . The tools with similar performances have high p-value ( low negative logarithm of p-value ) . CADD , SIFT and MT2 form one group where the results are somewhat similar , PolyPhen2 , LRT and MutationAssessor form another group , The rest of the tools have significantly different performances for all variants , VEST , FATHMM and PROVEAN have similar performances . The differences are large as the p value scale ranges from 1 to 10−16 . Thus , practically all the differences are statistically highly significant . ExAC database contains information for the genetic ancestry of the individuals . Thus , in addition to the general performance , we were able to analyze also ancestry-based assessment . The same three tools , i . e . PON-P2 , FATHMM , and VEST , showed the highest specificities also on the data for the ancestry groups ( called for populations hereafter ) ( Fig 2 ) . The methods , however , show somewhat different performances for different populations . PON-P2 and FATHMM have small performance differences between the populations , 2 and 1% , respectively , while VEST has bigger performance differences , 11% between FIN and AFR . Interestingly , all the tools have the lowest specificity for the Finnish population . This is presumably because the small , and in the past rather closed population passed through a narrow bottleneck some 300 years ago during which certain unique alleles were highly enriched . We analyzed whether the differences in specificities in the populations were due to the differences in the percentages of variants predicted as unknown ( S1 Table ) . The percentages of the predicted unknown variants for most tools are similar across all populations except for the Finnish population . Most tools , except for PON-P2 , have the lowest percentage of variants that could not be predicted for the Finnish population . On the other hand , PON-P2 has slightly higher percentage of unknown variants in Finnish population . The difference in performance between the populations is much bigger than the difference in the percentage of unknown variants . Next , we identified population-specific common variants which have AF ≥1% and <25% in one population but have AF <1% in all the other populations . These are referred to as population-specific unique variants and the remaining variants for the population are referred to as non-unique variants . The proportions of unique variants vary in the populations , ranging from 6 . 8% in European population ( excluding Finnish ) to 62 . 4% in the African population ( S2 Table ) . Humans have their origin in Africa and it is well known that the African population has the highest variation as most variants are recent , see e . g . [50] . The tools showed lower specificities for the unique variants than for the non-unique variants in the populations ( Fig 3A ) . The lowest performance is seen for the unique variants in the Finnish population . Performance differences vary largely depending on the tools and the populations . The performance differences between the unique and non-unique variants are the lowest in the African population ( 0 . 6–3 . 5% ) and the highest in the Finnish population ( 3 . 2–12 . 1% ) ( S3 Table ) . With respect to the tools , the differences for unique variants are the lowest for FATHMM ( ranging from 1 . 3 to 4 . 1% ) and PON-P2 ( ranging from 1 . 2 to 8 . 0% ) and the largest for MutationTaster2 ( 18 . 4% ) , VEST ( 16 . 4% ) and LRT ( 14 . 9% ) . The differences for the unique and non-unique variants in each population are visualized in Fig 3A . The differences are the smallest for FATHMM and PROVEAN , up to 3 . 6 and 6 . 6% , and the largest for LRT and CADD , up to 18 . 7 and 12 . 2% . As the tools have lower performances for unique variants , we investigated the frequencies of unique variants and those that were not unique ( i . e . non-unique ) . Most unique variants have low AF , between 1% and 5% , while the proportions of non-unique variants with different AFs are similar ( Fig 3B ) . Since many predictors have been trained with variants with high allele frequencies as benign variants , the lower specificities for unique variants could be due to disparity in the frequencies . To control the bias due to frequency , we compared the performance of the tools for unique and non-unique variants with AF in the same range ( i . e . 1–5% ) in each population . The comparison showed that the tools indeed have poorer performance for unique variants than for non-unique variants ( Fig 3C ) . The differences are the smallest for FATHMM , PON-P2 and PROVEAN , up to 3 . 7 , 6 . 7 and 6 . 7% , and the largest for CADD and MutationTaster , up to 12 . 2% for both ( S4 Table , Fig 3C ) . For Finnish population there are generally the largest differences ( 3 . 2 to 12 . 2% ) . Finally , we evaluated the performance for variants from males and females in the populations . No differences were observed in predictor performance . Most of the variants in these two datasets are overlapping . The proportions of unique variants in male ( AF ≥1% in male but <1% in female ) and female ( AF ≥1% in female but <1% in male ) populations are 5 . 6% and 16 . 9% , respectively ( S5 Table ) . The number of unique variants in females is 3 . 4 times higher than the unique variants in males . This may be because of the larger numbers of females than males with African ancestry ( 1 . 75 times ) in the ExAC dataset . The AFR population has the largest percentage of unique variants compared to the other groups . The performance for unique variants in male is lower than for the common variants and unique variants in female ( Fig 4 ) . To assess the influence of variants in sex chromosomes for the lower performance of tools for unique variants in males , we examined the proportions of variants for females and males in all chromosomes . As there were only 3 variants in Y chromosome we could not investigate performance for variants in this chromosome . In the remaining chromosomes , the ratio of unique variants in males to females range from 0 . 17 to 0 . 39 , with a median of 0 . 30 . The ratio is 0 . 28 in the X chromosome , i . e . very close to the median ( S5 Table ) . The tools show only minor differences in the specificities for variants in different chromosomes ( Fig 5 ) .
Performance comparison of the computational tools enables choosing the most reliable methods . Critical Assessment of Genome Interpretation ( CAGI , https://genomeinterpretation . org/ ) is a community wide effort to assess variant interpretation tools and approaches in the form of competitions [51] . In addition , performance of the tools has been tested by the developers as well as independent researchers . Since some predictors are frequently updated and new ones are developed , they should be assessed regularly [17] . Large datasets of both positive and negative classes are required to assess the performance comprehensively . Due to the lack of a large dataset of disease-causing variations that does not overlap with the training datasets used by the method developers , we could not assess the true positive and false negative rates for the tools . Although several performance measures are required to describe the overall performances of prediction methods [26 , 27] , we could only compare specificities of the tools , i . e . the capabilities of the tools to detect benign variants . We used the common variants from the ExAC database and the variants predicted to be neutral were considered as correct predictions and those predicted to be disease-related as false negatives . The large size of the ExAC database lends strength for the analysis . Many tools have been trained with disease-causing and likely benign variants . In most cases , the benign variants have been selected based on their allele frequencies in general population ( s ) . The common variants are considered as benign and the tools have been benchmarked against them . In some rare cases disease-related variants can have high frequency at least in some populations ( e . g . sickle cell anemia HbS allele ) . However , such cases are very rare and are not considered to affect statistics when using large number of cases , as in here . The analysis of burden of the harmful variants revealed that most harmful variants have extremely low AFs [52] . However , benign variants can have equally low AFs as harmful ones . Performance assessments of tools with variants with all AFs for both harmful and benign variants are desirable; however , such dataset does not exist . In this study , we defined variants with AF ≥1% and <25% as benign variants . The upper limit of 25% was set so that the variant allele analyzed is a minor allele even when the variant site has a random distribution of the four nucleotide bases in the population . Although performance evaluation of prediction tools on such common variants may overestimate specificities of the tools , validated benign variants with low AF values are rare . Our results show that specificities increase with AF and have similar trend for all the tools ( Fig 1 ) . Therefore , assessments using the common variants provide useful comparison of the performance of predictors . Our results show that the performances of tools in detecting the benign variants vary widely . The specificities of the tools ranged from 63 . 4% to 95 . 5% ( Table 1 ) . PON-P2 [22] had the best performance while MutationTaster2 [41] , SIFT [44] , and CADD [37] showed the poorest specificities . MutationTaster2 directly annotates the variants as disease-causing or benign based on the dbSNP [5] , The 1000 Genomes Project [6] , ClinVar [53] , and HGMD [54] data . We excluded such automatic annotations in this study to compare the predictive performance of MutationTaster2 . In addition to the specificities of the tools , we also compared the performance on variants common in different geographical populations . All the methods showed performance differences for populations , the lowest specificity was achieved for the variants in the Finnish population ( Fig 2 ) . The variants that were unique in specific populations ( AF ≥ 1% and < 25% in the specific population but AF < 1% in all other populations ) were more difficult to predict . The tools showed from slightly to markedly lower performance for these variants ( S3 and S4 Tables ) . Most of the unique variants had AFs < 5% ( Fig 3 ) . To investigate the possibility of the performance associated with low AF , we compared the performance for the unique variants and the non-unique variants ( those with AF ≥ 1% in more than one population ) with AF < 5% in the same population . The comparison showed that the specificities were slightly poorer for the unique variants than for the non-unique variants . Differences in the performance on chromosome-wide analysis were very small for all the tools ( Fig 5 ) . The methods showed very broad spectrum of performances; thus , it is important for the end-users in research as well as in precision medicine to pick a reliable one . Our results enable comparison of the tools and choosing the most reliable ones for interpretation of benign variants . | In precision/personalized medicine of many conditions it is essential to investigate individual’s genome . Interpretation of the observed variation ( mutation ) sets is feasible only with computational approaches . We assessed the performance of variant pathogenicity/tolerance prediction programs on benign variants . Variants were obtained from high-quality ExAC database and selected to have minor allele frequency between 1 and 25% . We obtained 63 , 160 such cases and investigated 10 widely used predictors . Specificities of the methods showed large differences , from 64 to 96% , thus users of these methods have to be careful when choosing the one ( s ) they will use . We investigated further the performances on different populations , allele frequencies , separately for males and females , chromosome wise and for population unique and non-unique variants . The ranking of the tools remained the same in all these scenarios , i . e . the best methods were the best irrespective on how the data was filtered and grouped . This is to our knowledge the first large scale evaluation of method performance on benign variants . | [
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"genome... | 2019 | How good are pathogenicity predictors in detecting benign variants? |
The Iowa Gambling Task ( IGT ) is one of the most common paradigms used to assess decision-making and executive functioning in neurological and psychiatric disorders . Several reinforcement-learning ( RL ) models were recently proposed to refine the qualitative and quantitative inferences that can be made about these processes based on IGT data . Yet , these models do not account for the complex exploratory patterns which characterize participants’ behavior in the task . Using a dataset of more than 500 subjects , we demonstrate the existence of sequential exploration in the IGT and we describe a new computational architecture disentangling exploitation , random exploration and sequential exploration in this large population of participants . The new Value plus Sequential Exploration ( VSE ) architecture provided a better fit than previous models . Parameter recovery , model recovery and simulation analyses confirmed the superiority of the VSE scheme . Furthermore , using the VSE model , we confirmed the existence of a significant reduction in directed exploration across lifespan in the IGT , as previously reported with other paradigms . Finally , we provide a user-friendly toolbox enabling researchers to easily and flexibly fit computational models on the IGT data , hence promoting reanalysis of the numerous datasets acquired in various populations of patients and contributing to the development of computational psychiatry .
Many neuropsychiatric disorders are associated with alterations of learning and decision-making . Standardized cognitive paradigms are thus increasingly used to improve diagnosis and evaluate the response to treatments . Developed 25 years ago [1] , the Iowa Gambling Task ( IGT ) remains one of the most popular tools used for this purpose in clinical settings ( Fig 1A ) . Over the years , it has been applied more or less successfully to many populations such as patients suffering from brain lesions , Parkinson disease , behavioral or substance addictions , mood disorders , personality disorders , etc . Although its reinforcement schedule confounding risk and punishment processing can be criticized , the IGT thus remains of considerable importance for the development of scalable methods in cognitive science and in the emerging field of computational psychiatry . The classical analysis strategy for IGT data results in a crude estimate of decision-making deficits . Based on the relative preferences for “advantageous” decks ( typically offering small gains but even smaller losses ) over “disadvantageous” decks ( typically offering big gains but even bigger losses ) , this approach does not leverage the full potential of the IGT . From a computational viewpoint , the IGT is indeed a highly complex task engaging value-based learning , risky decision-making , working memory and—as we shall see—different types of exploration . A series of computational models have been developed to better isolate these components , thereby offering clinicians and clinical neuroscientists more precise analytical tools to assess the cognitive profile of their patients . So far , computational neuroscientists interested in the IGT have mainly focused their efforts on the value-based learning and decision-making components of the task . Accordingly , the Expected Value ( EV ) or the Prospect Valence Learning ( PVL , PVL-Delta ) algorithms aim at capturing the non-linear and/or asymmetric decision weights associated with gains and losses [2 , 3] . More recently , the Value Plus Perseveration ( VPP ) model was developed to capture systematic perseveration or alternation tendencies across successive decisions [4] . However , the VPP model can be criticized for its high number of free parameters ( 8 ) relative to the number of trials ( 100 ) as well as for the uncertain cognitive validity of its perseveration module [5] . Finally , the last model proposed to date—termed Outcome Representation Learning ( ORL ) —also encompasses a perseveration module but tracks and weights separately ( at the time of decision ) the magnitude and the probability of positive and negative outcomes [6] . While the ORL was shown to perform better than alternative models , this latter feature is rather unlikely from the perspective of behavioral economics , as it implies that the decision-maker never combines reward magnitude and reward probability into an estimate of reward expectancy . Here , we adopted another modeling strategy leveraging the existence of directed exploration ( DE ) in the class of multi-armed bandit tasks to which the IGT belongs [7–9] . Wilson and colleagues defined directed exploration as “a strategy in which choices are explicitly biased toward information” , as opposed to undirected ( or random ) exploration corresponding to a “strategy in which decision noise leads to exploration by chance” [10] . Thus , DE constitutes an “umbrella term” as it can refer to any regular choice pattern which: ( i ) maximize information about available options , ( ii ) cannot be readily explained by participants’ sensitivity to gains and losses . In the context of the IGT , a straightforward DE strategy is to allocate an “exploration bonus” to the behavioral options which have been sampled less often or less recently than others . This mechanism entails the sequential selection of all available option irrespective of their value or uncertainty , hence resulting into a specific choice pattern: namely , the tendency to select the four available decks over 4 consecutive trials ( hereafter referred to as the Sequential Exploration ( SE ) index ) . Exploration bonuses and sequential exploration have a long history in the reinforcement learning literature , as they were already proposed in the seminal book of Sutton and Barto ( Dyna-Q+ algorithm ) [11] . They are also conceptually related to the optimistic initialization techniques used to ensure that all available options available to a decision-maker are sampled before settling on one of them [12] . Thus , we designed a compact computational architecture termed Value and Sequential Exploration in order to simultaneously capture exploitation , random exploration and sequential exploration in the IGT using 5 parameters . The core innovation of this new model is to articulate two types of choice strategies: a reward-seeking strategy shaped by reinforcement history and an information-seeking strategy shaped by choice history ( Fig 1B ) . Governed by a value sensitivity and a decay parameter , the former module reacts to the gains and losses delivered during the task . As such , it resembles to the PVL model except that it includes no loss aversion parameter . The latter module relies on an exploration bonus specific to each participant—which can be either positive or negative depending on whether a given participant tends to explore or avoid options which have not been sampled recently—as well as on a learning rate—which determines how fast the exploration weight of a given deck goes back to the initial value of the bonus after having been sampled . Unlike other forms of directed exploration ( such as uncertainty-dependent exploration ) , the exploration weights of the VSE model are totally independent of gains and losses . Different parameter combinations are thus able to reproduce the full range of possible SE indexes , from 0 to 100% frequency ( see Methods for more details ) . In order to demonstrate the superiority of the VSE model over the alternatives mentioned above ( EV , PVL , PVL-delta , VPP , ORL ) , we reanalyzed a multi-study dataset of 504 participants who passed the 100 trials version of the IGT [13] ( Fig 1A ) . State-of-the-art model comparison , simulation , as well as model and parameter recovery analyses were performed [14] . Second , in order to evaluate the cognitive validity to our model and illustrate its heuristic value , we focused on the data corresponding to the study of Wood and colleagues testing IQ-matched groups of old and young adults [15] . Indeed , it was recently shown that directed exploration diminishes across lifespan [16 , 17] , so that the exploration bonus of older participants should be smaller than that of young participants . Third , we provide an open-source , user-friendly Matlab toolbox which has been developed to obtain the current results and which shall enable researchers who are not experts in computational models to re-analyze IGT data using both our new model and previous ones ( https://github . com/romainligneul/igt-toolbox ) .
First , we evaluated whether sequential exploration ( SE ) occurred in the Iowa Gambling Task . To this end , we computed the “SE index” probing situations in which participants selected each of the four different decks over four successive trials using 25 independent consecutive quadruplets: e . g . 1–4 , 5–8 , etc . In the 504 subjects dataset , we observed such pattern 1400 times ( 11 . 1% ) while only 1182 occurrences would be expected under random exploration ( i . e . 9 . 38% , binomial test: p<10−10 ) . Note that this test is highly conservative , as reward-maximization strategies bias choices towards the most valuable decks . Accordingly , a permutation approach in which trials were shuffled in time for each subject independently ( total number of permutations: 5000 ) showed that the actual chance level was at 6 . 0% . The target pattern was much more frequent in the first 20–30 trials of the task and it continuously declined as subjects formed more precise representation of each desk value and learned to exploit the reward structure of the task ( Fig 1C ) . Interestingly , SE had a complex but strong relationship with decision-making performances in the IGT . A general linear model ( GLM ) analysis indicated that subjects with the highest overall performance had lesser SE indexes ( linear effect: t ( 1 , 501 ) = -3 . 40 , p<0 . 001 ) , presumably due to the fact that these subjects needed less exploratory trials to figure out the reinforcement structure of the task . However , we also observed low SE indexes in the worst subjects , presumably due to maladaptive perseveration , which translated into a significant quadratic relationship between SE and performance ( t ( 1 , 501 ) = 2 . 13 , p = 0 . 034 ) . Overall , the analysis of the SE index justified the development of a computational model capturing this important and previously overlooked exploration strategy in the IGT . Like all previous models , the Value and Sequential Exploration ( VSE ) architecture updates “exploitation weights” , which keep track of the recent trends in gains and losses associated with each deck . However , the VSE model also updates on each trial the “exploration weights” associated with each deck . Depending solely upon the choice history , this exploration module was designed to capture the dynamics of sequential exploration observed in the IGT . On each trial , exploitation and exploration weights are simply summed into a composite value before being transformed by a conventional softmax step into choice probabilities . Hereafter , we describe the equations and the parameters which fully characterize VSE . The exploitation module is directly inspired by the PVL model ( Steingroever et al . , 2013 ) although it includes no “loss aversion” parameter . A value sensitivity parameter controlled by θ ( bound between 0 and 1 ) is instead applied separately to wins and losses . v ( t ) =Gain ( t ) θ−Loss ( t ) θ ( 1 ) On each trial , the exploitation weight of each desk d is updated according to the following equations: Exploitd ( t+1 ) =Exploitd ( t ) *Δ+v ( t ) ( 2 . A ) Exploitd ( t+1 ) =Exploitd ( t ) *Δ ( 2 . B ) Eq ( 2 . A ) controls the update of the deck chosen , by adding the feedback just experienced to the ( decayed ) value of this deck . Eq ( 2 . B ) controls the update of unchosen decks , whose exploitation weight progressively returns to 0 at a rate controlled by the decay parameter Δ ( bound between 0 and 1 ) . Note that a decay of 1 indicate that exploitation weights are integrated over all previous trials , while a decay parameter of 0 indicate that subjects’ decisions rely mostly on the most recent outcomes . The main innovation provided by VSE consists in modeling sequential exploration in the IGT . Exploration weights reflect the attractiveness of each deck as a function of the number of trials for which the deck has not been selected . Exploration weights are agnostic regarding the monetary feedbacks experienced in the task . As such , they capture a pure information-seeking process , hence contrasting with Bayes-based uncertainty-minimization algorithms as well as the exploration modeled by the softmax or e-greedy rules [8] . Exploration weights are controlled by the following equations: Explored ( t+1 ) =0 ( 3 . A ) Explored ( t+1 ) =Explored ( t ) +α* ( φ−Explored ( t ) ) ( 3 . B ) Eq ( 3 . A ) controls the update of exploration weights for the selected deck , which fall to zero as soon as the outcome of that deck is sampled . Eq ( 3 . B ) controls the update of unselected decks , which is governed by a simple delta-rule . The learning rate α ( bound between 0 and 1 ) determines at which speed the exploration weights return to their initial value , defined by a free parameter termed “exploration bonus” or φ ( unbounded ) . A positive exploration bonus implies that the agent is attracted by decks which have not been explored recently , whereas a negative exploration bonus implies that the agent tends to favor familiar decks . Therefore , the exploration bonus φ directly reflects the strength of sequential exploration , so that a more positive value will translate into a higher probability of reproducing the aforementioned pattern of 4 different choices over 4 consecutive trials . Finally , Eq ( 4 ) models decision-making as a stochastic process controlled by the consistency parameter C: a higher C value indicates that choices are strongly driven by the composite values derived from Eqs 1–3 , whereas a C value of zero indicate random selection of each deck . Note that C results from the transformation of an inverse temperature β ( bound between 0 and 5 ) , in order to match PVL , PVL-Delta , VPP and ORL models ( where C = 3β -1 as well ) . Crucially , the architecture of VSE can account for purely random exploration ( β = 0 ) , for purely value-based exploitation ( β>>0 , θ>0 and φ = 0 ) , for purely directed exploration ( β>>0 , θ = 0 , and φ>0 ) and for a mixture of value-based exploitation , directed exploration and random exploration . Note that under purely directed exploration , the model predicts that the 4 decks should be successively selected in a cyclical manner , during the whole task ( e . g . 3 , 2 , 4 , 1 , 3 , 2 , 4 , 1 , 3 , etc . ) , hence reflecting exactly the definition of the SE index . Indeed , in such case , the deck with the highest exploration weight is always the deck which has not been selected for the longest period of time . Finally , a variant of the VSE model including a loss aversion parameter was also tested ( VSE+LA ) . In this model , the update of exploitation weights is therefore identical to that of the PVL model , augmented with the sequential exploration module of the VSE ( see S2 Fig for model comparison analyses including VSE+LA ) . The comparison of the VSE architecture with the 5 alternative models ( described in Methods ) was performed using the 504 subjects dataset . First , a fixed-effect analysis comparing summed Bayesian Information Criterion ( BIC ) , Akaike Information Criterion ( AIC ) and Free Energy ( F ) metrics over the whole cohort demonstrated decisive evidence in favor of VSE . In order to compare Free Energy ( F ) with the other metrics , it was transformed to -2*F for this analysis [18] . The difference between the VSE model and other models was everywhere superior to 512 ( Fig 2A; the least difference being observed with the VPP model based on the Free Energy estimator ) . Note that a difference superior to 100 is generally considered as decisive evidence indicating that choosing the second-best fitting model would incur unacceptable information loss [19] . Going further , we performed a Bayesian Group Comparison ( Stephan et al . , 2009 ) based on the log-evidence of each model and treating model attribution as a random effect . In order to obtain log-evidences , we transformed AIC and BIC values to -AIC/2 and -BIC/2 , respectively ( Free Energy natively represents that quantity and takes into account the uncertainty over parameters when penalizing for model complexity ) . Performed using all available metrics ( BIC , AIC , F ) , this analysis showed that the estimated frequency of the VSE model was in every case superior to 40% and that its approximate exceedance probability ( Ep , probability that a given model is the best candidate model to explain the data ) was always superior to 0 . 99 ( Fig 2B ) . Overall , both approaches to model comparison provided overwhelming evidence in favor of the VSE architecture . Regarding the relationship of model parameters with performance ( defined as the number of advantageous minus disadvantageous deck selection ) , it appeared that value sensitivity was the strongest predictor ( ρ = 0 . 39 , p<0 . 001 ) , followed by φ ( ρ = -0 . 24 , p<0 . 001 ) , decay ( ρ = 0 . 23 , p<0 . 001 ) and temperature ( ρ = -0 . 12 , p = 0 . 006 ) ( S1A Fig ) . Moreover , although a substantial interindividual variability was observed in SE , the parameter φ corresponding to the exploration bonus was significantly superior to 0 ( z = 4 . 78 , p<0 . 001 ) , in line with the existence of directed exploration . A correlation approach then confirmed that this key parameter reflected to which extent participants engaged sequential exploration as assessed by the SE index ( Spearman ρ = 0 . 76 , p<0 . 001 ) . Finally , since the likelihood of observing SE depends on which extent participants exploited the reward structure of the IGT , other parameters also predicted the SE index , but to a much lesser extent ( update of exploration weights: ρ = -0 . 26 , p<0 . 001; value sensitivity: ρ = -0 . 20 , p<0 . 001; consistency parameter: ρ = 0 . 13 , p = 0 . 003 ) ( S1B Fig ) . In order to make sure that the advantage of the VSE model reflected a better ability to predict choice data , both qualitatively and quantitatively , we used the 504 sets of parameters associated with each model to simulate 504 in silico agents playing the IGT . For each deck , feedbacks were drawn randomly from their corresponding empirical distributions , hence keeping reward contingencies similar across actual and simulated datasets . Then , we applied the exact same fitting procedure to this simulated dataset . First , we evaluated to which extent each model was able to reproduce participants’ choices using both first-pass and second-pass ( i . e simulated ) predictions ( chance level: 25% , Fig 2C ) . Again , the VSE model was the most performant model . The first-pass ( i . e fit on actual data ) reproduced 59 . 2+/-16% of the choices , whereas the second best model in this respect ( VPP ) reproduced 58 . 7+/-17% of the choices . While the difference between VSE and VPP was not significant ( z = 0 . 67 , p = 0 . 50 ) , it must be noted that the VPP has 3 more free parameters which results in a greater chance of overfitting . In this respect , it is interesting to note the difference observed when comparing how the choices derived from simulated data reproduced participants’ choices . Here , the advantage of VSE was clear , with 42 . 5+/-17% of successful predictions against 39+/-16% for VPP ( z = 3 . 63 , p<0 . 001 ) . Importantly , the VSE model was also the model which predicted the highest number of sequential exploration events ( Fig 3A; 1993 against 1389 for VPP , the second model in this respect; raw data: 5247 events ) and it was also the most sensitive model according to a d-prime analysis computed over all participants ( Fig 3B; 1 . 15 against 1 . 11 for the VPP ) . Note that dependent quadruplets were used for this analysis ( ie . 1–4 , 2–5 , etc . ) . Second , we evaluated to which extent each model could be recovered . Low models recovery rates implies that the choice data generated by a given model can be better explained by other models , hence suggesting that there is no specific behavioral signature associated with this model and that the interpretations of the best-fitting parameters should be taken with caution . This computationally intensive analysis ( see Methods for details ) showed good recovery performance for three models: EV , PVL and VSE ( Fig 3C ) . The advantage for the model actually used to generate the data was decisive in all cases for all estimators , except for the BIC metric , when fitting data generated with the VSE model with the PVL model . This latter exception is very likely due to the fact that the BIC over-penalized model complexity and thus favored the simpler PVL architecture . Moreover , the fact that the highest confusion occurred between the VSE and the PVL models is sensical , given that the “exploitation module” of the VSE model is based on the same accumulation mechanism used by the PVL model . By contrast , the analysis of the three remaining models ( ORL , PVL-Delta and VPP ) showed that the confusion spanned several models on at least one estimator ( e . g . VSE , PVL and VPP performed better to fit ORL-generated data , all models performed better to fit VPP- and PVLDelta-generated data ) . Third , we investigated how the parameters estimated from individual choice data could be recovered for each model . Indeed , methodological studies in the field of computational modeling have demonstrated that different combinations of parameters can account for the same sequence of decisions , and that small deviations in parameters values can conversely result in significantly different sequences of decisions , hence impeding the interpretability of best-fitting parameters in some cases . Parameter recovery was assessed by examining how the best-fitting parameters from the second-pass correlated with the best-fitting parameters from the first-pass ( i . e that based the actual data ) . Overall , the recoverability of parameters of VSE was superior to that of other models ( mean R = 0 . 81 , range: 0 . 67–0 . 95 ) . EV , PVL and ORL also showed good recoverability ( EV , mean = 0 . 76 , range: 0 . 66–0 . 83; PVL: mean = 0 . 79 , range 0 . 51–0 . 94; ORL: mean = 0 . 77 , range = 0 . 65–0 . 89 ) , while PVL-delta and VPP were less stable ( PVL-delta: 0 . 71 , range: 0 . 5–0 . 86; VPP , mean = 0 . 70 , range: 0 . 41–0 . 94 ) . In particular , it must be noted that the parameter φ reflecting the exploration bonus of the VSE had the highest recoverability ( 0 . 95 ) , hence making it a relevant target for the study of inter-individual differences ( Fig 3D ) . In their study ( included in the 504 participants dataset analyzed above ) , Woods and colleagues reported that old and young adults performed equally well on the Iowa Gambling Task but resorted to different strategies . More precisely , old adults appeared to forget more rapidly about outcomes than healthy participants but compensated this forgetting by a better ability to translate what they learned into consistent choice patterns . Thus , we used this subset of the data to evaluate how well the VSE model could capture heterogeneities in IGT strategies and to validate our modeling approach ( Fig 4A ) . In particular , based on the existing literature , we hypothesized that the exploration bonus should be lower in old as compared to young participants . First , we confirmed that old participants indeed forgot more rapidly than young participants according to the VSE model , as indicated by a lower decay parameter ( young: 0 . 55+/-0 . 27; old: 0 . 44+/-0 . 23; z = 2 . 75 , p = 0 . 006 ) . Second , the consistency parameter of old participants was indeed higher than that of young participants ( young: 0 . 75+/-0 . 42; old: 0 . 92+/-0 . 41; z = 2 . 71 , p = 0 . 007 ) . Third and most importantly , young and old participants differed significantly in their φ parameter controlling the intensity of directed exploration in the IGT ( young:0 . 94+:-2 . 14; old: 0 . 54+/-2 . 25; z = 2 . 10 , p = 0 . 036 ) . This latter result paralleled the model-free analysis of SE indexes which also revealed a reduction in directed exploration in the aging group ( pattern frequency: young = 16 . 5+/-14/6% , old = 10 . 5+/-12 . 8%; t ( 151 ) = 2 . 61 , p = 0 . 01; Fig 4B ) Overall , these results demonstrate the ability of the VSE model to capture age-related changes in directed exploration .
In this study , we uncovered a new choice pattern reflecting the presence of directed exploration within the standard version of the IGT . Indeed , the selection of 4 different decks over 4 consecutive trials—a phenomenon captured by the “sequential exploration index”—largely exceeded chance levels in a group composed of 504 participants , especially in the initial phase of the task . This discovery implies that the IGT can be used to study information-seeking behaviors within risky decision-making contexts . In order to better characterize and quantify this cognitive process within single individuals , we developed a new computational model ( VSE , for Value and Sequential Exploration ) able to articulate directed exploration with the motivation to optimize gains and losses , using only 5 parameters . The VSE model outperformed the 5 most prevalent models previously used to fractionate the cognitive processes engaged by the IGT , in terms of log-likelihood ( penalized for model complexity ) , prediction accuracy , as well as model and parameter recovery rates . We further demonstrated the potential of this architecture to capture fine-grained differences in IGT behavior between young and old participants . Last but not least , we published the scripts used to generate our results under the form of a user-friendly Matlab toolbox which shall enable the community of researchers and clinicians relying on the IGT as a routine assessment of risky decision-making to report more informative and detailed results with minimal programming and mathematical skills . In the field of reinforcement-learning , most algorithms are oriented towards normative utility-maximization goals . To do so , they rely heavily upon reward prediction errors , a quantity widely used as a teaching signal enabling step-by-step convergence towards a utility maximum . Yet , likewise all gradient ascent methods , reinforcement-learning algorithms face the risk of reaching only local , rather than global , utility maxima . Directed exploration aims at solving this problem by expanding knowledge about the environment , despite immediate opportunity costs . Thus , directed exploration is particularly valuable when an agent is required to perform numerous decisions within complex or volatile contexts , in which the optimal policy may not be immediately obvious . The IGT is a canonical example of such environment . Indeed , with 4 possible actions leading to highly variable outcomes , the IGT is typically characterized by several successive phases: a “pre-punishment” period during which all decks only produce gains but no losses , a “pre-hunch” period during which punishments start occurring , a “hunch” period during which most healthy participants start feeling that the decks offering the highest average gains actually entails even higher average losses ( i . e . deck A and B ) and a “conceptual” period during which these participants are able to verbalize that the decks offering small gains ( i . e . C and D ) are actually the most advantageous ones [20] . Although the term exploration is often applied to choices which are not maximizing utility with respect to a given learning model , the recent rise of predictive coding has completed this conceptualization [21–23] . Indeed , this framework postulates that uncertainty-minimization constitutes a driving principle of our cognitive system alongside utility-maximization , such that modeling the dynamics of exploratory decisions became an important endeavor in the field . Accordingly , recent studies have investigated uncertainty-minimizing strategies in multi-armed bandit tasks using Bayesian methods . In this formalism , options whose mean value is the least precise ( or , equivalently , associated with the largest variance ) are the best candidates for exploration . The existence of uncertainty-driven exploration was confirmed by some of these studies [9 , 24 , 25] . Yet , the type of directed exploration described here does not involve uncertainty computations . Instead , it relies on a simpler recency approach which promotes the exploration of options which have not been selected for a while , independently of the objective uncertainty bound to their pay-offs . This logics can be justified in three ways . First , despite its mathematical elegance , uncertainty-driven exploration does not provide a fully normative solution to the exploration-exploitation trade-off in multi-armed bandits ( the process being heavily dependent upon higher-order priors regarding the structure of tasks ) . Second , the recency method implemented here might still reflect an uncertainty-based mechanism if the subjective uncertainty associated with a given option increases with the duration elapsed since that option was tested for the last time . Third , the seminal study of Daw and colleagues had shown that uncertainty-driven exploration was not useful to describe exploratory patterns in a 4-armed bandit task sharing many commonalities with the IGT [8] . The computational costs associated with uncertainty-tracking may thus become too high within tasks involving more than 3 options . Accordingly , this number recently appeared as an upper limit on the number of stimulus-response mappings whose reliability can be simultaneously monitored by the human executive system [7 , 26] . The exploration module of our VSE architecture helped going beyond existing models used to account for healthy participants’ decisions in the IGT . Combined with the model-free analysis of the SE index , it expands the heuristic value of the IGT beyond the study of exploitation and reward-seeking behaviors . Moreover , the fact that VSE parameters were on average more recoverable than parameters of previous models will facilitate the interpretation of inter-individual and inter-group differences . Numerous studies which had used the IGT to characterize clinical populations may thus benefit from re-analyzing their data using the toolbox associated with the current paper . Once the trial-by-trial IGT data is converted to the appropriate Matlab format , this toolbox make such re-analysis extremely simple and intuitive , thanks to its compact but informative documentation and its densely commented scripts . With minimal programming knowledge , the six models described hereinabove can be fitted to any standard IGT dataset , compared and evaluated with respect to recoverability and prediction accuracy . These variables as well as other model-free measures ( net scores , sequential exploration indices , choice entropy , etc . ) can also be calculated , plotted and compared across different groups . While our analyses indicate that the IGT can be suitable to study sequential exploration in conjunction with the VSE model , it must however be emphasized that no modeling approach can circumscribe the non-orthogonal relationship of risk and valence inherent to the structure of the task . In particular , the IGT is not well suited to estimate loss aversion , as implemented by the VPP , PVL and PVL-Delta models . Accordingly , our data showed that the loss aversion parameter had very low recovery rates in the PVL-Delta and VPP models ( 0 . 55 and 0 . 48 , respectively ) and likely interfered with the value sensitivity parameter in the PVL model ( recovery rate: 0 . 52 ) . Moreover , adding a loss aversion parameter to the VSE model did not improve model fits ( see S2 Fig for details ) . Researchers specifically interested in loss aversion should thus use other paradigms designed to do so [27] . At the neurobiological level , directed exploration likely depends on the prefrontal cortex ( PFC ) , and more particularly on its rostrolateral portion ( rlPFC ) . Indeed , several neuroimaging studies of directed exploration found that the rlPFC is more active during exploratory decisions ( Badre et al . , 2012; Boorman et al . , 2009; Daw et al . , 2006 ) . Brain stimulation studies further showed that disrupting or facilitating rlPFC activity can significantly diminish or increase directed exploration , respectively [9 , 28] . Importantly , disrupting rlPFC activity using continuous theta burst TMS similarly lowered directed exploration and exploration bonuses as assessed by the VSE model in the IGT [29] . This involvement of the rlPFC might also explain the decrease in directed exploration seen in aging individuals , as grey matter density in this area is significantly reduced in old as compared to young adults [30] . Yet , other neural systems certainly interact with the rlPFC to orchestrate information-seeking in reinforcement-learning tasks , including the dmPFC which may control the switch from exploitation to exploration [31] . The prefrontal turn-over of dopamine might also play a pivotal role in regulating directed exploration [32 , 33] , whereas noradrenaline might be involved in the control of random but not directed exploration [34] . In order to further validate our model and illustrate the utility of VSE for the analysis of group differences , we investigated how aging influenced its parameters and more particularly the exploration bonus parameter . The results of this analysis were well aligned with those reported in the study of Wood and colleagues [15] , in that the VSE model still evidenced the exacerbated forgetting of previous outcomes in older adults , as well as the reduction in random exploration ( i . e increased choice consistency ) thought to compensate faster forgetting rates in these participants . More importantly , old adults also displayed a lower exploration bonus than young adults . This effect paralleled the reduction in directed exploration observed when computing directly the frequency of choosing 4 different decks over 4 consecutive trials ( SE index ) . It is also highly consistent with recent papers showing that directed exploration reduces across lifespan [16 , 17] . Since directed exploration requires the retention of the last few choices made in the task , the phenomenon may be related to the decline of working memory performances sometimes observed in aging cohorts [35] . Beyond the study of cognitive aging , the good recoverability of the VSE model itself and the excellent recoverability of its free parameters constitute two useful features with respect to the development of computational psychiatry . Indeed , the Variational Bayes approach adopted here can be readily combined with the advanced clustering techniques underlying this growing field of research ( https://mbb-team . github . io/VBA-toolbox/ ) , which aims at redefining the dimensionality of behavioral impairments across clinical labels in the hope of promoting drug discovery and personalized medicine [36] . To which extent the decomposition of IGT-related behaviors will contribute to this effort remain uncertain , but it has a high potential which could be realized if more clinical teams subscribe to the open-science philosophy by sharing their raw data . Following the initiative of Ahn and colleagues who provided data of stimulant and opiate users [37] , addiction research appears as a timely candidate: indeed , large datasets exist for alcohol use [38 , 39] , cannabis use [40] , as well as for behavioral addictions such as gambling and eating disorders [41 , 42] . To conclude , our study leveraged the power of an open “many labs” dataset in order to demonstrate the existence—and characterize the influence—of an overlooked behavior in the IGT . Building on previous work and more particularly on the Prospect Valence Learning ( PVL ) model [43] , the VSE architecture represents not only a quantitative but also a qualitative improvement upon alternative models by shedding light on directed exploration . Besides enabling any experimenter to fit the VSE and its ancestors ( EV , PVL , PVL-Delta , VPP , ORL ) on IGT data , the toolbox accompanying this paper might be used as an environment to develop even better models in the future . It must be acknowledged that this tool relies heavily on two other open-source packages for Matlab: modeling analyses largely depend on the VBA toolbox by Daunizeau and colleagues [18] whereas visualizations take advantage on the Gramm toolbox by Morel [44] . Last but not least , this study is fully aligned with the ideals of reproducibility and transparency in science: the dataset used is both large and freely available , while the scripts used to generate figures and statistics are available online alongside a clear documentation ( https://github . com/romainligneul/igt-toolbox ) .
The dataset comes from a ‘many labs’ initiative grouping 10 studies and containing data from 617 healthy participants [13] . Here , we restricted the analysis to the subset of 7 studies which used the classical 100 trials version of the IGT , resulting in 504 participants ( age range: 18–88 years; for the 5 studies with available information about sex: 54% of females ) . Within this dataset , 153 participants come from a single study on aging [15] . Among these participants , 63 are older adults ( 61–88 years old; 17 males ) and 90 are younger adults ( 18–35 years old; 22 males ) matched in terms of education level and intelligence ( WASI vocabulary ) . In order to quantify directed exploration in the IGT , we computed the probability of choosing the 4 different decks during series of 4 consecutive trials . We refer to the frequency of such choice pattern as “SE index” . We used this metrics because the occurrence of such events has a probability of only 9 . 38% under purely random exploration ( note that exploitation makes this probability even smaller by introducing an imbalance in the choice probability of different decks ) . Although directed exploration is certainty governed by more complex heuristics ( resulting in more complex choice patterns ) , this index was used to ascertain its presence and provide an estimation of its intensity . Inferences about the presence of sequential exploration used independent quadruplets of successive trials ( i . e: 1–4 , 4–8 , etc . ) , whereas inferences about interindividual differences used dependent quadruplets to maximize sensitivity ( i . e: 1–4 , 2–5 , 3–6 , etc . ) . Four previous models have been exhaustively and excellently described in a previous publication by Steingroever and colleagues [45] . The ORL model is described in Haines et al . [6] . Therefore , we will only provide a brief overview of their characteristics and then focus mainly on describing the features of the new VSE model . All the models described above have in addition a consistency parameter determining to which extend choices are driven by learned values ( or any type ) . This consistency parameter c is allowed to fluctuate in the [0 , 5] interval and is transformed before being used as an inverse temperature parameter β ( β = 3c-1 ) , except for the EV model where c is allowed to fluctuate in the [–2 , 2] interval and is transformed differently ( β = ( t/10 ) c with t corresponding to current trial number ) . In sum , the EV model has 3 parameters , the PVL and PVL-delta models have 4 parameters , the VPP model has 8 parameters and the ORL has 5 parameters . Note that the consistency parameter capture the opposite of “random exploration” ( i . e . decision temperature ) . A validated toolbox ( http://mbb-team . github . io/VBA-toolbox ) was used to optimize model parameters [18] . This toolbox relies on a Variational Bayesian ( VB ) scheme . Compared to non-Bayesian methods , this approach has the advantage of accounting for the uncertainty related to estimated model parameters and of informing the optimization algorithm about prior distributions of parameters’ values . All priors were natively defined as Gaussian distributions of mean 0 and variance 3 , which approximates the uniform distribution over the [0–1] interval after a sigmoid transformation . Depending on the range of values in which each parameter was allowed to vary , the sigmoid-transformed parameters were further stretched or shifted to cover different intervals while preserving the flatness of their prior distribution ( e . g . “multiplied by 2 , minus 1” , to obtain the interval [–1 , 1] ) . Model comparison results were replicated using a non-Bayesian model fitting procedure which relied on the standard fminunc function of Matlab ( line-search algorithm ) . All hidden states ( i . e values ) were initialized at 0 except for exploration weights which were initialized at 1 ( since no deck has been sampled at the beginning of the task ) . The VB algorithm was not allowed to update the initial values for hidden states . Comparison of VSE model with the 5 alternatives was first based on a classical fixed-effect analysis comparing summed Bayesian Information Criterion ( BIC ) , Akaike Information Criterion ( AIC ) and Free energy ( F ) metrics over the whole group . In this approach , it is classically considered that a difference of 10 units between the models with the lowest and the second lowest criterion value reflects very strong evidence in favor of the model with lowest value ( corresponding to a Bayes Factor of 150 ) . Then , a Bayesian Group Comparison was performed which treated model attribution as a random-effect varying from subject to subject . Also based on BIC , AIC and F , this type of analysis produces an exceedance probability corresponding to the probability that a given model is more likely than any other candidate model ( Stephan et al . , 2009 ) . There is a growing consensus among computational neuroscientists that evaluating models only based on estimators such as the AIC or BIC is not sufficient [14 , 47] . The problem is particularly salient when one aims at drawing inferences about cognitive processes from estimated parameters ( which is most often the case ) , because the same choice pattern can sometimes be explained by very different combinations of parameters and because models associated with lower information losses do not always better reproduce qualitative choice patterns . To address these issues and ensure that the VSE model performed equivalently or better than the VPP model in this respect , we performed the simulation and parameter recovery analyses detailed below . We used the best-fitting parameters of each subject to simulate an artificial decision-maker confronted to the IGT . Simulated choices were generated stochastically according to the consistency parameter , and feedbacks ( gains/losses ) were drawn from the distributions of feedbacks actually encountered by the participants . Then , we reran model estimations based on these simulated choices , which resulted in a new set of parameters . The quality of parameter recovery for the VSE and VPP models could then be assessed by examining the correlation of this second set of parameters with the parameters initially obtained by fitting real choices . We examined to which extent the initial choices predicted by the model and the choices performed by the simulated participants matched the actual choices of the participants , across models . In this latter analysis , we restricted our statistical inference and compare the VSE model with the second-best fitting model only . The model recovery analysis consisted in: ( i ) using each model to simulate 504 series of 100 trials using the parameters distribution obtained after fitting the model on the real dataset; ( ii ) fitting the 6 candidate models on each of these 6 simulated datasets , hence requiring in 18144 individual fits; ( iii ) performing a separate model comparison for each of the 6 simulated datasets . | The ability to perform decisions and learn from their outcomes is a fundamental function of the central nervous system . In order to maintain their homeostasis and maximize their biological fitness , organisms must maximize rewards and minimize punishments . Yet , pure exploitation often leads to suboptimal solutions . In order to discover the best course of action , organisms must also explore their environment , especially when this environment is complex or volatile . Here , we dissected exploratory strategies in one of the most classic decision-making paradigms of cognitive neuroscience . First , we found that humans tend to sample sequentially the space of possible actions . Second , we developed a new mathematical model better able to predict trial-by-trial choices , by articulating this sequential exploration mechanism with random exploration and exploitation . Third , we showed that sequential exploration reduces across lifespan , a result which might be explained by specific neuroanatomical or neurochemical changes associated with normal aging . Together , these findings may contribute to a better understanding of exploratory behaviors and a better assessment of their disruption in a wide range of neuropsychiatric conditions . | [
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"psychology"... | 2019 | Sequential exploration in the Iowa gambling task: Validation of a new computational model in a large dataset of young and old healthy participants |
FNR is a well-studied global regulator of anaerobiosis , which is widely conserved across bacteria . Despite the importance of FNR and anaerobiosis in microbial lifestyles , the factors that influence its function on a genome-wide scale are poorly understood . Here , we report a functional genomic analysis of FNR action . We find that FNR occupancy at many target sites is strongly influenced by nucleoid-associated proteins ( NAPs ) that restrict access to many FNR binding sites . At a genome-wide level , only a subset of predicted FNR binding sites were bound under anaerobic fermentative conditions and many appeared to be masked by the NAPs H-NS , IHF and Fis . Similar assays in cells lacking H-NS and its paralog StpA showed increased FNR occupancy at sites bound by H-NS in WT strains , indicating that large regions of the genome are not readily accessible for FNR binding . Genome accessibility may also explain our finding that genome-wide FNR occupancy did not correlate with the match to consensus at binding sites , suggesting that significant variation in ChIP signal was attributable to cross-linking or immunoprecipitation efficiency rather than differences in binding affinities for FNR sites . Correlation of FNR ChIP-seq peaks with transcriptomic data showed that less than half of the FNR-regulated operons could be attributed to direct FNR binding . Conversely , FNR bound some promoters without regulating expression presumably requiring changes in activity of condition-specific transcription factors . Such combinatorial regulation may allow Escherichia coli to respond rapidly to environmental changes and confer an ecological advantage in the anaerobic but nutrient-fluctuating environment of the mammalian gut .
Regulation of transcription initiation by transcription factors ( TFs ) is a key step in controlling gene expression in all domains of life . Genome-wide studies are revealing important features of the complexity of transcription regulation in cells not always apparent from in vitro studies . In eukaryotes , both the inhibition of TF binding by chromatin structure and the combinatorial action of multiple TFs contribute to the genome-wide pattern of TF binding and function [1]–[5] . In contrast , our knowledge of transcriptional regulation by bacterial TFs stems largely from elegant in vitro experiments that have provided atomic resolution views of TF function [6] . Much less is known about how chromosome structure and combinatorial action affect bacterial TF binding and transcriptional regulation on a genome-wide scale [7] . Previous studies have suggested that , in contrast to the chromatin-restricted TF binding in eukaryotes , the Escherichia coli genome is permissive to TF binding because the occupancy pattern for some TFs correlates well with match to consensus sequence and consequent binding affinity [8]–[10] . Other studies suggest that nucleoid-associated proteins ( NAPs; for example H-NS , Hu , Fis , and IHF ) organize the chromosome into discrete domains and structures that may affect transcriptional regulation [7] , [11]–[13] , but possible global effects of NAPs on TF-binding have not been systematically tested . To investigate the roles of TF action and chromosome structure in a prototypical bacterial regulon , we studied the regulon of the anaerobic TF FNR . FNR is widely conserved throughout the bacterial domain , where it evolved to allow facultative anaerobes to adjust to O2 deprivation [14] . Under anaerobic conditions , E . coli FNR contains one [4Fe-4S] cluster per subunit , which promotes a conformation necessary for FNR dimerization , site-specific DNA binding , and transcription regulation [15] , [16] . Genome-wide transcription profiling experiments [17]–[19] established that E . coli FNR controls expression of a large number of genes under anaerobic growth conditions , in particular those genes whose products function in anaerobic energy metabolism . However , corresponding studies to establish which promoters are directly or indirectly regulated by FNR under comparable growth conditions have yet to be reported . Studies of the regulatory regions of a few FNR controlled promoters have provided key insights into the mechanism of transcriptional regulation by FNR and the characteristics of FNR binding sites [20] , [21] . From these studies we know that FNR binding sites can have only a partial match to the consensus sequence of TTGATnnnnATCAA , and be located at variable positions within promoter regions , directing whether FNR has either a positive or negative affect on transcription . At FNR repressed promoters , FNR binding site locations range from upstream of the −35 hexamer ( which binds region 4 . 2 of RNA polymerase σ70 ) , to overlapping the transcription start site ( TSS; +1 ) . At most FNR activated promoters , the center of the binding site is ∼41 . 5 nt upstream of the TSS , placing FNR in position to interact with both the σ70 and α subunits of RNA polymerase ( RNAP ) [21] , [22] . Very few promoters are known to have FNR binding sites centered at −61 . 5 or greater , a position dependent typically on interactions with only the α subunit of RNAP [21] . The predominance of FNR binding sites positioned at −41 . 5 nt may reflect a preference for a particular activation mechanism , but it also could reflect sample bias in the limited number of activated promoters that have been studied to date . Thus , current knowledge is insufficient to allow accurate prediction of FNR binding sites genome-wide . Many FNR regulated promoters are controlled by multiple TFs ( for example CRP , NarL , NarP , and NAPs [7] , [20] , [21] ) , which can have either positive or negative effects on FNR function depending on the promoter architecture . For example , the narG promoter is activated by FNR , IHF , and the nitrate-responsive regulator , NarL [23] , [24]; in contrast , the dmsA promoter is activated by FNR , but repressed by NarL [25] , [26] . At the nir promoter , NarL displaces IHF to overcome a repressive effect of IHF and Fis , and thereby enhances FNR-dependent transcription [27] . Thus , in the presence of the anaerobic electron acceptor nitrate , FNR function can be either enhanced or repressed by NarL depending on the organization of TF-binding sites within the promoter region . In this way , the requirement of additional TFs for combinatorial regulation of promoters bound by FNR resembles transcriptional regulation in eukaryotes [28] . Such complex regulatory patterns cannot currently be inferred simply by identifying the locations of TF binding sites or by the strength of the FNR binding site . Direct measure of occupancy at these sites by each TF and correlation with the resulting transcripts in different growth conditions is needed to understand how complex bacterial regulatory networks coordinate gene expression . As an important first step , Grainger et al . used chromatin immunoprecipitation followed by microarray hybridization ( ChIP-chip ) to examine FNR occupancy using a FLAG-tagged FNR protein in E . coli cultures grown anaerobically in a rich medium [29] . Although many new FNR binding sites were identified , these data were not obtained from cells grown in the growth media used for reported transcriptomic experiments [17]–[19] and thus the datasets cannot readily be compared . To systematically investigate FNR binding genome-wide , we performed chromatin immunoprecipitation followed by microarray hybridization ( ChIP-chip ) and high-throughput sequencing ( ChIP-seq ) for WT FNR from E . coli grown anaerobically in a glucose minimal medium ( GMM ) . Computational and bioinformatic analyses were used to refine a FNR position weight matrix ( PWM ) . The PWM was used to determine the relationship between ChIP-seq/ChIP-chip enrichment and match to the PWM , and to identify predicted FNR binding sites not detected by ChIP-seq . To examine the subset of high-quality predicted FNR binding sites that lacked a FNR ChIP-seq peak , we obtained and analyzed aerobic and/or anaerobic ChIP-chip data for NAPs H-NS and IHF along with analysis of previously published aerobic ChIP-seq data for the NAP Fis [30] to determine if NAP occupancy might prevent FNR binding . Further , the effect of H-NS on FNR occupancy was examined directly using ChIP-chip analysis of FNR as well as on O2 dependent changes in expression in the absence of H-NS and its paralog StpA . After identifying FNR binding sites genome-wide , we performed whole genome transcription profiling experiments using expression microarrays and high-throughput RNA sequencing ( RNA-seq ) to compare a WT and Δfnr strain grown in the same medium used for the DNA binding studies . The transcriptional impact of FNR binding genome-wide was investigated by correlating the occupancy data with the transcriptomic data to determine which binding events led to changes in transcription , to identify the direct and indirect regulons of FNR , and to define categories of FNR regulatory mechanisms . Finally , the aerobic and anaerobic ChIP-chip and ChIP-seq distributions of the σ70 and ß subunits of RNAP throughout the genome were analyzed to determine the role of O2 and FNR regulation on RNAP occupancy and transcription .
A small number of FNR peaks showed a large degree of variation in peak height across the genome . Previous studies of the repressor LexA reported that ChIP-chip peak height correlated with the match to the consensus sequence [10] , suggesting that differences in site occupancy may reflect relative binding affinities to individual sites . Because FNR is a global regulator with a more degenerate binding site than LexA , we tested whether we could use this parameter to gain additional information about FNR binding-site preferences . A PWM ( Figure 2 Inset ) was constructed from an alignment of sequences from the ChIP-seq peaks and the scores representing the match to the PWM were determined with the algorithm PatSer ( Table S5 ) [36] . In contrast to the studies of LexA [10] , we found a poor correlation between the height of the FNR ChIP-seq peak and the match to the FNR PWM for the site predicted within each peak ( Figure 3A ) . The same lack of correlation was also observed with FNR ChIP-chip data , indicating that this was not specific to the detection method . Additionally , there was a lack of correlation between FNR peak height and the number of known FNR binding sites . Furthermore , the majority of the FNR ChIP-seq or ChIP-chip peaks had similar heights , regardless of the score of the FNR motif present ( Figure 3A ) . One explanation for this latter result is that most FNR binding sites were saturated for binding in vivo . To examine this possibility directly , we performed ChIP-chip experiments over a range of cellular FNR dimer concentrations below the normal anaerobic cellular level of ∼2 . 5 µM [37] , controlled by varying IPTG levels in a strain with fnr fused to an IPTG-inducible promoter . Peak areas for 35 selected FNR sites , representing a distribution of peak heights , were quantified for several cellular FNR dimer concentrations ( ∼0 . 45 , ∼0 . 7 , ∼1 . 9 , and ∼2 . 5 µM ) . These plots showed a typical binding saturation curve for both novel and previously identified FNR binding sites , and revealed that all sites examined were saturated for binding at the normal cellular FNR dimer level of ∼2 . 5 µM ( Figure 3B , Figure S1 ) . However , because the broad distribution of peak heights between different sites was still observed , despite the fact that the sites were maximally occupied , we concluded that variation in peak height was not related to strength of FNR binding ( Figure 3 , Figure S1 ) . As a control , we tested four FNR peaks that were determined to be non-specific due to enrichment in a Δfnr control ChIP-chip experiment and these peaks showed no change in peak height when FNR levels were varied ( Figure S1 ) . Thus , we conclude that differences in peak height in the ChIP-seq and ChIP-chip experiments for FNR were most likely due to differences in cross-linking efficiency or immunoprecipitation at particular genomic locations and not to differences in FNR binding affinity . A well-known challenge in genomic studies is the use of computational tools to accurately predict DNA binding sites , particularly for global regulators like FNR that have degenerate binding sites . To investigate the usefulness of the PWM generated from our set of ChIP binding sites for predicting FNR sites genome-wide , we initially used a PatSer [36] threshold low enough that a FNR motif was identified in each FNR ChIP-seq peak . However , this threshold resulted in >10 , 000 possible genomic FNR binding sites . In contrast , if we used a precision-recall ( PR ) curve [38] to determine the optimal threshold to predict FNR binding sites ( ln ( p-value ) of −10 . 75 ) , then we obtained a more reasonable number ( 187 ) of predicted FNR binding sites ( Figure 2 , Table S6 ) . Surprisingly , fewer than half of these sites ( 63 of 187 ) corresponded with a FNR ChIP-seq peak ( Table S6 ) , despite the fact that some predicted sites without a corresponding ChIP-seq peak had higher quality PatSer scores than those with a ChIP-seq peak . Although it is possible that some of the predicted sites without a ChIP-seq peak contain flanking sequence elements that disfavor FNR binding , we considered the possibility that many are functional sites but either FNR binding was masked by other DNA binding proteins or FNR cross-linking failed for other reasons . NAPs are known to affect the binding of some TFs in E . coli [7] , [12] . To ask if the NAPs H-NS , IHF , or Fis might occlude the 124 predicted FNR binding sites lacking a FNR ChIP-seq peak , we analyzed ChIP-chip data for H-NS and IHF , obtained from the same growth conditions , and publicly available ChIP-seq data for Fis [30] . Nearly all of these FNR sites ( 111 of 124 sites; ∼90%; silent FNR sites ) were enriched in IHF , H-NS , or Fis , consistent with the idea that these NAPs occupy the silent FNR sites and thereby block FNR binding ( Table S6 , Figure S2 ) . Similar occupancy was observed when the 124 predicted FNR sites were compared with H-NS and IHF enrichment from published ChIP-chip and ChIP-seq data performed under different growth conditions [30] , [31] , [33] . In comparison , only ∼20% ( 14 of 63 sites ) of the FNR sites that coincided with a FNR ChIP-seq peak were enriched in a NAP ChIP signal , significantly less than NAP occupancy at FNR sites lacking a peak ( p-value<0 . 05 ) . In contrast , we found ∼50% of the previously identified LexA binding sites [10] were co-occupied with H-NS . We conclude that the NAPs H-NS , IHF , or Fis likely prevent FNR binding at some sites by occlusion . We also examined whether the silent FNR sites are preferentially occluded by the extended H-NS binding regions . The extended binding regions of H-NS ( >1 kb ) likely represent H-NS filaments that are known to cover multiple kb of DNA and silence transcription [7] , [12] , [13] , [30] , [34] . Consistent with this notion , our results showed that the extended H-NS binding regions were negatively correlated with RNAP ( ß ) ChIP-chip occupancy and this silencing occurred in both the presence and absence of O2 ( p-value<0 . 05 ) ( Figure S3 ) . In contrast , shorter H-NS enriched regions ( <1 kb ) were both positively and negatively correlated with RNAP ChIP-chip occupancy under aerobic and anaerobic growth conditions . The 46 silent FNR sites bound by H-NS were more likely to be occupied by extended H-NS binding regions ( 42 sites ) than by short H-NS binding regions ( 4 sites ) ( p-value<0 . 05; example in Figure S3C ) , suggesting that extended H-NS binding regions may inhibit FNR binding at silent FNR sites . To investigate the impact of H-NS binding on FNR occupancy , we characterized FNR ChIP-chip peaks in a strain deleted for both hns and stpA; stpA encodes a H-NS paralog that partially compensates for H-NS in a Δhns mutant [39] , [40] . Many new FNR peaks ( 196 ) appeared in the Δhns/ΔstpA strain ( Figure 1 , Figure 4A–C , Table S7 ) , and a large fraction ( 81%; 158 FNR peaks ) of these new peaks corresponded to H-NS binding regions in the WT strains , indicating that FNR binding was unmasked in the absence of H-NS and StpA . The distribution of the FNR PWM scores of the FNR sites found within the FNR ChIP-chip peaks unmasked by the absence of H-NS and StpA was similar to that found in the WT strain ( Figure 4C , Tables S6 and S7 ) . The majority ( 78 of 99 ) of silent FNR sites lacking FNR peaks in the Δhns/ΔstpA strain were enriched for IHF and/or Fis , suggesting that these NAPs still occluded FNR binding in the absence of H-NS and StpA ( Table S6 ) . Taken together , these results establish that removal of H-NS and StpA allowed FNR to bind to sites covered by H-NS in WT strains . Nearly all FNR peaks found in the WT strain were retained in the Δhns/ΔstpA mutant ( 163 of 169 peaks; Figure 4A , Table S7 ) , but a small proportion ( ∼15% ) showed a significant increase in peak average ( average log2 ( IP/INPUT ) value of the binding region ) in the Δhns/ΔstpA strain ( Figure 4D ) . The majority of these FNR peaks with increased peak averages were also bound by H-NS in the WT strain , suggesting that removing H-NS allowed for increased cross-linking or immunoprecipitation of FNR at these loci likely due to changes in chromosomal structure in the absence of H-NS and StpA [35] . In contrast , removing H-NS did not affect FNR occupancy or cross-linking at locations lacking H-NS ChIP signal in WT strains . We conclude that H-NS reduces or blocks FNR binding at many locations in vivo . To determine which FNR binding events from the WT strain caused a change in gene expression , the FNR occupancy data were correlated with the 122 operons differentially expressed ( DE ) by FNR ( Table S8 ) . Surprisingly , less than a half of the 122 operons were correlated with a FNR ChIP-seq peak while less than a fourth of the 207 FNR ChIP-seq peaks were correlated with a FNR-dependent change in expression ( Figure S4 ) . To address this unexpected result , we systematically analyzed the regulation of all of these operons by incorporating published data and classified the operons into seven regulatory categories ( Figure 5 ) . Category 1 ( Table 1 ) contained operons that were directly activated by FNR because they showed a FNR-dependent increase in anaerobic transcript levels and a FNR ChIP-seq peak within 500 nt of the translation start site of the first gene of an operon . Category 2 ( Table 1 ) contained operons that were directly repressed by FNR ( showed a FNR-dependent decrease in expression and had a FNR ChIP-seq peak ) . Categories 3–5 contained a surprisingly large number of operons ( 156 ) with a FNR ChIP-seq peak within 500 nt of the translation start site of the first gene of an operon but no FNR-dependent change in expression . Previously published studies ( 23 operons ) and our additional collation of other relevant TF-binding sites ( 52 operons ) suggest that at least half ( 75 ) of these sites may be directly regulated by FNR under alternative growth conditions ( Table S9 ) . For example , Category 3 ( Tables 2 and 3 ) contained operons known or proposed to be co-regulated by FNR and another TF under growth conditions not used in our study . Category 4 ( Table 4 ) contained operons known to be repressed by another TF under our growth conditions . Category 5 ( Table S9 ) contained operons with other potential regulatory mechanisms . Category 6 ( Table 5 , Table S10 ) contained operons that were indirectly regulated by FNR because no FNR ChIP-seq peak was found within 500 nt of the translation start site despite showing a FNR-dependent change in expression . Finally , Category 7 ( Table S11 ) contained operons with a FNR peak identified only in the Δhns/ΔstpA strain , which also showed potential FNR regulation in the absence of H-NS and StpA . The 32 operons directly activated by FNR ( Table 1 ) contain some of the best-studied FNR regulated operons . In addition to operons associated with anaerobic respiration ( dmsABC , frdABCD , nrfABCDEFG , narGHJI ) [41]–[43] , this category included glycolytic ( pykA ) and fermentative enzymes ( pflB and ackA ) , which would be expected to promote mixed acid fermentation of glucose to ethanol , acetate , formate and succinate in the absence of an added electron acceptor ( Figure 6 ) , the conditions used in this study . As expected , we also found that these promoters showed an increase in σ70 occupancy , as illustrated by representative FNR and σ70 data for FNR activation of dmsABC ( Figure 7 ) , providing a proof-of-principle for our approach . While expression of many operons in this category was known to be FNR regulated , only about half had been shown to directly bind FNR ( Table 1 ) . FNR also directly activated operons with functions that illustrate the broader role of FNR in anaerobic metabolism: pepE , a peptidase , suggesting peptide degradation in E . coli similar to that observed in Salmonella [44]; ynjE , an enzyme involved in biosynthesis of molybdopterin , a cofactor used by anaerobic respiratory enzymes [45]; pyrD , a dihydroorotate dehydrogenase in pyrimidine biosynthesis [46]; and ynfK , a predicted dethiobiotin synthetase and paralog of BioD of the biotin synthesis pathway . The activation of the biofilm TF bssR by FNR suggests a link between biofilm formation and anaerobiosis ( Table 1 ) . FNR directly activated the carnitine-sensing TF CaiF , confirming a link between FNR and carnitine metabolism [29] , [47] . In addition , the FNR-enriched region found upstream of fnrS supports FNR direct transcription activation of this small regulatory RNA [48] , [49] , although the fnrS sRNA was not represented in our gene expression arrays and was too small to be detected by our RNA-seq protocol ( Table 1 ) . To determine the position of FNR binding sites relative to the TSS , we used the FNR PWM ( Figure 2 Inset ) to search the FNR enriched regions using a PatSer score threshold low enough to identify FNR sites from every ChIP peak [36] . A majority ( 89% ) of the FNR ChIP-seq peaks in the FNR direct regulon contained one FNR binding site ( Table 1 ) . Of the 23 promoters directly activated by FNR with a known TSS , 19 FNR sites were centered at −41 . 5 ( ±4 nt ) , the known position of a Class II site , while one site was centered at −60 . 5 ( Class I site ) ( Table 1 ) , supporting previous results suggesting a bias toward FNR binding Class II sites in activated promoters . Analysis of the 21 operons directly repressed by FNR revealed both simple and complex repression mechanisms ( Table 1 ) . The majority of the operons directly repressed by FNR showed expression patterns similar to that of ndh , encoding the aerobic NADH dehydrogenase II , which showed a FNR-dependent decrease in expression and decrease in σ70 occupancy under anaerobic growth conditions ( Figure 7 ) . These operons included nrdAB , the aerobic ribonucleotide reductase; hisLGDC , a subset of the histidine biosynthesis enzymes; fbaB , the class I fructose-1 , 6-bisphosphate aldolase involved in gluconeogenesis; and can , the carbonic anhydrase . FNR also repressed iraP , which encodes the anti-adaptor protein that stabilizes σS , and rmf , which encodes the stationary phase inducible ribosome modulation factor . In contrast , a subset of operons showed complex repression similar to cydAB , with an anaerobic dependent increase in expression despite the fact that anaerobic expression increased further in a strain lacking FNR , indicating partial repression ( Table 1 ) [50] . Nearly all of these operons are also co-regulated by ArcA ( Park and Kiley , Personal Communication ) suggesting that , like cydAB , FNR and ArcA co-regulation could lead to maximal expression of these genes under microaerobic conditions [50] . These operons include hdeD , gadE and hdeAB-yhiD , involved in acid stress response , and ompC and ompW , encoding outer membrane proteins . The finding that strains lacking ompC , rmf , and rpoS show decreased viability compared to single or double mutants [51] suggests that these proteins may function in a common stress response , potentially necessary under microaerobic growth conditions . Interestingly , for the 16 promoters directly repressed by FNR with a known TSS , the FNR binding sites were broadly distributed , ranging from −125 . 5 to overlapping the +1 ( Table 1 ) . In sum , these results indicate the surprising finding that FNR directly represses a broad set of functions , including some stress responses , expanding the role of FNR beyond simply repressing genes associated with aerobic respiration . Finally , comparison of the transcriptomic data to changes in σ70 holo-RNAP ChIP-seq occupancy under aerobic and anaerobic growth conditions revealed that nearly all FNR-regulated operons are expressed using σ70 RNAP . Increases or decreases in σ70 enrichment under anaerobic conditions correlated well , for the most part , with the expression changes for promoters activated or repressed by FNR , respectively , as well as expression changes in anaerobic and aerobic WT cultures ( Table 1 , Tables S2 and S12 ) . Three operons , which lacked σ70 enrichment , have been shown to be dependent on σE ( hcp-hcr ) [52] , σN ( hycABCDEFGHI ) [53] and σS ( fbaB ) [54] , raising the possibility that alternative σ factors transcribe a subset of the FNR direct regulon . Comparison of our FNR data with published regulatory data suggested that many FNR regulated operons were co-activated by TFs not active during growth in GMM , specifically NarL , NarP and CRP . For example , FNR-dependent transcription of napFDAGHBC , encoding the periplasmic nitrate reductase , requires co-activation by the NO3−/NO2− sensing response regulator NarP [55] . Transcriptomic data [19] showed FNR and NarL or NarP dependent activation in the presence of NO3− and/or NO2− ( Table 2 ) [19] for nine operons that we found associated with FNR ChIP-seq peaks but lacking a FNR-dependent change in expression in our transcriptomic experiments , suggesting co-activation by NarL or NarP when NO3− and/or NO2− is present . Another possible co-activator of operons in this group is CRP , which is inactive under glucose fermentation conditions presumably because of decreased cAMP [56] . Although previous studies have shown that ansB is co-activated by FNR and CRP [57] , we did not observe binding of FNR upstream of ansB in this study , potentially due to differences in growth conditions . Nevertheless , 12 operons within this group showed an increase in anaerobic expression in transcriptomic data obtained from WT strains grown with carbon sources other than glucose ( e . g . glycerol , mannose , arabinose or xylose ) compared to growth in glucose ( Table 3 ) [19] , [58] ( Park and Kiley , Personal Communication ) . A majority ( nine ) contained distinct CRP and FNR binding sites , suggesting co-activation by FNR and CRP when glucose is absent and cAMP levels are increased ( Table 3 ) . Interestingly , for the other three of these operons , guaB , ptsH and uxaB , the identified FNR binding site overlapped the CRP binding site , suggesting potential competition between FNR and CRP for binding when both TFs are active ( Table 3 ) . We propose that FNR activation of ten operons is repressed by Fur under the iron replete conditions used here , similar to the known regulation of feoABC , encoding a ferrous iron uptake transporter [59] . In addition to feoABC , nine additional operons known to be bound by Fur had a FNR ChIP-seq peak but lacked a FNR-dependent change in expression , suggesting that Fur repression masked FNR regulation of these operons ( Table 4 ) . Expression of several of the remaining operons associated with FNR ChIP-seq peaks are known to require other TFs but were not known to be co-regulated by FNR , potentially explaining the lack of FNR-dependent regulation under our growth conditions . A subset of these FNR-regulated operons may be co-regulated by OxyR ( active under oxidative stress ) , CadC ( active at low external pH ) or PhoP ( active in low Mg2+ concentration ) ( Table S9 ) . In a recent SELEX study [60] , three BasR binding sites were identified upstream of operons containing FNR peaks but without a FNR-dependent change in expression , suggesting BasR could possibly influence FNR regulation at these three promoters ( Table S9 ) . In some cases , promoter architecture may mask FNR regulation . A small number of operons ( 12 ) contained multiple TSSs , raising the possibility that FNR may regulate transcription from a TSS that does not increase the total transcript levels to above the cutoff used in our analyses ( Table S9 ) . Alternative σ factors , active under other growth conditions , may also play a role in regulating transcription of a subset of these operons ( Table S9 ) . Taken together , we conclude that although FNR serves as a global signal for anaerobiosis , many operons likely require the combinatorial integration of TFs sensing other environmental signals for expression . Surprisingly , a large number of operons ( 70 ) were differentially expressed by FNR but were not associated with a FNR ChIP-seq peak , suggesting they are regulated by FNR indirectly ( Category 6 , Table S10 ) . To determine whether any of these operons had a FNR site upstream that was missed by ChIP-seq , sequences 500 nt upstream of these operons were searched using the FNR PWM and the algorithm PatSer with the PR curve determined threshold ( Figure 2 ) [36] . Only one operon , hmp , contained a predicted FNR-binding site and previous data also supported FNR binding to hmp [61] . Thus , 69 operons are indirectly regulated by FNR . The indirect regulation by FNR could be easily explained for 11 operons targeted by the small RNA FnrS , which is directly activated by FNR [48] , [49] . These RNAs increased in the FNR− strain because of the lower FnrS levels ( Table 5 ) [48] , [49] . To determine whether FNR binding to sites unmasked by the absence of H-NS and StpA caused a change in expression , we assayed if any of the corresponding genes were differentially expressed by O2 only in the Δhns/ΔstpA strain . Of the 158 new FNR peaks unmasked in the Δhns/ΔstpA strain , 18 genes showed an anaerobic increase in expression ( Table S11 ) , and consistent with this , many of the promoters contained a FNR binding site at a position associated with activation ( e . g . near −41 . 5 ) . For example , hemolysin E ( hlyE ) , in agreement with previous results [62] , and the anaerobic NAP Dan ( ttdR ) [63] showed increased expression under anaerobic conditions only . This suggests a possible role of Dan in the absence of H-NS and StpA . Only two genes showed a decrease in expression in the absence of H-NS and StpA ( yncD and feaR ) under only anaerobic growth conditions . However , the expression of the vast majority of genes having FNR bound at unmasked sites resulted in changes under both aerobic and anaerobic growth conditions , indicating that changes in nucleoid structure that occur in the absence of H-NS and StpA could cause misregulation of transcription . For example , H-NS and Rho coordinate to regulate transcriptional termination and the absence of H-NS may cause increased transcriptional readthrough of Rho-dependent terminators [64] . Thus , it seems likely that our analysis provides an underestimate of the impact of H-NS on FNR function , since physiological conditions that alter H-NS activity are likely to have less severe effects on nucleoid structure .
The finding that there was little relationship between peak height and the quality of the FNR motif differs from the results found for LexA , which showed a correlation between peak height and match to consensus [10] . Our data suggest that FNR peak height may be more related to the efficiency of cross-linking or immunoprecipitation since sites that appear to be saturated for binding displayed significantly different peak heights . Thus , at least for FNR , peak height cannot be used to assess relative differences in site occupancy between chromosomal sites . Cross-linking or immunoprecipitation of FNR may be less efficient than for LexA because the larger number of other regulators bound at FNR-regulated promoters may affect accessibility to the cross-linking agent or FNR immunoprecipitation . FNR sites having either a strong match to consensus ( for example , ydfZ – TTGATaaaaAACAA ) or a weak match ( for example , frdA – TCGATctcgTCAAA ) were saturated for binding at FNR dimer concentrations at its cellular level ( ∼2 . 5 µM ) [37]; thus , in vivo most accessible FNR sites are likely to be fully occupied . These data also revealed that FNR occupancy was not significantly different for strong and weak sites over the tested range of FNR dimer concentrations , suggesting that in vivo FNR binding is unlikely to be dictated solely by the intrinsic affinity of FNR binding sites . Our finding that not all predicted FNR binding sites are bound by FNR in vivo offers new insight into the accessibility of the genome for binding TFs . Previous studies have predicted anywhere from 12 to 500 FNR binding sites in the E . coli genome [66]–[69] , depending on the algorithm used . Of the 187 FNR binding sites predicted here , only 63 contained a corresponding FNR ChIP-seq peak in the WT strain , suggesting many high quality FNR sites are not bound . Although some of these silent sites may result from false negatives in the ChIP experiments ( e . g . failure to immunoprecipitate FNR bound at some sites ) , only five of the 124 silent FNR sites ( acnA , aldA , hyfA , hmp and iraD ) showed any evidence of FNR regulation in prior studies [70] . Rather , several lines of evidence suggest that binding of NAPs or other TFs masks FNR binding at many of these sites in vivo . First , we observed that binding sites for the NAPs IHF , H-NS , and Fis were statistically overrepresented at the positions of silent FNR binding sites , suggesting these proteins occlude FNR binding . Second , we found that in the absence of H-NS and StpA , additional FNR binding sites became available for FNR binding as detected by ChIP , suggesting that NAPs influence FNR site availability in vivo . A similar effect has been observed in eukaryotes , where extensive research on TF site availability has shown that chromatin structure in vivo can block binding of TFs ( e . g . Pho4 , Leu3 and Rap1 ) to high quality DNA binding sites [1] , [2] , [4] . Additionally , known changes to chromosomal structure by IHF , Fis , and H-NS have been shown to inhibit DNA binding of other proteins [7] , [12] , [71] . Thus , if the binding profiles of NAPs change under alternative growth conditions , then the occluded FNR binding sites would likely become available for FNR binding . Nonetheless , the fact that the 207 FNR-enriched regions from this study included 80% of the 63 regions identified by Grainger et al . ( Table S5 ) , despite the difference in the growth conditions and experimental design [29] , suggests that the overlapping subset of FNR binding events may reflect a core set that is insensitive to growth conditions or binding of other TFs . Furthermore , binding events specific to each growth condition may be reflective of either changes in accessibility of FNR to binding sites due to changes in DNA-binding protein distribution or perhaps increases in activity of a second TF that binds cooperatively . Other regulators , such as CRP , a closely related member of the FNR protein family , also appear to have more binding sites available genome-wide than are occupied in vivo under tested growth conditions . Shimada et al . identified 254 CRP-cAMP binding sites using Genomic SELEX screening , which was 3–4 fold more than the number of CRP sites previously identified by ChIP-chip experiments [72] , [73]; thus not all chromosomal CRP sites appear to be accessible for binding , although additional experiments would be required to explicitly examine the accessibility of CRP binding sites throughout the genome . Taken together , these results suggest that the restrictive effect of chromosomal structure could influence TF binding beyond FNR . Environmental stimuli that change NAP distribution would also change TF binding site accessibility and affect transcription . For example , as E . coli enters the mammalian GI tract , it experiences a temperature increase from ∼25°C to 37°C , and this increase in temperature has been shown to affect transcription of a number of operons , including increased expression of anaerobic-specific operons [74] , [75] . Because H-NS binding is sensitive to changes in temperature [76] , [77] , an explanation for these temperature-dependent transcriptional changes [74] , [75] could be genome-wide decreases in H-NS binding and distribution; these changes could increase the accessibility of the binding sites for FNR and other TFs to regulate transcription . Supporting this explanation , several genes with a temperature dependent increase in expression showed FNR binding and regulation in the absence of H-NS and StpA , including hlyE , feaR , yaiV , and torZ . The activity of NAPs can also be affected by the binding of other condition specific TFs . For example , ChIP-chip and Genomic SELEX analysis of the stationary phase LysR-type TF , LeuO , suggested that binding of LeuO antagonized H-NS activity , but not necessarily H-NS binding , throughout the genome in Salmonella enterica and E . coli [78] , [79] . Thus , a picture emerges from our data that binding of FNR is dependent on characteristics of the genome beyond the presence of a FNR binding site; this restrictive effect of chromosome structure by NAPs may affect binding of other TFs in bacteria . NAPs have been shown to occlude and affect binding of TFs and other DNA binding proteins , such as restriction endonucleases and DNA methylation enzymes , suggesting a general role of NAPs in regulating genome accessibility by bending , wrapping and bridging the DNA structure [7] , [12] , [13] , [27] , [42] , [76] , [80] , [81] . Additionally , NAPs influence DNA supercoiling , which has been shown to affect binding of the TFs Fis and OmpR in S . enterica [82] , [83] , providing another mechanism by which NAPs can change the chromosomal structure to influence TF-DNA binding . Taken together , our results support a dynamic model of complex genome structure that affects TF binding to control gene regulation in bacteria . Although expression of a subset of the operons in the FNR regulon appeared to require only FNR for regulation ( Categories 1 and 2 ) , our findings point to widespread cooperation between FNR and other TFs for condition-specific regulation ( Categories 3 and 4 ) . Changes in activity of these TFs would result in FNR regulation to adapt to changes in environment , such as growth in non-catabolite repressed carbon sources ( CRP ) [57] , anaerobic respiration of nitrate ( NarL and NarP ) [19] , and growth in iron-limiting conditions ( Fur ) [84] . Although this co-regulation provides insight into growth conditions that should allow FNR-dependent changes in gene expression , the synergistic regulators for many promoter regions bound by FNR are currently unknown ( Category 5 ) , but would likely be identified in future genome-scale studies using different growth conditions , particularly microaerobic growth , which has been shown to affect FNR regulation of virulence genes in the pathogen Shigella flexneri [85] . Overall , our results suggest that the regulation of a subset of FNR-dependent promoters in E . coli may depend on combinatorial regulation with other TFs , a mechanism that resembles regulation of eukaryotic promoters [8] , [20] , [86] . These experimental data support previous in silico regulatory models generated using published data [87]–[89] , suggesting combinatorial regulation may be common in E . coli . Further , ChIP-chip and ChIP-seq analyses of other TFs in E . coli ( e . g . CRP , Fis , and IHF ) and Salmonella typhimurium ( e . g . Sfh , a H-NS homolog ) , identified many TF binding sites that did not correlate with changes in gene expression in corresponding TF-specific transcriptomic experiments [30] , [33] , [73] , [90] , [91] . These results raise the possibility of potential combinatorial regulation for other TFs , although additional analysis is required to support this notion . We found that FNR directly controls expression of five secondary regulators , most of which are also regulated by specific cofactors , suggesting that the scope of the indirect FNR regulon ( Category 6 ) is also likely to change depending on growth conditions . Of the five regulators , three act in an apparent hierarchal manner . The small RNA FnrS , which is upregulated by FNR and is suggested to stimulate mRNA turnover , decreased the mRNA levels of multiple FnrS target genes in GMM [48] , [49] . Expression of the TF CaiF was also activated by FNR , but the genes regulated by CaiF were not expressed in GMM because CaiF requires the effector carnitine to be active [92] . FNR activated BssR , a TF involved in biofilm formation . About ∼40 operons are thought to be controlled by BssR [93] , but none of the five BssR-dependent operons in the FNR indirect regulon that we tested by qRT-PCR showed any change in expression in a BssR− strain ( data not shown ) ; thus , under our growth conditions , BssR appeared to be inactive . FNR also directly repressed the expression of two TFs , including the pyruvate sensing TF PdhR which represses several operons in the absence of pyruvate [94] , [95] . Although one might expect that PdhR repressed genes would increase anaerobically , many of these genes are redundantly repressed by ArcA ( Park and Kiley , Personal Communication ) ; thus the impact of PdhR may be negligible under anaerobic growth in GMM . Similarly , the TF GadE , which is active at low pH [96] , was also directly repressed by FNR and accordingly the operons in the GadE regulon were not identified as part of the indirect FNR regulon in GMM . Finally , we note the caveat that some operons that appear indirectly regulated by FNR may change expression as a result of indirect physiological and metabolic effects in a FNR− strain , which may alter the activity of other TFs , resulting in mis-regulation of operons . For example , our data show that FNR does not directly regulate arcA transcription , but previous results have suggested that ArcA activity may be affected by the metabolic changes that occur when fnr is deleted [97] . Thus , although a subset of ArcA regulatory targets ( 29 operons ) showed potential indirect FNR regulation , such effects were likely caused by changes in the phosphorylation state of ArcA resulting from metabolic changes in a FNR- strain ( Table S13 ) ( Park and Kiley , Personal Communication ) . In conclusion , our results reveal complex features of TF binding in bacteria and expand our understanding of how E . coli responds to changes in O2 and other environmental stimuli . A subset of predicted FNR binding sites appear to be inhibited by NAPs and are available in the absence of H-NS and StpA , suggesting that the bacterial genome is not freely accessible for TF binding and that changes in TF binding site accessibility could result in changes in transcription . Finally , correlation of the occupancy data with transcriptomic data suggests that FNR serves as a global signal of anaerobiosis but the expression of a subset of operons in the FNR regulon requires other regulators sensitive to alternative environmental stimuli . This strategy is reminiscent of global regulation by CRP-cAMP [73] in that FNR , like CRP , is bound at many promoters under specific conditions without corresponding changes in mRNA levels , suggesting a common strategy whereby promoters are primed to be activated when the appropriate growth conditions are encountered .
All strains were grown in MOPS minimal medium supplemented with 0 . 2% glucose ( GMM ) [98] at 37°C and sparged with a gas mix of 95% N2 and 5% CO2 ( anaerobic ) or 70% N2 , 5% CO2 , and 25% O2 ( aerobic ) . Cells were harvested during mid-log growth ( OD600 of ∼0 . 3 using a Perkin Elmer Lambda 25 UV/Vis Spectrophotometer ) . E . coli K-12 MG1655 ( F- , λ- , rph-1 ) and PK4811 ( MG1655 ΔfnrΩSpR/SmR ) [99] were used for the ChIP-chip , ChIP-seq and transcriptomic experiments unless otherwise specified . All data obtained in this study used GMM as the growth media , and although we know that not all promoters directly regulated by FNR are expressed under these conditions , this has the advantage that both mutant and parental strains exhibit the same growth rate . For experiments that varied the in vivo concentration of FNR , a strain that contained a single , chromosomal copy of WT fnr under the control of the Ptac promoter at the λ attachment site was constructed . Following digestion of pPK823 [99] with XbaI and HindIII , the DNA fragment containing fnr was cloned into the XbaI and HindIII sites of pDHB60 ( ApR ) [100] to form pPK6401 . Plasmid pPK6401 was transformed into DHB6521 [100] and the Ptac-fnr construct was stably integrated into the λ attachment site using the Lambda InCh system as described [100] to produce PK6410 . P1vir transduction was used to move the Ptac-fnr , ApR allele into strain PK8257 , which contains the FNR activated ydfZ promoter-lacZ fusion and deletion of lacY . This strain was transformed with pACYClacIQ-CAM [101] to generate PK8263 . To determine the effect of FNR on the expression of the BssR regulon , a ΔbssR strain was constructed by P1vir transduction of ΔbssR::kanR from the Keio collection [102] into MG1655 to generate PK8923 . To determine the role of H-NS on FNR binding , first stpA was recombined with the CmR gene , cmr , using λ red recombination and the pSIM plasmid [103] . P1vir transduction introduced the Δhns::kanR allele from the Keio collection [102] into the strain lacking stpA to generate the Δhns/ΔstpA strain . Total RNA was isolated as previously described [104] . The concentration of the purified RNA was determined using a NanoDrop 2100 , while the integrity of the RNA was analyzed using an Agilent 2100 Bioanalyzer and the RNA Nano LabChip platform ( Agilent ) . Total RNA ( 10 µg ) from two biological replicates each of MG1655 ( +O2 and −O2 ) and PK4811 was reverse transcribed using random hexamers ( Sigma ) and the SuperScript II Double-Stranded cDNA Synthesis Kit ( Invitrogen ) following the manufacturer's protocol . The cDNA ( 1 µg ) was fluorescently labeled with Cy3-labeled 9 mers ( Tri-Link Biotechnologies ) with Klenow Fragment ( NEB ) for 2 hours at 37°C and recovered using ethanol precipitation . Labeled dsDNA ( 2 µg ) was hybridized onto the Roche NimbleGen E . coli 4plex Expression Array Platform ( 4×72 , 000 probes , Catalog Number A6697-00-01 ) for ∼16 hours at 42°C in a NimbleGen Hybridization System 4 ( Roche NimbleGen ) following the manufacturer's protocol . The hybridized microarrays were scanned at 532 nm with a pixel size of 5 µm using a GenePix 4000B Microarray Scanner ( Molecular Devices ) , and the PMT was adjusted until approximately 1% of the total probes were saturated for fluorescence intensity . The data were normalized using the Robust Multichip Average ( RMA ) algorithm in the NimbleScan software package , version 2 . 5 [105] . ArrayStar 3 . 0 ( DNASTAR ) was used to identify genes that showed at least a two-fold change in expression between the WT and Δfnr strains and were significantly similar among biological replicates , using a moderated t-test ( p-value<0 . 01 ) [106] . Genes were organized into operons using data from EcoCyc [70] . An operon was called differentially expressed ( DE ) if only one gene within an operon showed a statistically significant change in expression . NimbleGen microarrays identified 214 statistically significant DE genes that were contained within 134 operons The anaerobic MG1655 and FNR− samples from the normalized whole genome expression microarray data from Kang et al . [18] were also analyzed . Genes were determined to be DE if they had a change in expression greater than or equal to two-fold and if the genes were found to be statistically similar between biological replicates using a t-test ( p-value<0 . 01 ) . An operon was called DE if only one gene within an operon showed a statistically significant change in expression . This analysis identified 204 significant DE genes in 130 operons . Sixty operons were found to be DE in both the NimbleGen and Kang et al . data sets ( Table S8 ) . Of the 70 operons found DE in only the Kang et al . data set , 41 operons were just below the significance threshold in the NimbleGen data set and 11 operons resulted from activation of the flagellar regulon due to an insertion upstream of flhDC , which was absent in the isolate of MG1655 used in this study . The Δhns/ΔstpA aerobic and anaerobic expression data were obtained from stand specific , single stranded cDNA hybridized to custom designed , high-density tiled microarrays containing 378 , 000 probes from alternate strands , spaced every ∼12 bp through the genome as described previously [107] except Cy3 was used instead of Cy5 . Microarray hybridization and scanning were performed as described above except that the PMT was adjusted until the median background value was ∼100 . All probe data were normalized using RMA in the NimbleScan software package , version 2 . 5 [105] . Gene probe values found to be significantly different between two biological replicates using a Benjamini & Hochberg corrected t-test ( p-value<0 . 05 ) were eliminated from further analysis . Genes were called DE if the median log2 values were different by more than two-fold and if the genes were significantly different using an ANOVA test ( p-value<0 . 05 ) . To enrich for mRNA from total RNA , the 23S and 16S rRNA were removed using the Ambion MICROBExpress kit ( Ambion ) following manufacturer's guidelines , except the total RNA was incubated with the rRNA oligonucleotides for one hour instead of 15 minutes . The rRNA depleted RNA samples isolated from two biological replicates of MG1655 and its FNR− derivative were processed by the Joint Genome Institute ( JGI ) for RNA-seq library creation and sequencing . The RNAs were chemically fragmented using RNA Fragmentation Reagents ( Ambion ) to the size range of 200–250 bp using 1× fragmentation solution for 5 minutes at 70°C ( Ambion ) . Double stranded cDNA was generated using the SuperScript Double-Stranded cDNA Synthesis Kit ( Invitrogen ) following the manufacturer's protocol . The Illumina Paired End Sample Prep kit was used for Illumina RNA-seq library creation using the manufacturer's instructions . Briefly , the fragmented cDNA was end repaired , ligated to Illumina specific adapters and amplified with 10 cycles of PCR using the TruSeq SR Cluster Kit ( v2 ) . Single-end 36 bp reads were generated by sequencing on the Illumina Genome Analyzer IIx , using the TruSeq SBS Kit ( v5 ) following the manufacturer's protocol . Resulting reads were aligned to the published E . coli K-12 MG1655 genome ( U00096 . 2 ) using the software package SOAP , version 2 . 20 [108] , allowing no more than two mismatches . Reads aligning to repeated elements in the genome ( for example rRNA ) were removed from analysis . For reads that had no mapping locations for the first 36 bp , the 3–30 bp subsequences were used in the subsequent mapping to the reference genome . Reads that had unique mapping locations and did not match annotated rRNA genes were used for further analysis . For each gene , the tag density was estimated as the number of aligned sequencing tags divided by gene size in kb and normalized using quantile normalization . The tag density data were analyzed for statistically significant differential expression using baySeq , version 2 . 6 [109] with a FDR of 0 . 01 , and genes were organized into operons using data from EcoCyc [70] . An operon was called DE if only one gene within an operon showed a statistically significant change in expression . The RNA-seq analysis identified 133 statistically significant DE operons ( 197 genes ) . Altogether , microarray and RNA-seq experiments identified 258 operons DE by FNR and slightly fewer than half of these operons ( 122 ) were found in at least two of the transcriptomic experiments ( Figure S5 , Table S8 ) . ChIP assays were performed as previously described [110] , except that the glycine , the formaldehyde and the sodium phosphate mix were sparged with argon gas for 20 minutes before use to maintain anaerobic conditions when required . Samples were immunoprecipitated using polyclonal antibodies raised against FNR , IHF or H-NS , which had been individually absorbed against mutant strains lacking the appropriate protein . In the case of FNR , affinity purified antibodies were used in some experiments , purified using the method previously described [111] . For RNA Polymerase , a σ70 monoclonal antibody from NeoClone ( W0004 ) or a RNA Polymerase ß monoclonal antibody from NeoClone ( W0002 ) were used for immunoprecipitation . For FNR , neither lengthening the cross-linking time nor increasing or decreasing the amount of FNR antibody used in the ChIP protocol showed significant changes in the FNR ChIP-chip peak heights or number of peaks identified . For ChIP-chip , FNR ( three samples ) , FNR− ( one sample ) , β ( two samples ) , H-NS ( two samples ) and IHF ( two samples ) were fluorescently-labeled using Cy3 ( INPUT ) and Cy5 ( IP ) and hybridized for ∼16 hours at 42°C in a NimbleGen Hybridization System 4 ( Roche NimbleGen ) to custom designed , high-density tiled microarrays containing 378 , 000 probes from alternate strands , spaced every ∼12 bp through the genome . The hybridized microarrays were scanned at 532 nm ( Cy3 ) and 635 nm ( Cy5 ) with a pixel size of 5 µm using a GenePix 4000B Microarray Scanner ( Molecular Devices ) , and the PMT was adjusted until approximately 1% of the total probes were saturated for fluorescence intensity of each dye used . The NimbleScan software package , version 2 . 5 ( Roche NimbleGen ) was used to extract the scanned data . ChIP-chip data were normalized within each microarray using quantile normalization ( “normalize . quantiles” in the R package VSN , version 3 . 26 . 0 ) [112] to correct for dye-dependent intensity differences as previously described [113] . Biological replicates were normalized between microarrays using quantile normalization as previously described [113] , and the normalized log2 ratio values ( IP over INPUT ) were averaged . There was a strong correlation between enriched regions of ChIP-chip biological replicates ( R = 0 . 7 ) . ChIP-chip peaks for FNR , H-NS and IHF were identified in each data set by the peak finding algorithm CMARRT , version 1 . 3 ( FDR of 0 . 01 ) [114] and proportional Z-tests were used to determine significant differences between proportional data . For ChIP-seq experiments , 10 ng of immunoprecipitated and purified DNA fragments from the FNR ( two biological replicates ) and σ70 samples ( two biological replicates from both aerobic and anaerobic growth conditions ) , along with 10 ng of input control , were submitted to the University of Wisconsin-Madison DNA Sequencing Facility ( FNR samples and one σ70 sample ) or the Joint Genome Institute ( one σ70 sample ) for ChIP-seq library preparation . Samples were sheared to 200–500 nt during the IP process to facilitate library preparation . All libraries were generated using reagents from the Illumina Paired End Sample Preparation Kit ( Illumina ) and the Illumina protocol “Preparing Samples for ChIP Sequencing of DNA” ( Illumina part # 11257047 RevA ) as per the manufacturer's instructions , except products of the ligation reaction were purified by gel electrophoresis using 2% SizeSelect agarose gels ( Invitrogen ) targeting either 275 bp fragments ( σ70 libraries ) or 400 bp fragments ( FNR libraries ) . After library construction and amplification , quality and quantity were assessed using an Agilent DNA 1000 series chip assay ( Agilent ) and QuantIT PicoGreen dsDNA Kit ( Invitrogen ) , respectively , and libraries were standardized to 10 µM . Cluster generation was performed using a cBot Single Read Cluster Generation Kit ( v4 ) and placed on the Illumina cBot . A single-end read , 36 bp run was performed , using standard SBS kits ( v4 ) and SCS 2 . 6 on an Illumina Genome Analyzer IIx . Basecalling was performed using the standard Illumina Pipeline , version 1 . 6 . Sequence reads were aligned to the published E . coli K-12 MG1655 genome ( U00096 . 2 ) using the software packages SOAP , version 2 . 20 , [108] and ELAND ( within the Illumina Genome Analyzer Pipeline Software , version 1 . 6 ) , allowing at most two mismatches . Sequence reads with sequences that did not align to the genome , aligned to multiple locations on the genome , or contained more than two mismatches were discarded from further analysis ( <10% of reads ) . For visualization the raw tag density at each position was calculated using QuEST , version 1 . 2 [115] , and normalized as tag density per million uniquely mapped reads . The read density was determined for each base in the genome for the IP and INPUT samples for FNR and σ70 samples . For FNR , peaks were identified using three peak finding algorithms: CisGenome , version 1 . 2 , NCIS , version 1 . 0 . 1 , and MOSAiCS , version 1 . 6 . 0 [116]–[118] ( FDR for all of 0 . 05 ) , while σ70 peaks were identified using NCIS , version 1 . 0 . 1 ( FDR of 0 . 05 ) . Further discussion of these algorithms is in Text S1 . Differences between aerobic and anaerobic σ70 ChIP-seq occupancy were determined using a one-sided , paired t-test ( p-value<0 . 01 ) comparing 100 bp surrounding the center of each peak . To normalize between +O2 and −O2 samples , the read counts for the enriched regions ( peaks ) for each sample were shifted by the negative median read count value of the background ( un-enriched ) signal . The p-values were adjusted using the Bonferroni method to correct for multiple testing . There was a strong correlation between ChIP-seq biological replicates ( R = 0 . 8 ) as well as between ChIP-chip and ChIP-seq data ( Figure S6 ) . All data were visualized in the MochiView browser [119] . Additional ChIP-chip -O2 data sets were performed for WT FNR and a Δfnr [99] control . The 15 FNR peaks identified only in ChIP-chip had low IP/INPUT ratios and were eliminated since ChIP-seq is known to have increased signal to noise relative to ChIP-chip [120] . The Δfnr -O2 ChIP-chip data identified 71 peaks that corresponded to peaks in the FNR -O2 ChIP-seq data , indicating they were not FNR specific , and were removed from the FNR ChIP-seq dataset ( Table S5 ) . To construct the FNR PWM , the sequence of a region of ∼100 bp around the nucleotide with the largest tag density within each of the FNR ChIP-seq peaks ( the summit of each peak ) found by all three peak finding algorithms was analyzed . MEME was used to identify over-represented sequences [121] and the Delila software package was used to construct the PWMs [122] . To search all ChIP-seq peaks for the presence of the FNR PWM , a region of 200 bp around the summit of each FNR ChIP-seq peak was searched with the FNR PWM using PatSer , version 3e [36] , and the top four matches to the FNR PWM , as determined by PatSer PWM score , were recorded at each ChIP-seq peak . The standard deviation of the PatSer scores for the four FNR predicted binding sites at each ChIP-seq peak was determined and used as a threshold to determine the number of predicted binding sites at each peak . If the PatSer predicted FNR binding site at a peak with the highest PatSer score was more than one standard deviation greater than the PatSer predicted FNR binding site with the second best PatSer score , that peak was identified as having only one predicted FNR binding site . For FNR peaks ( ∼11% ) with the two best PatSer predicted FNR binding site scores less than one standard deviation apart , a Grubbs test for outliers was used a single time to identify outliers within the four PatSer predicted FNR binding sites at a peak ( α of 0 . 15 , critical Z of 1 . 04 ) . If a PatSer predicted FNR binding site at a FNR peak was identified as an outlier , it was removed from analysis and the standard deviation was re-calculated using the remaining three PatSer binding site scores at that peak . The remaining PatSer predicted FNR binding sites at the FNR peak were then re-examined as described above . After removing outlier PatSer predicted FNR binding sites , a peak was determined to contain two predicted FNR binding sites if the two best predicted FNR binding sites at that peak had PatSer scores less than one standard deviation apart . The precision-recall curve was constructed using the FNR PWM and searching throughout the genome using PatSer , version 3e [36] . Precision was defined as True Positives ( locations with a FNR ChIP-seq peak and a predicted FNR binding site ) divided by True Positives plus False Positives ( locations with a predicted FNR binding site but no FNR ChIP-seq peak ) . Recall was defined as True Positives divided by True Positives plus False Negatives ( locations with a FNR ChIP-seq peak but no FNR predicted binding site ) . A high precision value means all predicted binding sites are true positives , but there is a high false negative rate . A high recall value means all true positives have been captured , but there is a high false positive rate . The strain with fnr under the control of Ptac ( PK8263 ) was used to study changes in [FNR] on ChIP-chip peak height . Cultures were grown anaerobically overnight in MOPS+0 . 2% glucose and were subcultured to a starting OD600 of ∼0 . 01 in MOPS+0 . 2% glucose plus Cm20 and various [IPTG] ( 4 µM IPTG , 8 µM IPTG , and 16 µM IPTG ) . After this initial step , growth , ChIP-chip experiments ( two biological replicates of 4 and 8 µM IPTG and three biological replicates of 16 µM IPTG were used ) and initial analysis were identical to the procedures described above . Estimates of FNR concentration were determined by quantitative Western blot as previously described [37] . A novel method of normalization was developed to compare peak areas between IPTG concentrations for 35 peaks that showed a large distribution in peak heights and 4 peaks that were classified as false positives by enrichment in the Δfnr ChIP-chip sample . The peak finding algorithm CMARRT identified peaks in the WT FNR ChIP-chip sample , and this peak region was trimmed to include the center 50% of the peak region . This trimmed region was used for each [IPTG] sample for consistency . For each of the 39 peaks examined , the probe values in a region of ∼3000 bp beyond the peak boundary ( ∼1500 bp upstream and downstream of the peak boundary ) was selected for analysis from each sample . Within the ∼3000 bp region , the probes beyond the peak boundary were considered background for each sample . The median of the background ( un-enriched ) probes was calculated and the log2 IP/INPUT probe values for the entire peak region ( enriched and un-enriched ) were shifted by the negative median value of the background probes . The peak average ( average of log2 IP/INPUT values ) and standard deviation was determined for 39 peak regions to compare between samples at each [IPTG] and WT ChIP-chip samples . A one-sided , paired t-test was performed between all conditions ( p-value<0 . 05 ) to determine statistically significant changes in average peak values . Growth , ChIP-chip experiments , normalization and peak calling was performed as described above . To normalize between WT and Δhns/ΔstpA samples , the enriched regions ( peaks ) for each sample were shifted by the negative median log2 IP/INPUT value of the background ( un-enriched ) probes . The peak averages ( average of log2 IP/INPUT values ) were determined for each condition ( WT and Δhns/ΔstpA ) at each FNR peak found in either strain background . A one-sided , paired t-test with Bonferroni correction was performed between the two conditions ( p-value<0 . 05 ) to determine the statistically significant change in peak averages . For peaks found in both WT and Δhns/ΔstpA , peaks were identified as significantly higher in Δhns/ΔstpA using a one-sided , paired t-test with Bonferroni correction performed between the two conditions ( p-value<0 . 05 ) and if the FNR peak average in the Δhns/ΔstpA strain was greater than the standard deviation found for WT peak average . The ChIP-chip and ChIP-seq data can be visualized on GBrowse at the following address: “http://heptamer . tamu . edu/cgi-bin/gb2/gbrowse/MG1655/” . All genome-wide data from this publication have been deposited in NCBI's Gene Expression Omnibus ( GSE41195 ) ( Table S14 ) [123] . | Regulation of gene expression by transcription factors ( TFs ) is key to adaptation to environmental changes . Our comprehensive , genome-scale analysis of a prototypical global TF , the anaerobic regulator FNR from Escherichia coli , leads to several novel and unanticipated insights into the influences on FNR binding genome-wide and the complex structure of bacterial regulons . We found that binding of NAPs restricts FNR binding at a subset of sites , suggesting that the bacterial genome is not freely accessible for FNR binding . Our finding that less than half of the predicted FNR binding sites were occupied in vivo further challenges the utility of using bioinformatic searches alone to predict regulon structure , reinforcing the need for experimental determination of TF binding . By correlating the occupancy data with transcriptomic data , we confirm that FNR serves as a global signal of anaerobiosis but expression of some operons in the FNR regulon require other regulators sensitive to alternative environmental stimuli . Thus , FNR binding and regulation appear to depend on both the nucleoprotein structure of the chromosome and on combinatorial binding of FNR with other regulators . Both of these phenomena are typical of TF binding in eukaryotes; our results establish that they are also features of bacterial TF binding . | [
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] | 2013 | Genome-scale Analysis of Escherichia coli FNR Reveals Complex Features of Transcription Factor Binding |
The layout of sensory brain areas is thought to subtend perception . The principles shaping these architectures and their role in information processing are still poorly understood . We investigate mathematically and computationally the representation of orientation and spatial frequency in cat primary visual cortex . We prove that two natural principles , local exhaustivity and parsimony of representation , would constrain the orientation and spatial frequency maps to display a very specific pinwheel-dipole singularity . This is particularly interesting since recent experimental evidences show a dipolar structures of the spatial frequency map co-localized with pinwheels in cat . These structures have important properties on information processing capabilities . In particular , we show using a computational model of visual information processing that this architecture allows a trade-off in the local detection of orientation and spatial frequency , but this property occurs for spatial frequency selectivity sharper than reported in the literature . We validated this sharpening on high-resolution optical imaging experimental data . These results shed new light on the principles at play in the emergence of functional architecture of cortical maps , as well as their potential role in processing information .
In the neocortex , the part of the mammalian brain in charge of higher functions , multiple sensory modalities are represented . Characterizing finely these functional and anatomical organizations has been a great success of the past decades , in part thanks to great advances in cortical imaging techniques , and we now dispose of a relative clear description of the neocortex architecture . However , the principles that govern these architectures , as well as their role in efficiently encoding and decoding information , remain largely unknown , and are central concepts for comprehending how the brain perceives and processes information [1] . The early visual cortex of higher mammals provides a particularly interesting framework since it contains the concurrent representation of multiple attributes of the visual scene , processed into parallel cortical maps whose layouts are commonly thought to be mutually dependent . Groups of neurons in this area are preferentially selective to one specific value for each attribute . For instance , in response to a drifting grating , neurons in the early visual cortex encode the orientation ( OR ) [2] of the stimulus as well as its spatial frequency ( SF ) [3] . The two-dimensional OR map is continuous and consists of regular domains where preferred OR varies smoothly together with singularities , the pinwheel centers ( PC ) , around which all ORs are represented [4 , 5] . Moreover , other features like ocular dominance [6] and the local nature of the visual scene are retinotopically encoded in the primary visual area [2]: the information of a specific zone of the visual scene is processed by nearby neurons [7] , and brain areas organizing these neurons reproduce the same characteristics at several places into a quasi-periodic structure [8] . Within a fundamental domain around a PC , the OR map is locally exhaustive ( all attributes are represented ) , yet it is parsimonious in the sense that any OR is represented along a single level set . These two principles constitute very natural candidates for organizing the maps , yielding specific zones receiving all the information of the visual scene in an economic manner . The study of the representation of other attributes may allow investigating whether these principles also constrain their layout . Among other possible functional organization in the visual cortex , the SF has recently attracted much interest . A common view is that its organization is constrained to that of the OR in order to ensure a uniform coverage [9] , i . e . an even representation of the pairs ( OR , SF ) . This theory was supported by data reporting an orthogonal relationship between iso-SF and iso-OR lines [10 , 11] or the fact that PCs shall be situated near extrema of the SF representation [12–15] . These evidences did not appear clearly across different species: while strong orthogonality has been reported at global scale in monkey [10] , only a weak tendency to orthogonality was shown in ferret [11] . In cat , it remains a disputed issue . Indeed , it was recently shown that the distribution of angles between iso-OR and iso-SF lines were not peaked around 90 degrees: these are globally uniform , with a small bias towards alignment in the vicinity of PCs [16] . This context motivated us to come back to this problem . We mathematically demonstrate here that SF representations that satisfy our two candidate principles , namely that are locally exhaustive and optimally parsimonious , organize around singular points into a universal topology evocative of an electric dipole potential ( see Fig 1 ) . This theory is particularly interesting since recent high resolution optical imaging data in cat [17] provide first evidences in favor of the presence of a continuous structure of SF maps with dipolar singularities co-localized with the PCs . Beyond the possible principles at the origin of this architecture , such organizations have important consequences on the coding capabilities of associated cortical areas . We show using a computational model that pinwheel-dipole ( PD ) architectures , even if they do not allow even representation of ( OR , SF ) , may improve perceptual precision compared to the orthogonal architecture . Going deeper into the dependence of coding capabilities of PD architectures to SF selectivity , we realize that these organizations leave room for balanced detection of both attributes , but this occurs for SF selectivities sharper than the value previously reported in the literature [16] . Using finer estimates of the selectivity in the vicinity of PCs , we show indeed a clear sharpening of the SF selectivity near PCs perfectly consistent with the computational value predicted by the trade-off , leading to the natural prediction that PCs are singular locations of several maps at which selectivity ensures balanced detection .
Finding an optimal topology satisfying few simple conditions is easier said than done . A striking feature of the OR map is its very specific organization around singularities , the pinwheel topology , where the map is locally exhaustive and parsimonious . We will show that these two principles can characterize univocally the topology of maps representing periodic ( e . g , OR ) or non-periodic quantities ( e . g , SF ) . Because of the quasi-periodic structure of visual representations and the local nature of our criteria , we restrict our analysis to a small region of the visual cortex defined by an open set Ω , which is assumed to be , without loss of generality , a disc . The OR map is therefore defined as a continuous function f : Ω ↦ S 1 where S 1 is the circle [0 , π] where we identify 0 and π . The SF map g is also defined on Ω , and takes values on an open ( non-periodic ) interval U ⊂ R . Maps will be said exhaustive on S 1 or U if their range covers the whole set of possible values . The topological redundancy of a map is mathematically defined as the maximal number of connected components of the level sets . Parsimonious maps are those achieving the minimal redundancy possible . For instance , the pinwheel topology , corresponding to maps in which level sets are single arcs connecting a singular point to the boundary of Ω ( see for example the case of the isotropic pinwheel represented in Fig 1A ) , has redundancy one and is hence parsimonious . Dipolar topologies correspond to maps whose level sets are made of two lobes of closed loops connecting the boundary to itself , completed by pairs of arcs connecting the singularity to the boundary of the domain . These have thus redundancy two , as is the case for example of the real-valued map plotted in Fig 1B . These two topologies are particularly important: indeed , we shall demonstrate that these are the unique topologies that are surjective ( i . e . , exhaustive ) and minimize the topological redundancy ( parsimony ) . We note that such maps necessarily show singularities . We concentrate on maps that are everywhere continuous except at isolated points . Such maps are referred to as smooth simple maps . We demonstrate in S1 Text section I the following: Theorem 1 . Smooth simple maps that are exhaustive and parsimonious enjoy the following universality: Consequently , pairs of smooth simple maps ( f , g ) : Ω ↦ S 1 × U satisfying both exhaustivity and parsimony of each coordinate at arbitrarily small scales are the PD topology with co-localized singularities . This theoretical result is very general: it shows a universal property of maps satisfying local exhaustivity and parsimony principles . In particular , in view of our biological problem , shall the OR and SF maps satisfy these two principles , one will necessarily find PD structures in the vicinity of the PCs of the OR map . The proof of the theorem is based on ( i ) proving that the two principles impose that the maps have a singularity , and ( ii ) that the assumptions of the theorem constrain level sets to have precisely the desired topology . This mathematical result has several implications that account for some experimental facts [16] inconsistent with the orthogonal architectures , including ( i ) the sharp transition of the SF map at PC locations , and ( ii ) the non-orthogonal distribution of angles between iso-OR and -SF lines . Indeed , PD structures show a globally uniform distribution , with generically a small bias towards alignment for saturating models . In order to show the latter property , we shall study a simple model of PD architecture , with an SF map chosen in analogy with the electric dipole potential in 2 dimensions . In detail , the PD model is given by the dimensionless maps1 φ: z ↦ arg ( z ) /2 and γ : z ↦ Re ( 1 / z ) or , in polar coordinates for z = reiϕ , φ : z ↦ ϕ 2 and γ : z ↦ cos ( ϕ ) r , respectively for the OR and the SF ( see S1 Text section II for more details ) . For this specific pair of maps , it is easy to show by direct calculation that the angle distribution is uniform . Although qualitatively consistent with the overall flat distribution reported in [16] , it does not account for the slight over- ( under ) -expression of parallel ( orthogonal ) lines . This is due to the unrealistic sharp divergence of the SF representation of the electric dipole . Biological dipoles shall saturate to a maximal and minimal value at the singularity , and this saturation recovers this bias , as we show in the S1 Text . For instance , a simple generalization of the γ-map that allows for analytical developments , γ α : ( r e i ϕ ↦ cos ( ϕ ) r α ) with α < 1 , which is less sharp than the γ map ( but still diverging ) , reproduces the distribution of angles very accurately as we show in Fig 1C . The thresholded γ map also has the generic property of fitting accurately the distribution ( Fig 1C ) , as generically do maps with SF saturating at the singularity . These properties of PD architectures tend to point towards the fact that dipoles are consistent with previously reported facts on the behavior of the SF map at PCs . And recent optical imaging data have provided direct evidences of the presence of PD architectures using new high resolution optical imaging data to resolve the fine structure of the SF map on cat’s early visual cortex in the vicinity of PCs [17] . From the functional viewpoint , the fact that the PD architecture is highly non-orthogonal implies that the sampling of the attributes is not uniform near PCs . One may therefore expect the coding properties of the PD architectures to be very different than in a uniform coverage architecture . Under the uniform coverage assumption , orthogonality of level sets implies that the SF representation reaches a maximum ( or a minimum ) at the PC and smoothly decays away from the PC . Therefore , in a neighborhood of the PC , only a small portion of the range of SF is represented , contrasting with the PD architecture which is exhaustive . In particular , full representation of both high and low SFs in the orthogonal architecture would necessitate at least two PCs , one corresponding to a maximum of SF representation and the other one a minimum . At one given PC with orthogonal topology ( say , corresponding to a maximum of the SF representation ) , it is likely that stimuli with high SF will be well encoded regardless of their OR , but may be blind to low SF stimuli . In contrast , the PD structure represents both high and low SFs , but different SFs are associated to distinct ranges of OR ( see Fig 2 ) . Overall , in both architectures , we find cells with preferred OR and SF covering the same proportion of possible stimuli , but with a different structure in the parameter space . It is therefore a priori unclear whether one of the architectures could present an advantage in perception capacity . In order to investigate this question , we simulated the activation of a piece of cortex with a PD or orthogonal architecture both fitted to the optical imaging data . We thus modeled the response of a cortical area to a given stimulus , and compared ( i ) the capacity of the PD and orthogonal architectures in discriminating two stimuli and ( ii ) the precision of the coding of both architectures . Models and results are described below , and more details on the data and models can be found in the Material and Methods section and S1 Text Section III .
This study showed that optimizing simple criteria strongly constrains the layout of maps , and that these layouts can provide specific coding capabilities . We concentrated here on two principles , local exhaustivity and parsimony , which imply that both maps shall display co-localized singularities , around which the OR map is organized as a pinwheel and the SF map as a dipole . While pinwheels were identified since decades [4] , dipoles were never observed in previous studies . In a companion paper [17] , high resolution optical imaging made it possible to observe these topologies and validate quantitatively the presence of PD singularities . It is striking that both maps satisfy the same optimality properties near the same singularities . At the scale of the whole map , fine detection of local characters in the visual scene makes optimal parsimony not desirable: it is rather important to be able to have accurate detections at several places of the visual scene . Principles at play in the architecture of the whole map shall therefore take into account this necessity , as well as some invariance principles [8 , 19] that may fix the density of singularities with respect to a map typical scale . Moreover , purely global criteria such as continuity and coverage are often not sufficient to reproduce quantitatively the architectures of maps in the early visual cortex [20–22] . It is likely that its overall structure emerges from a compromise between local criteria , invariance principles and global continuity-coverage optimization . Locally around the singularity , we showed that the maps organization has important implications in coding capabilities . It is noticeable that the PD architecture allows for a balanced detection of the OR and the SF , but for tuning widths smaller than the value reported in the literature . This fact did not depend tightly on the choice of the model: it was consistently obtained qualitatively and quantitatively over a class of functions fitted to the experimental data . This has motivated us to estimate more finely the SF tuning width in the vicinity of pinwheels . Consistently with the balance detection point identified in the model , we observed a sharp decrease of the SF tuning width statistically compatible with the model’s prediction . This is a surprising phenomenon: the sharpening of the SF tuning curve contrasts with the well-documented broadening of OR selectivity near pinwheels [18 , 23] . It is interesting to note that sharpening cannot be an artifact of the presence of a singularity , at which subsampling related to the imaging resolution may induce rather a broadening of the selectivity as in the OR case . This significant sharpening of the SF selectivity assembles with a number of other peculiar properties of cells in the vicinity of PCs , including their increased resistance to monocular deprivation [24] , enhanced sensitivity to OR adaptation [25] and distorted retinotopic representation [26] , pointing towards a very specific role of pinwheel location in the visual system . It is worthwhile noting that psychophysics data [27 , 28] in cat report estimates of the visual acuity in OR and SF consistent with balanced normalized errors . Moreover , the normalized error of OR and SF visual acuity of cat falls within the range of 1 − 5% which is precisely the level of normalized error at the intersection of OR and SF error curves . This agreement is very surprising given that our estimates postulate only a very simple decoding algorithm and do not take into account the coding of regions away from PCs . Processing of the visual information likely uses more information and may take advantage of both preferred attributes and regions of maximal sensitivity . From the information theory viewpoint , an important theoretical question is to characterize topologies maximizing information capacity for multiple attributes representation , in the vein of the studies on OR only [29] . Eventually , extending the analysis to the properties of multiple pinwheels ( thus incorporating the properties of regular domains ) is an important endeavor that requires a more detailed experimental characterization of the organization of maps away from PCs . From a biological viewpoint , it would be worthwhile to investigate whether this study can be extended beyond the case of the OR and SF maps of the cat . This would necessitate to record functional maps for other attributes , and check whether these satisfy exhaustivity and parsimony of representation , if the singularities of OR-SF maps are special points of these maps , and if selectivity properties adjust to ensure perceptual trade-offs . It should not be surprising that other principles constrain the layout of other maps . In particular , the direction preference map is not exhaustive near pinwheels , and is discontinuous [30] , because it is constrained to the OR map . Another example is given by the binocular disparity map [31] , which may be optimized to ensure the even representation of visual input from both eyes [32] and by the ocular dominance map which was shown to be orthogonal to the OR [5] and whose role in perception remains to be completely understood [33] . From a mathematical modeling viewpoint , the good agreement between the theoretically derived model and new data constitute an encouraging step towards the development of more complex models that could account for higher order visual areas processing more complex features .
Experiments were conducted on 4 young adult cats aged between 24 and 72 weeks . Animals were anesthetized , paralyzed , and artificially ventilated with a 3:2 mixture of N2O and O2 containing 0 . 5–1% isuflurane . All experiments were performed in accordance with the relevant institutional and national guidelines and regulations ( i . e . , those of the Collège de France , the CNRS , and the DDPP ) . Experiments also conformed to the relevant regulatory standards recommended by the European Community Directive and the US National Institutes of Health Guidelines . In order to investigate the different efficiencies of the PD and orthogonal architectures , we have simulated circular regions Ω of radius R = 50 pixels . Each pixel of these discs represents a set of neurons responding to a specific range of OR and SF , distributed around the singularity at the origin . The amplitude of their response to varying external stimuli produces graded responses , peaked at a specific value ( the quantity represented in the corresponding functional map ) and well approximated by the product of a wrapped Gaussian function , for the OR coordinate , and a Gaussian function , for the SF one ( the tuning curves , see Fig 3B ) [34] . The OR map has been defined as half of the polar angle , modulo an arbitrary phase . For the dipolar SF map we studied the class of functions γα , saturating at extreme values and incorporating possible angular and shape deformations . For the orthogonal case , we considered a rotational invariant map , linearly decreasing from a maximum value located at the origin . The realistic range of values for the free parameters of the PD model have been evaluated by fitting to the optical imaging data ( restricting to fits with coefficient of determination > 0 . 8 , using Matlab function regress ) . The same data have been used to estimate the slope of the radial SF decay parameter in the orthogonal model . For both PD and orthogonal cases , we have simulated 50 different couples of OR and SF maps , differing each other for the parameter sets chosen in these intervals . Details of the models and functions used are provided in S1 Text section III ( as well as in the papers [16 , 17] ) . High-resolution intrinsic optical imaging was performed in cat visual cortical areas A17 and A18 to record maps of OR and SF . All experiments were performed in accordance with the relevant institutional and national guidelines and regulations ( i . e . , those of the Collège de France , the CNRS , and the DDPP ) . Experiments also conformed to the relevant regulatory standards recommended by the European Community Directive and the US National Institutes of Health Guidelines . A complete and in depth description of the experimental protocol is detailed elsewhere [16 , 17] . | Brain areas receiving sensory input often show specific functional organizations whose layout may subtend perception . We aim at understanding possible principles shaping these architectures and their role in information processing . Cat primary visual cortex provides our choice model . Pinwheel singularities of the celebrated orientation map are characterized by a locally exhaustive and parsimonious representation . We mathematically establish that these principles would constrain the spatial frequency map to have dipolar singularities co-localized with the pinwheels . Computational simulations show that this architecture could allow trade-off in detecting orientation and spatial frequency , but for sharper selectivities than reported in the literature . We show on physiological data that a sharp drop indeed appears , consistently with balanced detection paradigm . Moreover , recent experimental data have evidenced the presence of pinwheel-dipole topology in cat visual cortex . | [
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Snakebite is a major problem affecting the rural poor in many of the poorest countries in the tropics . However , the scale of the socio-economic burden has rarely been studied . We undertook a comprehensive assessment of the burden in Sri Lanka . Data from a representative nation-wide community based household survey were used to estimate the number of bites and deaths nationally , and household and out of pocket costs were derived from household questionnaires . Health system costs were obtained from hospital cost accounting systems and estimates of antivenom usage . DALYs lost to snakebite were estimated using standard approaches using disability weights for poisoning . 79% of victims suffered economic loss following a snakebite with a median out of pocket expenditure of $11 . 82 ( IQR 2–28 . 57 ) and a median estimated loss of income of $28 . 57 and $33 . 21 for those in employment or self-employment , respectively . Family members also lost income to help care for patients . Estimated health system costs for Sri Lanka were $ 10 , 260 , 652 annually . The annual estimated total number of DALYS was 11 , 101 to 15 , 076 per year for envenoming following snakebite . Snakebite places a considerable economic burden on the households of victims in Sri Lanka , despite a health system which is accessible and free at the point of care . The disability burden is also considerable , similar to that of meningitis or dengue , although the relatively low case fatality rate and limited physical sequelae following bites by Sri Lankan snakes means that this burden may be less than in countries on the African continent .
Snakebite is a major public health problem in rural communities in Asia , Africa and Latin America [1] . The problem has been extensively studied from a bio-medical perspective but rarely from a socio-economic viewpoint . However , the negative social and economic consequences of snakebite are likely to be considerable . Firstly , snakebite is a problem of tropical low and middle income countries that are already facing the considerable dual burden of communicable and non-communicable diseases; snakebite mortality is strongly associated with low per capita Gross Domestic Product and a low Human Development Index [2] . In such settings , the ill-health associated with snakebite can exert a considerable burden on the economic development of these countries . Secondly , the victims are usually economically productive , young individuals in these communities whose future productive lifespan can be negatively affected by snakebite . Thirdly , most of those affected are rural daily wage earners employed in the informal sector that include farming and other labour intensive occupations , for whom snakebite results in a considerable opportunity cost for being away from work . Very few studies have attempted to formally investigate the socio-economic burden associated with snakebite . A household survey in rural India demonstrated a significant reduction in medium and long-term family income due to snakebite , in addition to the immediate costs of a bite [3] . A West African study estimated years of life lived with disability ( YLD ) due to limb amputations resulting from snakebite using DALYs for 16 countries in the region [4] . Disability-adjusted-life years ( DALYs ) is a widely used metric for estimating disease burden based on strong economic and ethical principles [5] . The Global Burden of Disease ( GBD ) Study used DALYs successfully to describe disability and the burden of important diseases for the period 1990–2020 [6] . Since then , the method of GBD estimation has been improved in repeated attempts to estimate the global disease burden [7] . National estimates of the overall social and economic impact of snakebite have not been attempted for any country to date . This paper estimates the economic cost and disease burden of snakebite for Sri Lanka using data from a recent country-wide community based survey [8] .
The study was approved by the Ethics Review Committee of the Faculty of Medicine , University of Kelaniya . Permission for conducting the study was obtained from District and Divisional level public administrators before data collection . Grama Niladharis of the sampled GN divisions were informed about the study through the public administration system . Written informed consent was obtained from the participants before data collection . The information sheet was read out to illiterate patients in the presence of a family member and a witnessed thumb print was used to signify consent . A country-wide community based cross sectional survey was conducted between August 2012 and June 2013 [8] . The survey was designed to sample approximately 1% of the population of Sri Lanka distributed equally among its nine provinces . A Grama Niladhari ( GN ) division ( the smallest administrative unit in the country ) was defined as a cluster for data collection , and 125 clusters were allocated to each of the 9 provinces . Within each province the number of clusters was divided among the districts in proportion to each districts’ population . The clusters were selected using simple random sampling from the list of GN divisions available at the Department of Census and Statistics , Sri Lanka . 40 households were sampled consecutively from the randomly selected starting point in each cluster . Information related to all residents of the sampled households was obtained by trained data collectors using an interviewer administered questionnaire . They were assisted by local field volunteers recruited from within the cluster . The respondent of each household was either the head of the household or a responsible adult present in the house . A two part structured questionnaire was used for data collection . The questionnaire was translated into Sinhala and Tamil and was pre-tested in a GN division within each province which was not selected for the study . Based on the findings of the pretesting , the questionnaire was fine-tuned prior to use . In the first phase of data collection , the research assistant screened the households for snakebite within the previous 12 months and obtained socio-demographic data from the households . In the second phase of data collection , instruments were administered to the households which had reported snakebites within the previous 12 months in order to obtain details of the bite , clinical manifestations of envenoming , residual disability and deaths due to snakebite . Detailed information on the household costs of snakebite was recorded . Only systemic symptoms or signs were considered to reflect envenoming . Data were double entered into databases created in Epidata software . Discrepancies were corrected by referring to the original data sheets . Data analysis was performed in SPSS version 22 . The median out-of-pocket cost of different cost elements were estimated based on the data reported by the victims or a household member for a number of different out of pocket costs . Victims or household members were also asked to estimate the number of days lost off work due to the snakebite and the amount of wages lost by the victim and the household due to the snakebite . The total sum spent by patients for a particular cost item and the proportion of patients that incurred that cost was applied to the estimated national incidence of snakebite to estimate the total annual out-of-pocket cost of snakebite for the entire country . The income lost by the patient or family members and the proportion of patients who lost income in different ways were applied to the estimated national incidence of snakebite to arrive at the total annual lost income due to snakebite for the entire country . The health system cost of snakebite was estimated based on cost data obtained from the cost accounting system maintained at the Teaching Hospital , Kurunegala , Sri Lanka . The average cost of a patient day in the medical ward excluding drug costs was obtained from this database . This cost included the cost of nursing and medical care , investigations and the hotel costs of maintaining a patient in a medical ward . The cost of a patient day amounted to LKR 3214 . 00 ( USD 22 . 96 ) . Ancillary treatments are rarely used in Sri Lanka and were therefore not included . The cost of a vial of anti-venom was obtained from the price list issued by the Medical Supplies Division , Ministry of Health , Sri Lanka [9] . The number of anti-venom vials used was estimated based on the national guidelines for management of snakebite and the assumption that the 30% , 65% and 5% of envenomed patients received respectively , 10 , 20 and 30 vials of anti-venom during the management of envenoming . This assumption was based on the consensus arrived among five specialist physicians experienced in managing snakebite in different parts of the country . The median duration of hospitalization for a snakebite with and without envenoming was estimated based on the data reported by the households of victims . The costs were extrapolated for the country using the estimated national incidence of snakebite from the nationwide survey . DALYs were calculated using the following formula: DALY = YLD + YLL . The template developed by the World Health Organization [10] was used for estimation of DALYs . Population data from the 2012 national census were obtained from the Department of Census and Statistics , Sri Lanka ( [11] . For envenoming , the disability weight used for the higher estimate was 0 . 6 which is the accepted disability weight used for poisoning in the original GBD study [6] . We used a disability weight of 0 . 163 for the lower estimate based on the disability weight used for poisoning in the 2013 GBD study . In using the disability weight for poisoning , we have assumed that snakebite envenoming and poisoning are comparable in terms of the associated disability . The duration of an episode of snakebite with envenoming was considered to be 0 . 3 years . For snakebites without envenoming disability weights of 0 . 006 ( lower estimate ) and 0 . 108 ( higher estimate ) were used . These are the weights used for open wounds in the GBD studies [6 , 12] . The duration of illness for snakebite without envenoming was considered to be 0 . 04 years . The standard discount rate used was 0 . 03 . Beta and constant values used for standard age weighting were 0 . 04 and 0 . 1658 .
551 ( 79 . 3% ) victims incurred an economic loss following a bite and 550 ( 79 . 1% ) victims incurred out-of-pocket expenditure for healthcare . The median total out-of-pocket expenditure per snakebite episode ( envenomed and non-envenomed ) was USD 11 . 82 ( Inter-quartile range 5–28 . 57 ) . Details of out-of-pocket expenditure are given in Table 1 and include travel , food , costs of keeping carers with the victim during the hospital stay , fees for laboratory investigation , purchase of pharmaceuticals and medical products that were not available in the hospital and other unspecified direct costs . In addition , a cost was often incurred for religious and cultural rituals which were organized for 138 ( 19 . 9% ) victims necessitating expenditure for 101 ( 14 . 5% ) families . The median cost of conducting these activities was USD 7 . 14 ( Inter quartile range 3 . 57–14 . 29 ) . The annual estimated national direct out-of–pocket expenditure for snakebite was USD 1 , 981 , 699 . 442 of the victims ( 63 . 6% ) were employed , but only 134 ( 19 . 3% ) reported that they had to stop work temporarily due to the bite . The median total income lost due to the bite by these victims was USD 28 . 57 ( Inter-quartile range 17 . 14–56 . 07 ) ( Table 2 ) . Self-employed victims ( n = 158 , 22 . 7% ) lost a median income of USD 33 . 21 ( Inter-quartile range 17 . 86–54 . 46 ) either due to lost work or costs of a replacement . National annual estimated lost income was USD 910 , 259 and USD 844 , 142 for employed and self-employed victims respectively . At least one family member of 103 ( 14 . 8% ) bite victims had lost at least one workday due to the bite . The median economic loss by these family members due to the bite was USD 28 . 57 ( Inter-quartile range 14 . 29–81 . 07 ) . This amounted to an annual estimated lost income of USD 101 , 037 for family members of victims . Overall , the estimated annual lost income amounted to a total of USD 1 , 855 , 438 , meaning that the total annual economic burden on households nationally was USD 3 , 837 , 137 . The estimated annual health system cost of snakebite management was USD 10 , 260 , 651 . 53 ( Table 3 ) . This comprised approximately USD 6 . 3 million for the snakebite anti-venom and USD 3 . 9 million for the hospital management of patients ( Table 2 ) . Combining household and health system costs meant that the estimated total annual economic burden of snakebite was USD 14 , 097 , 789 . The total YLL due to snakebite was 4 , 765 for males and 4 , 853 for females . Total YLD ranged from 859–3 , 161 for males and 624–2 , 296 for females . The annual estimated total number of DALYs for envenoming and death due to snakebite ranged from a lower estimate of 11 , 101 to an upper estimate of 15 , 076 per year . This comprised 5 , 624–7 , 927 DALYs for males and 5477–7 , 150 DALYs for females , equating to 0 . 5–0 . 7 DALYs per 1000 population for snakebite envenoming ( Tables 4 and 5 ) . The total estimated DALYs due to snakebites without envenoming ranged from 20–500 per year ( S1 and S2 Tables ) .
Snakebite is a major neglected tropical disease . The relative lack of data on the burden of snakebite demonstrates the lack of attention to this condition which is confined to poor rural areas of tropical , low and middle income countries [2] . We have estimated the societal and economic burden of snakebite using nationally representative data generated from a large scale community survey conducted in Sri Lanka . We estimate that snakebite is responsible for up to approximately 15 , 000 DALYs per year . This is three times greater than the estimate from the Institute for Health Metrics and Evaluation for Sri Lanka and would suggest that the burden of snakebite in Sri Lanka is equivalent to that of meningitis and greater than that of dengue [13] . Given the numbers of snakebites in Sri Lanka , the estimated DALYs due to snakebite in this paper are relatively modest compared to those estimated from West Africa [4] . The availability of antivenom and supportive facilities in an accessible and free health system has led to a reduction in snakebite mortality over the last two decades in Sri Lanka to the point where case fatality rates are now only 1 . 5% in envenomed patients [8] . This situation contrasts with the limited access to healthcare facilities and poor availability of antivenom in many parts of Africa , where snakebite occurs in the poorest rural populations [2] . The current crisis in antivenom supply for Africa means that many patients die because they simply cannot be treated [14] . In addition , a major contributor to the burden calculation for West Africa was the disability that results from severe tissue damage and consequent amputation , an outcome that is uncommon following envenoming by venomous species in Sri Lanka [4 , 15] . High quality facilities and emergency care coupled with adequate supplies of anti-venom lead to the good outcomes following snakebite in Sri Lanka , but means the estimated health system costs of managing snakebite are considerable at over USD 10 million and account for 0 . 7% of total government health expenditure [16] . It is unlikely that these costs will reduce in the near future as there is no indication that the high incidence of bites is declining and improving health seeking behaviour means that western standard healthcare facilities are increasingly likely to be used . Even more concerning is the economic burden that snakebite places on victims and their households . Few studies have previously attempted to estimate the effect of snakebite on this although one Tamil Nadu study demonstrated the ongoing adverse household consequences of expenditure on snakebite , requiring loans and selling of household assets to pay for treatment costs and the economic burden of snakebite upon households has also been noted in Bangladesh [3 , 17] . Our research demonstrated the substantial household costs of an episode of snakebite from both out of pocket costs and lost income . Some workers in the formal employment may be compensated by paid sick-leave , but only a few victims of snakebite have formal employment . The median household cost of an episode of envenoming was $12 and around a fifth of patients reported losing income of approximately $30 . To put this in context , the annual per capita expenditure on health in 2014 was $127 [18] and the mean per capita income in the rural areas was $74 per month [19] . The national household economic burden of snakebite amounted to USD 3 . 8 million and it is highly likely in Sri Lanka that snakebite drives the same catastrophic costs for the poor as many other diseases [19 , 20] . There are clearly limitations to this study . Many of the estimates depend on assumptions about the duration of disability and recall of individuals about the costs that they incurred . There were challenges in the estimation of disability burden as there are no accepted disability weights for snakebite and so those for poisoning were used for envenomed individuals and assumptions regarding wounds were made for non-envenomed . Despite this , to our knowledge , this is the first ever comprehensive estimation of a national socio-economic burden from snakebite . Our results demonstrate the extent of this burden in Sri Lanka and highlights the considerable physical and economic impact of this disease , both upon the country and upon the lives of poor rural workers . | Snakebite predominantly affects poor people in the rural tropics . The effect that snakebite has on these populations , both economically and in terms of death and disability , is poorly understood . We used data from a national household survey of snakebite in Sri Lanka to estimate the burden of death and disability and to calculate the financial cost of a snakebite episode for the Sri Lankan health system and for Sri Lankan households . We found that the burden of snakebite was considerable , similar to that of common diseases like meningitis or dengue and that treating snakebite cost the Sri Lankan government over $10 million each year . Despite health care being free in Sri Lanka , almost 80% of households experienced additional costs and loss of income following a snakebite; such costs are disastrous for poor rural workers . | [
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"d... | 2017 | The socio-economic burden of snakebite in Sri Lanka |
This work analyses the genetic variation and evolutionary patterns of recessive resistance loci involved in matching-allele ( MA ) host-pathogen interactions , focusing on the pvr2 resistance gene to potyviruses of the wild pepper Capsicum annuum glabriusculum ( chiltepin ) . Chiltepin grows in a variety of wild habitats in Mexico , and its cultivation in home gardens started about 25 years ago . Potyvirus infection of Capsicum plants requires the physical interaction of the viral VPg with the pvr2 product , the translation initiation factor eIF4E1 . Mutations impairing this interaction result in resistance , according to the MA model . The diversity of pvr2/eIF4E1 in wild and cultivated chiltepin populations from six biogeographical provinces in Mexico was analysed in 109 full-length coding sequences from 97 plants . Eleven alleles were found , and their interaction with potyvirus VPg in yeast-two-hybrid assays , plus infection assays of plants , identified six resistance alleles . Mapping resistance mutations on a pvr2/eIF4E1 model structure showed that most were around the cap-binding pocket and strongly altered its surface electrostatic potential , suggesting resistance-associated costs due to functional constraints . The pvr2/eIF4E1 phylogeny established that susceptibility was ancestral and resistance was derived . The spatial structure of pvr2/eIF4E1 diversity differed from that of neutral markers , but no evidence of selection for resistance was found in wild populations . In contrast , the resistance alleles were much more frequent , and positive selection stronger , in cultivated chiltepin populations , where diversification of pvr2/eIF4E1 was higher . This analysis of the genetic variation of a recessive resistance gene involved in MA host-pathogen interactions in populations of a wild plant show that evolutionary patterns differ according to the plant habitat , wild or cultivated . It also demonstrates that human management of the plant population has profound effects on the diversity and the evolution of the resistance gene , resulting in the selection of resistance alleles .
Host-parasite interactions often show a high degree of genetic specificity , in that only a subset of parasite genotypes can infect and multiply in each host genotype [1–6] . The outcome ( infection vs . resistance ) of the host genotype-by-parasite genotype interaction can be integrated into coevolutionary models that differ in the underlying infection matrices [5] . The different proposed models stem from two general ones , the gene-for-gene ( GFG ) and the matching-alleles ( MA ) models , which were initially proposed to explain plant-parasite and invertebrate-parasite interactions , respectively [1 , 7] , although evidence indicates that they are not taxonomically restricted [5] . These two models differ widely in their conceptual framework . In the GFG model , there is a hierarchy of resistance alleles in the host and infectivity alleles in the parasite , so that some host resistance alleles are intrinsically better than others , conferring resistance to a larger set of parasite genotypes , and similarly , some parasite alleles determining infectivity are intrinsically better than others , allowing infection of a larger set of host genotypes . In the MA model , there is no hierarchy of resistance ( infectivity ) alleles , and a particular host genotype is better at resisting a subset of parasite genotypes , and worse at resisting the rest of parasite genotypes , and a parasite genotype is better at infecting a subset of host genotypes , and worse at infecting the rest [1] . Both models also differ in the mechanisms determining host-parasite interactions . In the GFG model , infection occurs when the host genotype does not recognize the parasite genotype , i . e . , matches between host and parasite molecules do not occur , while in the MA model successful infection requires molecular matches between host and parasite [5 , 7] . Hence , the evolution of resistance ( infectivity ) loci will differ if host-parasite interactions correspond to GFG or MA models . Notably , models predict that costs associated with resistance ( infectivity ) are required to maintain polymorphisms at resistance ( infectivity ) loci in the host ( parasite ) population in GFG interactions , but not in MA ones [1 , 7–9] . Accordingly , evidence of resistance costs has been reported for GFG interactions [10–12] but , to our knowledge , costs of resistance have not been analysed in MA interactions . In the last 20 years a big progress has been made in understanding the molecular genetics of plant-parasite , including plant-virus , interactions . Resistance determined by single dominant genes ( R genes ) is based on host recognition of genotype-specific parasite molecules , being thus compatible with a GFG model , while recessive resistance prevents the matching of the specific host and parasite molecules required for infection , according to a MA model [13–17] . Molecular analyses of the genetic variation of resistance loci in host populations refer almost entirely to R genes determining resistance to cellular pathogens . R genes are considered to have evolved in response to the negative effects of parasite infection on the host fitness [13 , 18 , 19] , that is , to virulence sensu [20] . Data from different systems show that R genes are hypermutagenic , and suggest that they are frequently under balancing selection [21] . In contrast with the effort devoted to understand the evolution of R genes , the molecular evolution of recessive resistance genes ( in fact , susceptibility genes ) has been seldom analysed . This gap is especially important in the case of plant-virus interactions , as a large fraction of monogenic resistance of plants to viruses is recessive [15 , 22] . Thus , the few published reports refer to plant-virus interactions [23–25] , and focus on analyses of germplasm collections of crops , rather than on wild plant populations . Human-driven and natural selection on plant genomes can be very different in both cultivated and wild plant populations [26–29] . Thus , a full understanding of the evolutionary dynamics of MA-like plant-parasite interactions requires analyses in wild plant populations , as well as of comparisons between wild and cultivated ones . Within this scenario , the aim of this work is to analyse the evolutionary patterns of plant recessive resistance loci involved in MA-like interactions , and how these patterns are affected by human management of the host populations . For this , we studied a wild plant that is currently undergoing incipient domestication , the wild pepper or chiltepin , Capsicum annuum var . glabriusculum ( Dunal ) [30] . Chiltepin is considered as the ancestor of the domesticated pepper C . annuum var . annuum L . [31] , an economically important crop that was domesticated in Mesoamerica [32 , 33] . Chiltepin is a 5–10 year-lived perennial bush distributed from northern Colombia to south western United States . In Mexico , it grows in a variety of environments from the evergreen tropical forests of the Yucatan peninsula and the Gulf of Mexico to the dry deciduous forests of central and western Mexico and to the Sonoran desert [33–35] . Chiltepin plants grow and reproduce during the rainy season and their pungent fruits are consumed by birds , which disperse the seeds [34] . In some regions , fruits are harvested from wild populations for human usage [36] and their high value has led to its very recent cultivation . In the last 25 years , chiltepin cultivation has progressed from home gardens to monocultures in small traditional fields , where they are managed as an annual crop [35] . However , cultivated chiltepin does not show obvious phenotypic differences with wild populations and does not present any of the major traits of pepper domestication syndrome , such as larger , pendulous , non-deciduous fruits of different colours and pungency , flower morphology favoring selfing , and synchronized high germination rates [37] . Genetic variation is high in wild populations and shows a strong spatial structure associated with the biogeographical province of origin , and cultivation results in a significant loss of both genetic diversity and spatial genetic structure [35] . Wild and cultivated chiltepin populations are infected by potyviruses , reaching incidences of up to 42% according to population and year [38] . Thus , this work focuses on the recessive resistance gene pvr2 , which has alleles in pepper ( Capsicum spp . ) conferring recessive resistance to virus species in the genus Potyvirus [39] . Potyviruses are a numerous group of economically important plant viruses with tubular particles encapsidating a single-stranded messenger-sense RNA genome of about 10000 nucleotides ( nt ) , with a virus-encoded protein covalently linked to its 5’ end ( VPg ) and a polyadenylated tail at its 3’ end [40] . As for most characterized recessive resistance genes to viruses in plants [15 , 41 , 42] , pvr2 encodes an eukaryotic translation initiation factor , specifically , factor eIF4E1 [39] . Recessive resistance is expressed as immunity ( no infection ) or decreased virus multiplication [15 , 43 , 44] , and the various pvr2 resistance alleles reported differ from the susceptibility wild type allele in a small and mainly non-conservative number of amino acid changes [22 , 23 , 39 , 45] . It has been shown that the potyviral VPg interacts directly with pvr2/eIF4E1 in yeast two-hybrid and in vitro binding assays , and the physical interaction between pvr2/eIF4E1 and the virus VPg is required for virus infection [46–49] , although the exact role in the potyvirus life cycle of eIF4E-VPg interaction remains a matter of discussion [15 , 50] . Mutations at pvr2/eIF4E1 that prevent its interaction with the VPg lead to resistance [22 , 23 , 51] and mutations at the VPg central domain that restore the pvr2/eIF4E1-VPg interaction allow infection [23] . Thus , the pvr2/eIF4E1-VPg-determined pepper-potyvirus interaction corresponds mechanistically to a MA model . The pvr2/eIF4E1 allelic diversity has been extensively screened in accessions of C . annuum var . annuum ( domestic bell and chili pepper ) and , to a lesser extent , in its relatives in the Capsicum genus , reporting one of the largest allelic series of eIF4E , including different susceptibility and resistance alleles to potyviruses [22 , 23 , 39 , 45 , 52 , 53] . Genetic variation and functional analyses have provided evidence of selection at pvr2/eIF4E1 for potyvirus resistance [23] . However , these analyses were based for the largest part on accessions of domestic Capsicum species , and included few accessions of wild relatives , so that selection for potyvirus resistance could be associated with selection pressures ( including potyvirus infection ) specific of , or modulated by , the agroecosystem environment . The reported incidence of potyviruses infection in chiltepin , together with the high genetic diversity of wild chiltepin populations in a variety of habitats in Mexico , and its incipient domestication , makes the chiltepin-potyvirus interaction a unique system to analyse the genetic variation and the evolutionary patterns of a recessive resistance gene ( pvr2/eIF4E1 ) , as well as the potential effects of human management of a host plant and its habitat on the diversity and the evolution of resistance , the two goals of this study . To attain these goals we ( i ) obtained the nucleotide sequence of pvr2/eIF4E1 in plants collected from wild and cultivated chiltepin populations in different biogeographical provinces of Mexico; ( ii ) analysed the genetic diversity and structure of pvr2/eIF4E1 according the region of origin and the level of human management; ( iii ) identified and characterized functionally the different pvr2/eIF4E1 alleles present in chiltepin populations; ( iv ) analysed the effect of these mutations on pvr2/eIF4E1 structure , ( v ) evaluated the frequency of potyvirus resistance in the populations and ( vi ) assessed the incidence of potyvirus infection in chiltepin populations . Our results suggest that resistance probably has associated costs due to functional constraints on pvr2/eIF4E1 . Also , in wild chiltepin populations pvr2/eIF4E1 accumulated synonymous changes , and the frequency of resistance alleles was low , while in cultivated populations pvr2/eIF4E1 accumulated non-synonymous changes and the frequency of resistance alleles was significantly higher than in wild populations . These results are evidence of stronger selection for resistance under cultivation , and indicate a role of human management on the evolution of pvr2/eIF4E1 .
The coding sequence of the pvr2/eIF4E1 gene has a length of 687nt and encodes a predicted protein of 228 amino acids . The variability of the pvr2/eIF4E1 coding sequence was evaluated in 97 chiltepin plants , 70 from wild and 27 from cultivated populations . These plants were randomly selected from 16 wild and 9 cultivated populations ( 2–4 plants per population ) to represent the diversity of the species in six biogeographical provinces of Mexico ( S1 Table ) . Note that neither the total number of sampled populations nor the ratio of wild to cultivated ones is evenly distributed across biogeographical provinces ( S1 Table ) , which reflects the abundance of chiltepin and the intensity of cultivation [35] . A total of 12 . 4% of plants were identified as heterozygous at the pvr2/eIF4E1 locus ( S2 Table ) . The proportion of heterozygous plants was similar between wild and cultivated populations ( χ2 = 1 . 3; P = 0 . 253 ) , the same result being obtained when the plants from cultivated populations were compared with three random subsets of wild plants of the same size ( χ2<30; P>0 . 083 ) . For wild populations , the proportion of heterozygous plants significantly varied between biogeographical provinces ( χ2 = 17 . 9; P = 0 . 003 ) , which was due to the higher frequency of heterozygotes in AZP: when populations from this province were not included in the analysis , heterozygosity no longer depended on province ( χ2 = 2 . 42; P = 0 . 659 ) . From these 97 plants , a total of 109 coding sequences of the pvr2/eIF4E1 gene were obtained , 77 from wild and 32 from cultivated populations , and 17 haplotypes were identified at the nucleotide sequence level ( Table 1 , S1 Table ) . No significant difference in haplotype richness was observed between wild and cultivated populations over all biogeographical provinces ( χ2 = 2 . 4; P = 0 . 169 ) a result that , again , held regardless of sample size ( χ2<1 . 5; P = 0 . 903 ) . The genetic diversity of the coding sequence was of 0 . 00359 ± 0 . 00115 nucleotide substitutions per site for the whole set of 109 pvr2/eIF4E1 sequences and of 0 . 00655± 0 . 00130 for the concatenated sequenced introns ( Table 2 , see S3 Table for detailed intron diversity ) . Coding sequence diversity was highest in YUC and SMO , and lowest in SON and CPS ( Table 2 ) . Plants grown from seeds of fruits purchased at local markets were also analysed , named as local market populations . People selling the fruits claimed that they had been collected from local wild chiltepin populations , which was confirmed on the basis of the polymorphisms of nine microsatellite markers [35] . To further check if local market populations were derived from fruits harvested from wild populations and , thus , represented their genetic diversity , the genetic differentiation of the pvr2/eIF4E1 coding sequences between wild and local market populations was analysed . The value of the fixation index FST between these two groups of populations was very low and not significantly different from zero ( FST ( W/LM ) <0 . 001 , P = 0 . 388 ) , showing no genetic differentiation between these two types of populations that , hence , can be clumped into a single class ( wild populations ) . When the genetic diversity was analysed according to habitat , it was found to be 1 . 4 times higher in the cultivated than in the wild populations ( 0 . 00400 vs . 0 . 00292 , Table 2 ) and the FST value between wild and cultivated populations ( FST ( habitat ) = 0 . 208 , P<0 . 001 ) indicated that pvr2/eIF4E1 was genetically structured according to habitat , a result that held when the comparison was between sequences from cultivated plants and random subsets of sequences from wild plants of the same size ( χ2>0 . 107; P<0 . 001 ) . The diversity of the pvr2/eIF4E1 coding sequences also showed a strong spatial structure , both at the population level ( FST = 0 . 625 , P<10−4 and FST = 0 . 643 , P<10−4 , for all or only wild populations , respectively ) and at the level of the biogeographical province ( FST = 0 . 522 , P<10−4 and FST = 0 . 584 , P<10−4 , for all or only wild populations , respectively ) . More specifically , the chiltepin populations of each biogeographical province were genetically differentiated for the pvr2/eIF4E1 coding sequences , except between CPS/SON , CPS/CPA and CPS/YUC regions ( S4 Table ) . To analyse if this spatial structure followed a model of isolation by distance , a Mantel test was performed between the matrices of genetic and geographical distances among chiltepin wild populations . Data showed that the distribution of the genetic variation of pvr2/eIF4E1 was not correlated with the geographic distance ( r = 0 . 220 , P>0 . 065; S1 Fig ) . Table 2 also shows the nucleotide diversity of the pvr2/eIF4E1 coding sequence at synonymous and non-synonymous positions and the dN/dS ratio indicates that pvr2/eIF4E1 is globally under mild negative selection ( dN/dS = 0 . 899 ) . When sequences from wild and cultivated populations were analysed separately , dN/dS values were significantly different . Evidence for negative selection on pvr2/eIF4E1 was stronger in wild populations ( dN/dS = 0 . 605 ) , while it appeared to be under positive selection in cultivated populations ( dN/dS = 1 . 784 ) . However , no site under positive selection was consistently identified by the different methods applied ( see Material and Methods ) , either when all sequences were analysed together or according to habitat , wild or cultivated . Only codon 205 was identified as under positive selection by the REL method . Tajima’s D ( DT ) showed negative values for pvr2/eIF4E1 ( -0 . 691; -0 . 868 and -0 . 519 for all , wild and cultivated populations , respectively ) which did not depart from the null hypothesis of neutrality . However , a sliding window analysis of DT across the entire pvr2/eIF4E1 coding sequence revealed regions with strongly positive DT values , around codon 105 for wild populations and between codons 67 and 77 in cultivated populations ( Fig 1 ) . Positions 67–77 include those determining potyvirus resistance ( see below ) and position 105 has a polymorphism exclusive to AZP province . At the amino acid sequence level , a total of eleven allelic variants were identified based on 10 polymorphic sites , 7 of which were localized in exon 1 ( Fig 2 ) . Eight of these alleles had been reported previously within the Capsicum genus [22 , 23 , 45] , three of them conferring susceptibility to potyviruses ( pvr2+ , pvr1+ and pvr217 ) and five conferring resistance ( pvr21 , pvr22 , pvr24 , pvr27 , pvr29 ) . The eight previously reported alleles represented 87 out of the 109 pvr2/eIF4E1 sequences ( i . e . 79 . 8% ) obtained in this study ( Fig 2 ) . The 3 new alleles ( named pvr223 to pvr225 ) were characterized by single ( pvr223 and pvr224 ) or double ( pvr225 ) mutations relative to the reference allele pvr2+ ( Fig 2 ) . Interestingly , two of the three amino acid changes identified in these new alleles involved new polymorphic sites in comparison with previously reported alleles ( codons 40 and 105 , Fig 2 ) . The three new alleles were identified in wild populations , allele pvr223 was identified in CPA represented by only one sequence , and alleles pvr224 and pvr225 were identified in AZP , representing 21 out of the 24 sequences ( 87 . 5% ) from this biogeographical province ( Fig 2 ) . A minimum spanning network ( MSN ) connecting all pvr2/eIF4E1 alleles in the chiltepin population ( Fig 3 ) showed that the tomato orthologous pot-1+/eIF4E used as outgroup was connected to the pvr1+ allele , which is the root of the network . The MSN also shows that most pvr2/eIF4E1 alleles were connected by steps of just one amino acid substitution . Interestingly , the new allele pvr223 corresponds to one of the most parsimonious putative intermediates described in Moury et al [44] to connect pvr2+ to pvr29 . However , one intermediate ( labelled “1” in the network ) , needed to connect pvr223 to pvr29 is still missing , and sequence comparison of all previously described pvr2/eIF4E1 alleles [22 , 23 , 45] did not reveal any sequence corresponding to this intermediate . MSN analysis demonstrated that the mutation D205G occurred at least twice in the evolution of pvr2/eIF4E1 in chiltepin . To test if the new pvr2/eIF4E1 alleles identified in the chiltepin population were not impaired in the essential eIF4E1 function in mRNA translation , we analysed their ability to complement the eIF4E knockout yeast strain JO55 as in Charron et al [23] . Assays showed no growth difference in the selective medium between the yeasts complemented with the fully functional pvr2/eIF4E1 susceptibility allele pvr2+ and the newly described ones ( S2 Fig ) , strongly suggesting that alleles pvr223 , pvr224 and pvr225 are functional in translation . Next , for all the pvr2/eIF4E1 alleles identified in chiltepin populations we analysed the interaction between eIF4E1 and viral VPg , as in the interaction of pepper with Tobacco etch virus ( TEV ) and Potato virus Y ( PVY ) there is strong correlation between absence of interaction and resistance . The physical interaction between the 11 pvr2/eIF4E1 proteins encoded and the VPg of the avirulent PVY-LYE84 isolate was analysed using yeast two-hybrid ( Y2H ) system . Differences of growth on selective medium were observed for yeast transformed with the constructs containing the different pvr2/eIF4E1 proteins and PVY-LEY84 VPg ( Fig 4 , S3 Fig ) , which confirmed the interaction pattern reported for the previously characterized alleles , i . e . interactions between the pvr2/eIF4E1-VPg for pvr2+ and pvr1+ susceptibility alleles , and no interaction for the resistance alleles pvr21 to pvr29 . The proteins encoded by the pvr217 , pvr224 and pvr225-alleles interacted with the PVY-LYE84 VPg , suggesting that they are susceptibility alleles . In contrast , the eIF4E1 encoded by pvr223 did not , suggesting it is a resistance allele toward PVY-LYE84 ( Fig 4 , S3 Fig ) . A detailed analysis of the effects of the mutations present in these alleles relative to pvr2+ ( Fig 2 ) , which has been taken as reference for susceptibility [23 , 44 , 45] , showed that the single mutation V67E ( characterising pvr24 ) is sufficient to abolish the pvr2/eIF4E1-VPg interaction ( S3 Fig ) . Similarly , the mutation A68E defining pvr223 and also present in pvr29 , is sufficient to disrupt the pvr2/eIF4E1-VPg interaction ( S3 Fig ) . Conversely , the single mutations A15V , D40G , K71R and V105I did not impair that interaction ( S3 Fig ) . When these results were compared with a phylogeny of the pvr2/eIF4E1 haplotypes , it was apparent that the interaction between pvr2/eIF4E1 and PVY-LYE84 VPg was more stable for the alleles corresponding to the most ancestral haplotypes ( pvr1+ , pvr2+ , pvr217 , pvr224 and pvr225 ) than for the more derived pvr2 alleles ( pvr21 , pvr22 , pvr24 , pvr27 , pvr29 and pvr223 ( Fig 4 , see also Fig 3 ) . Interaction assays were also performed between the pvr2/eIF4E1 alleles identified in chiltepin populations and the VPg of TEV-HAT isolate , and demonstrated that the pvr2/eIF4E1-VPg interaction was efficient except for the pvr22 allele as previously reported [23] . Finally , chiltepin plants were inoculated with isolates PVY-LYE84 and TEV-HAT ( see Material and Methods ) in order to confirm the susceptibility/resistance phenotypes of the new alleles deduced from the Y2H assays . Since the pvr217 and pvr223 alleles are infrequent in chiltepin populations ( Fig 2 ) , the CPA populations where they were found were not included in this analysis . However , as alleles pvr224 and pvr225 are prevalent in AZP ( Fig 2 ) , 40 plants from seeds of the BER-W population were inoculated with each virus , and all of them showed symptoms 21 days after inoculation and high viral accumulation as detected by ELISA . The pvr2/eIF4E1 coding sequences were obtained from 10 randomly chosen plants among those inoculated with PVY-LYE84: 8 plants were homozygous for pvr224 , 1 plant was homozygous for pvr225 and 1 plant was a pvr224/pvr225 heterozygote , which confirmed that the pvr224 and pvr225 alleles confer susceptibility to PVY-LYE84 and TEV-HAT . Altogether , the described assays indicated that 6 out of 11 pvr2/eIF4E1 alleles found in chiltepin populations confer resistance to PVY-LYE84 infection . However , most pvr2/eIF4E1 sequences obtained in this study ( 83 out of 109 , i . e . 76 . 1% ) correspond to susceptibility alleles . When the distribution of resistance alleles in the sampled plants was analysed , it was found that 20 . 6% of plants would be resistant to PVY-LYE84 ( Table 3 ) . Resistance frequency significantly differed among biogeographical provinces ( for all populations: χ2 = 58 . 2 , P<10−4; for wild populations: χ2 = 29 . 5 , P<10−4; for cultivated populations: χ2 = 20 . 2 , P = 10−4 ) , being highest in populations from SMO and YUC ( for overall population: 84 . 2% and 25 . 0% , respectively; for wild populations: 66 . 7% and 10 . 0% , respectively; for cultivated populations: 92 . 3% and 100 . 0% , respectively; Table 3 ) . Interestingly , the frequency of resistant plants was significantly higher in cultivated populations than in wild ones ( 55 . 6% and 8 . 6% , respectively , χ2 = 25 . 4; P<10−4; S5 Table , Table 3 ) . Most previously reported mutations in the pvr2/eIF4E1 protein of Capsicum spp . resulting in potyvirus resistance were predicted to be in the cap binding pocket [23 , 54] . None of the amino acid substitutions detected in pvr2/eIF4E1 of chiltepin relative to pvr2+ , except D109N , were located at the sites interacting with the mRNA m7GTP cap or the eIF4G factor ( S4 Fig ) . Since no experimental structure is available for the eIF4E1 protein of Capsicum , a three-dimensional model was built in order to locate and to predict the structural effects in pvr2/eIF4E1 of the mutations identified in chiltepin . First , the amino acid sequence of the C . annuum var . annuum pvr2+ reference allele was aligned with those of eIF4E proteins with known crystal structure ( from Homo sapiens , Mus musculus , Triticum aestivum , and Pisum sativum ) . A phylogeny of these five eIF4E was reconstructed ( S5A Fig ) , and their secondary structures were compared ( S5B Fig ) , which showed a very high conservation except for the N-terminal domain which is longer in human , wheat and pepper ( S5B Fig ) . The non-conserved N-terminal domain was demonstrated to be flexible in yeast [55] , and our analysis confirmed that this domain is predicted to be disordered in human , wheat and in Capsicum ( S5B and S5C Fig ) . The 3D-models generated independently for the 11 pvr2/eIF4E1 alleles in Fig 2 confirmed , first , that the N-terminal domain of pvr2/eIF4E1 is flexible , and second , that the structural core of pvr2/eIF4E1 protein is not significantly altered by any amino acid substitution identified in chiltepin populations ( S6 Fig ) . With the single exception of residue 109 , which is placed in the β strand spanning amino acid positions 107–115 , all the analyzed mutations involve residues located at loops ( S6 Fig ) . Loops connecting secondary structure elements exhibit a great conformational flexibility and are usually exposed to the aqueous environment . Correspondingly , all mutations in pvr2/eIF4E1 alleles locate at the protein surface and , interestingly , they are close to the domain involved in the m7GTP cap recognition and far distant from the interface associated with eIF4G recruitment ( Fig 5 ) . It must be also noticed that being part of the disordered N-terminal region , the mutation A15V and to a lesser extent , the mutation D40G , should not alter significantly the essential functions of the pvr2/eIF4E1 protein . In addition to being localized at the surface of the protein ( Fig 5 ) , most amino acid substitutions ( 6 out of 10 ) involved steric changes associated to side chain volumes ( except for A15V , K71R , V105I and D109N mutations ) as well as noticeable local variations of the electrostatic potential in the protein surface ( Fig 6 ) . For the new alleles pvr223 , pvr224 and pvr225 , only the mutation A68E in pvr223 introduced a large change in electrostatic potential relative to pvr2+ , from a strong positive to a clearly negative potential in the external surface of the protein ( Fig 6 ) . It is interesting to note that there is a perfect correlation between all significant changes of electrostatic potential in pvr2/eIF4E1 and the disruption of its interaction with PVY VPg ( Fig 6 ) . Our results reveal that drastic changes in the local electrostatic potential of surface regions caused by some mutations ( e . g . neutral to negative in V67E or neutral to positive in L79R ) have a great impact in terms of disrupting the interaction with PVY-LYE84 VPg . Finally , as the N-terminal tails are disordered in the 3D models of all 11 pvr2/eIF4E1 alleles , variations among alleles in the electrostatic potential of those disordered regions are in part translated to nearby regions of the structural core . This is why the electrostatic potential of the structurally conserved core is not exactly the same in all alleles , which could indirectly alter the function of the pvr2/eIF4E1 protein . To estimate the incidence of potyvirus infection in chiltepin populations , we analysed by ELISA leaf samples of 955 plants collected in wild and cultivated populations between 2007 and 2010 . A total of 147 samples were ELISA positive , indicating a global Potyvirus incidence of 15 . 4% ( Table 4 ) . Potyvirus incidence varied significantly according to biogeographical province ( χ2 = 50 . 2 , P<10−4 ) , being highest in SON and AZP ( 23 . 8% and 24 . 1% , respectively ) , where pvr2/eIF4E1 resistance alleles were not identified . Potyvirus incidence varied significantly according to year ( from 8 . 5% in 2008 to 22 . 2% in 2010; χ2 = 15 . 0 , P = 0 . 002 ) . This temporal variation was solely due to wild populations , in which incidence varied according to year ( χ2 = 24 . 1 , P<10−4; Table 4 ) , which was not the case for the cultivated ones ( χ2 = 1 . 5 , P = 0 . 676; Table 4 ) , indicating a more constant challenge of virus infection in human-managed populations . Habitat , wild or cultivated , was not a factor on Potyvirus incidence ( χ2 = 0 . 3 , P = 0 . 597; Table 4 ) , however , the percentage of infected plants that showed disease symptoms ( mosaic , leaf distortion ) was significantly higher in cultivated than in wild populations ( 45 . 5% and 9 . 8% , respectively; χ2 = 24 . 6 , P<10−4 ) whereas it did not differ according to biogeographical province ( χ2 = 7 . 3 , P = 0 . 202 ) ( Table 5 ) . To identify which Potyvirus species infected chiltepin populations in Mexico , we amplified a highly conserved region of NIb gene from the most ELISA positive samples . Amplification was successful from 8 samples , 4 from AZP , collected in 2008 and 2009 , 3 from SON , 2007 , and 1 from CPA , 2009 , yielding two groups of sequences: those from SON and CPA were 99% identical to Pepper mottle virus ( PepMoV ) , and those from AZP were 83% identical to Tobacco etch virus ( TEV ) ( S7 Fig ) . Amplification and sequence determination of the genes encoding the VPg and CP in these samples confirmed the results based on the NIb fragment ( VPg: 97% and 77% of identity with PepMoV and TEV , respectively; CP: 98% and 83% of identity with PepMoV and TEV , respectively ) . The TEV-like potyvirus differed in 30 out of the 188 VPg amino acid positions from TEV but none of them included a site reported to be involved in pvr2 resistance-breaking ( S8 Fig ) .
In this study , the genetic diversity of the recessive resistance gene pvr2/eIF4E1 to potyviruses was analysed in the wild ancestor of domesticated pepper , Capsicum annuum var . glabriusculum ( chiltepin ) , with the aim of inferring the evolutionary pattern of a resistance locus involved in matching-allele ( MA ) -like interactions , and of evaluating the impact of incipient domestication on that pattern . For that , we compared the diversity of pvr2/eIF4E1 for wild and cultivated chiltepin populations in six biogeographic provinces within its distribution range in Mexico , and we determined the phenotype of susceptibility or resistance of pvr2/eIF4E1 alleles by the analysis of the interaction between pvr2/eIF4E1 and PVY-LYE84 VPg in a yeast two hybrid ( Y2H ) assay , and by the response of plants to viral inoculations . Infection requires the physical interaction between pvr2/eIF4E1 and the potyviral VPg , and it has been shown that there is a perfect correlation between pvr2/eIF4E1-VPg interaction-no interaction in Y2H and susceptibility-resistance in plants [22 , 23 , 51] . Also , the lack of physical interaction between pvr2/eIF4E1 and PVY-LYE84 VPg has been shown to be an efficient way of identifying resistance to potyviruses in Capsicum spp . However , interactions of particular pvr2/eIF4E1 resistance alleles with the VPg of other potyviruses may be more stable , resulting in susceptibility . Indeed , among the 25 previously described pvr2/eIF4E1 alleles , 23 confer resistance to PVY-LYE84 and only one to TEV-HAT [22 , 23 , 44 , 56] . In 109 pvr2/eIF4E1 full-length coding sequences obtained from 97 chiltepin plants , 17 haplotypes were identified at the nucleotide sequence level , which largely differed in frequency . The most frequent one , haplotype D , accounted for 28% of total sequences , and the other four haplotypes encoding the susceptibility allele pvr1+ , which according to the minimum spanning network ( MSN ) and phylogenetic analyses represents the basal state of pvr2/eIF4E1 in chiltepin ( Figs 2 and 3 ) , accounted for 44% of total sequences ( Fig 2 ) . Allele frequency also varied according to biogeographical province , so that the genetic diversity of pvr2/eIF4E1 coding sequence was 2 . 5–5 times higher in YUC and SMO than in the other four biogeographical provinces ( Table 2 ) . Also , the most basal pvr2/eIF4E1 haplotype ( G , Fig 2 ) was only identified in YUC . These results are consistent with the higher genetic diversity of chiltepin in YUC and SMO estimated from nuclear microsatellite makers ( SSRs ) [35] and with reports that identify the Yucatan peninsula and the areas around the Gulf of Mexico as centres of diversity and domestication of C . annuum [33 , 57] . Analyses of nuclear SSRs have shown a strong spatial structure of chiltepin genetic diversity according to biogeographical province [35] , which was also the case for pvr2/eIF4E1 , both when the coding sequence or the introns ( S3 Table ) were analysed . However , at odds with results from SSRs , which showed evidence of isolation by distance , the genetic distance among chiltepin populations at pvr2/eIF4E1 poorly correlated with geographical distance . The discrepancy between the spatial structure of the variation of putatively neutral genetic markers and of pvr2/eIF4E1 suggests that this gene is under selection associated with environment-specific factors . Although other factors may certainly be involved , selection on pvr2/eIF4E1 could be associated with resistance to potyviruses , as potyvirus incidence differs according to biogeographical province ( Table 4 ) . In agreement with the hypothesis that there is selection on pvr2/eIF4E1 for resistance , MSN and phylogenetic analyses indicate that pvr2/eIF4E1 has evolved to confer potyvirus resistance . Most pvr2/eIF4E1 alleles can be connected by just one amino acid substitution , and the allelic diversity found in chiltepin allowed to identify alleles , as pvr223 , which were predicted as most parsimonious intermediates in pvr2/eIF4E1 evolution by Moury et al [44] ( Fig 3 ) . Analyses showed that the susceptibility allele pvr1+ is at the base of pvr2/eIF4E1 phylogeny . From that state , evolution has proceeded towards decreasing the stability of the interaction between pvr2/eIF4E1 and PVY-LYE84 VPg , i . e . , towards resistance , as judged by yeast growth in a selective medium complemented by a Y2H assay interaction ( Fig 4 ) . The most supported node in pvr2/eIF4E1 phylogeny splits haplotypes encoding susceptibility alleles pvr1+ and pvr217 , from a cluster built of two less strongly supported subclusters , one including haplotypes corresponding to susceptibility alleles pvr224 and pvr225 , and the other including haplotypes corresponding to susceptibility alleles pvr2+ , from which all other haplotypes , encoding resistance alleles , derive ( Fig 4 ) . The pattern of evolution into this last cluster including both susceptibility and resistance alleles is compatible with a hypothesis of selection on pvr2/eIF4E1 resulting in the evolution of a variety of resistance alleles , as was concluded from the analysis of a set of 25 accessions of Capsicum annuum [23] . Interestingly , when the phylogeny of all reported pvr2/eIF4E1 alleles was reconstructed , resistance also appeared as a derived state , and evolution to resistance occurred in different phylogenetic clusters ( S9 Fig ) . Although support for the internal nodes of the phylogeny was not strong , the topology was consistent regardless of the method of phylogenetic reconstruction , or when the phylogeny was based on only first and second codon positions ( S9 and S10 Figs ) . Phylogenies derived from third codon positions ( S11 Fig ) did not present an informative pattern , supporting the significance of the main clusters in the other phylogenies . However , at odds with previous analyses [23] , when the alleles in our chiltepin data set are considered , evidence of selection for resistance is weaker: most ( 10/17 ) haplotypes encoded susceptibility alleles and a large number of pvr2/eIF4E1 polymorphisms in the chiltepin population were due to synonymous nucleotide substitutions , so that 7/17 haplotypes encoded the susceptibility alleles pvr1+ ( 5 haplotypes ) and pvr2+ ( 2 haplotypes ) . In contrast , only non-synonymous mutations were found in the data set analysed by Charron et al [23] . Accordingly , no site , including those that determine potyvirus resistance , was identified in our data set as being under positive selection , with the possible exception of codon 205 , in which the mutation D205G confers potyvirus resistance and occurred at least twice during pvr2/eIF4E1 evolution in chiltepin ( Fig 3 ) . Positive selection on codons involved in potyvirus resistance was only detected in a data set including a wide range of plant species [44] . In the chiltepin population the frequency of potyvirus resistance was moderate , as 21 . 6% of plants were predicted to be resistant to PVY-LYE84 , and 26 . 0% of pvr2/eIF4E1 sequences corresponded to resistance alleles ( Table 3 ) . Most resistance alleles were identified in SMO populations , and among resistance alleles only pvr23 and pvr24 were found in more than one biogeographical province ( Fig 2 ) . Interestingly , 55 . 6% of plants , and 62 . 5% of pvr2/eIF4E1 sequences were resistant to PVY-LYE84 in cultivated populations , as compared with 8 . 4% of plants and 7 . 8% of sequences in wild ones , and the higher proportion of resistance in cultivated populations held for the three biogeographical provinces in which resistance alleles/plants were found ( YUC , SMO and CPA , Table 3 ) . Four out of seven nucleotide sequence haplotypes encoding resistance alleles were found in cultivated populations . Heterozygosity at the pvr2/eIF4E1 locus was not different in wild or cultivated populations ( Table 1 , S5 Table ) , while for SSRs heterozygosity was higher in wild than in cultivated populations , and values were higher than for pvr2/eIF4E1 [35] . Nucleotide diversity at pvr2/eIF4E1 was higher in cultivated than in wild populations , whereas a significant decrease in genetic variation at neutral markers in cultivated populations was previously demonstrated in chiltepin [35] as it is commonly observed during plant domestication [26–28] . Also , there was a higher fraction of non-synonymous substitutions in cultivated populations than in wild ones , resulting in dN/dS ratios indicative of positive selection , as opposed with data from wild populations ( Table 2 ) . Last , DT values were positive for the region between codons 67 and 77 , which includes most determinants of potyvirus resistance ( Region I in Fig 2 ) , in cultivated but not in wild populations ( Fig 1 ) . Thus , all data taken together indicate that selection for potyvirus resistance is stronger in cultivated than in wild chiltepin populations , and results in higher diversification of the pvr2/eIF4E1 gene . It is noteworthy that both a ~55% frequency of potyvirus resistance and evidence of diversifying selection was found by Charron et al [23] in 25 accessions of C . annuum , mostly cultivated . High frequency of eIF4E-mediated resistance to the bymoviruses ( in family Potyviridae ) Barley yellow mosaic virus and Barley mild mosaic virus has also been found in accessions from domesticated barley varieties , with evidence of diversifying selection for resistance [24] . The eIF4E alleles conferring resistance to the potyvirus Pea seed borne mosaic virus were only found in domestic pea accessions , in spite of high variability of the locus in wild accessions [25] . So , these reports of other host-virus systems agree with a hypothesis of cultivation-associated selection for resistance at eIF4E . Although the ecological changes associated with cultivation are considered to favor the incidence of plant pathogens [58 , 59] , which is certainly the case for begomoviruses and other viruses infecting chiltepin in Mexico [38 , 60] , potyvirus incidence in chiltepin did not differ according to habitat ( Table 4 ) . However , potyvirus incidence varied less among years in cultivated than in wild populations ( Table 4 ) , indicating a more constant challenge of virus infection . Interestingly , in chiltepin populations localized in anthropic environments and tolerated but not cultivated by humans , i . e . “let-standing” populations [35] , potyvirus incidence varied temporally as in wild populations ( χ2 = 9 . 1 , P = 0 . 028 ) strongly suggesting that cultural practices favor a more constant potyvirus prevalence . More significantly , infection in cultivated populations was much more virulent , as 5 times more infected plants showed disease symptoms in cultivated than in wild populations ( Table 5 ) , and disease expression can be a good proxy of virulence in plant virus interactions [61–63] . Differences in selection for potyvirus resistance in the wild and under cultivation can be due to human-driven directional selection , as a response to strong symptom expression in cultivated populations , or to natural selection caused by cultivation conditions favoring a more constant and stronger effect of potyvirus infection . The role of natural selection during plant domestication is often overlooked and has been recently emphasized [29] . Also , the shorter generation time in cultivated populations , where chiltepin is managed as an annual crop , as compared with the 4–6 year perennial life span in the wild , could favor a higher selection rate per generation for resistance in the cultivated populations . We cannot at present evaluate the relative role of these contrasting factors on the evolution of potyvirus resistance in chiltepin wild and cultivated populations . The core structure of the pvr2/eIF4E1 protein would not be affected significantly by the amino acid substitutions found in chiltepin . However , substitutions that uncoupled the pvr2/eIF4E1-VPg interaction , resulting in resistance , were around the cap-binding pocket and strongly affected the electrostatic surface potential at this region , which is reasonable to expect would affect the binding of eIF4E to the cap of cellular mRNAs and , hence its efficiency in translation initiation . Thus , potyvirus resistance would have a cost even if the resistance alleles are fully functional for translation in yeast complementation assays . The location of amino acid substitutions on the protein structure , the low dN/dS values and the low frequency of resistance alleles in wild chiltepin populations , altogether support a hypothesis of functional constraints translating into costs limiting the evolution of pvr2/eIF4E1 towards potyvirus resistance . Capsicum plants carrying an eIF4E1 loss-of-function allele , which could provide evidence on eIF4E1 involvement in development/plant fitness and thus of mutation costs , are not available . A TILLING eIF4E1 knock out allele in cultivated tomato was not associated with obvious developmental defaults under greenhouse conditions [64] , although it might be detrimental under more stressful wild conditions . Costs of resistance have been often reported in GFG-like plant-pathogen interactions [10–12 , 65] , but are not a feature of the evolution of pure MA interactions . However , it is considered that real-world host-parasite interactions that mechanistically correspond to a MA model would fall within a continuum between pure MA and GFG models , in which partial infection with less successful parasite multiplication occurs , with correspondingly partial costs of resistance and infectivity [5 , 7] . This seems indeed to be the case of the pvr2/eIF4E1-mediated interaction between Capsicum and potyviruses , as infections largely differ in efficiency and costs of infectivity have been reported [66–68] . Our present results suggest that resistance costs could also determine the evolutionary dynamics of the Capsicum-Potyvirus interaction . The evolution of dominant resistance genes ( R genes ) of plants to cellular pathogens , which are involved in GFG-like interactions , has been analysed extensively . Data indicate that R genes are hypermutagenic and often under balancing selection [21 , 69–72] . The present work focuses on the analysis of the evolution of a recessive resistance gene involved in a MA-like interaction in populations of a wild plant . It also compares evolutionary dynamics between plant populations under different levels of human management . Notably , results show a quite different pattern depending on the level of human management of the habitat . While there is no evidence of high genetic variation or of selection on pvr2/eIF4E1 in wild chiltepin populations , as often reported for R genes [21 , 69–72] , there is evidence of selection on pvr2/eIF4E1 for potyvirus resistance in the cultivated populations , which is compatible with a hypothesis of balancing selection maintaining pvr2/eIF4E1 resistance diversity . These major results are perhaps unexpected as cultivation of chiltepin is recent and has not yet resulted in domestication or in obvious phenotypic changes , and the cultivated populations here analysed are not genetically differentiated from sympatric wild ones according to the variation of nuclear SSRs markers [35] . It is widely accepted that human management of plant habitats heavily influence the epidemiology of plant pathogens , including plant viruses [59 , 73] , as has been shown for viruses infecting chiltepin [38 , 60] . This study shows that human management of the habitat may also have a deep impact on the evolution of plant-pathogen interactions , an underexplored topic in need of more research .
Chiltepin plants were sampled during the summers of 2007–2010 at different sites over the species distribution in Mexico [35] . Plant samples were collected from chiltepin populations growing in a variety of habitats under different levels of human management [35] . For analyses of the pvr2/eIF4E1 gene we focused on those from the most extreme levels of human management , i . e . the wild and cultivated populations . Plants grown from seeds in fruits purchased at local markets were also analysed , and were considered here as from wild populations , if ( i ) the people selling the fruits claimed that they had been collected from local wild chiltepin populations and ( ii ) after their genetic characterization based on the polymorphisms of nine microsatellite markers [35] , those market populations were indeed shown to be related to the local wild populations . Thus , for analyses of the pvr2/eIF4E1 gene , we considered a total of 25 populations , 16 wild and 9 cultivated , ( S1 Table ) from six biogeographical provinces of Mexico: Yucatan ( YUC ) , Eastern side of the Sierra Madre Oriental ( SMO ) , Altiplano Zacatecano-Potosino ( AZP ) , Costa del Pacífico Sur ( CPS ) , Costa del Pacífico ( CPA ) , and Sonora ( SON ) [74] . A larger set of samples from populations growing in all the habitats ( wild , cultivated and let-standing populations ) [35] was used to evaluate Potyvirus incidence according to biogeographical province , habitat and year of sampling . For analysis of the pvr2/eIF4E1 gene total nucleic acids were extracted from leaves as in González-Jara et al [35] . The pvr2/eIF4E1 gene is constituted of 5 exons of 278 , 166 , 126 , 66 and 51 nucleotides ( nt ) , respectively , separated by 4 introns of more than 3500 nt , 110 nt , 1143 nt and 83 nt , respectively [75] . To amplify both introns and exons of the pvr2/eIF4E1 gene , two different PCRs were run directly on the total nucleic acid extracts , using the Phusion High-Fidelity DNA Polymerase ( New England Biolabs , MA , USA ) . The first PCR was performed with primers F-eIF4E . Full ( ATGGCAACAGCTGAAATGGAG ) and R-eIF4E . int1 ( CCCCGAGAATCTTAGTAGCTCA ) , designed to amplify a 756 nt fragment including pvr2/eIF4E1 exon 1 and the 5’ most 403 nt of intron 1 . Conditions for this PCR were 98°C for 30 sec , and 35 cycles of 98°C for 10 sec , 56°C for 30 sec and 72°C for 25 sec . The second PCR was performed using primers F-eIF4E . ex2 ( TGCTTACAATAATATCCACCACCC ) and R-eIF4E . 3’UTR ( CACAAGGTACTCAAACCAGAAGC ) , designed to amplify a 1848 nt fragment including the four other exons of pvr2/eIF4E1 and introns 2 to 4 . Conditions for this PCR were 98°C for 30 sec , and 35 cycles of 98°C for 10 sec , 54°C for 30 sec and 72°C for 1 min . Primers F-eIF4E . Full and R-eIF4E . int1 were also used to obtain the full nucleotide sequence of the amplicon from the first PCR . To determine the nucleotide sequence of the amplicon from the second PCR , primers F-eIF4E . ex2 , R-eIF4E . int3 ( CCCCTTCATCTATAAGCATATTTC ) , F-eIF4E . int3end ( GATGGTCTCAAGGGTTATGTGTC ) and R-eIF4E . 3’UTR were used , in order to obtain the complete sequence of exons 2 , 3 , 4 and 5 , and of introns 2 and 4 , and two partial sequences of intron 3 ( 5’ fragment: 293 nt; 3’ fragment: 547 nt ) . The pvr2/eIF4E1 coding sequence was then deduced from the exon sequences . Sequence analyses identified plants heterozygous for the pvr2/eIF4E1 gene . Sequence determination in heterozygotes was done after RT-PCR amplification of pvr2/eIF4E1 coding sequences and/or cloning of the DNA amplicons in pCRII ( TA Cloning Kit Dual Promoter , Invitrogen , Carlsbad , CA , USA ) . RT-PCR amplification of pvr2/eIF4E1 coding sequences was also used to identify the pvr2/eIF4E1 allele ( s ) present in virus-inoculated plants ( see below ) . In this case , the RT step was performed with the SuperScript III Reverse Transcriptase ( Invitrogen ) according to the manufacturer’s recommendations using primer R-eIF4E . 3’UTR , followed by a PCR amplifying the cDNA corresponding to the full coding sequence of pvr2/eIF4E1 with the primers F-eIF4E . Full and R-eIF4E . 3’UTR ( PCR conditions: 98°C for 30 sec , and 35 cycles of 98°C for 10 sec , 53°C for 30 sec and 72°C for 25 sec ) . Nucleotide sequences were aligned to maintain the reading frame using CLUSTAL-W [76] as implemented in Mega 6 [77] . Differences in heterozygous plants at the pvr2/eIF4E1 locus , in haplotype richness and in resistance frequency between populations , regions or habitat were assessed by the analysis of contingency tables using the Fisher exact test . Genetic diversity within and between populations , biogeographical provinces or levels of human management were estimated using the Kimura 2-parameter model , with standard errors of each measure based on 1000 replicate bootstraps , as implemented in Mega 6 . Differences in nucleotide diversity of the virus populations among biogeographical provinces and between habitats were tested by analysis of molecular variance ( AMOVA ) , as implemented in Arlequin v . 5 . 3 . 1 . 2 [78] . Differences in dN/dS values were considered to be significant if the mean value of one estimate fell outside of the 95% CI values of another , indicating that these dN/dS values were drawn from different distributions . AMOVA calculates the FST index explaining the between-groups fraction of total genetic diversity . Significance of these differences was obtained by performing 1000 permutations . Tajima’s D ( DT ) and sliding window analyses were conducted using DnaSP v . 5 . 10 [79] . Mantel correlation tests between geographic and genetic distance matrices were performed to test the isolation-by-distance hypothesis [80] in wild chiltepin populations using the web service http://ibdws . sdsu . edu/~ibdws/ [81] . We used the geographic distance matrices obtained in González-Jara et al [35] . Geographical and genetic distances between pairs of populations were log transformed , and 1000 permutations were performed to assess the significance of the correlations . We used the median-joining network method implemented in the Network version 4 . 611 software ( available at www . fluxus-engineering . com ) [82] to reconstruct the minimum spanning network ( MSN ) connecting all chiltepin pvr2/eIF4E1 alleles identified at the amino acid level . Phylogenetic relationships were reconstructed by the Neighbor-Joining method as implemented in Mega 6 [77] and incorporating the best-fitted nucleotide substitution model ( F81 model ) determined by jModelTest 0 . 1 . 1 [83] . The sequence of the Potyvirus susceptibility allele pot-1+ from tomato ( Solanum lycopersicum , accession number AY723733 ) was used as outgroup . Phylogenies were also reconstructed by Maximum Likelihood and by Maximum Parsimony using Subtrees Pruning and Regrafting method as implemented in Mega 6 with similar results . The ratio of non-synonymous ( dN ) to synonymous ( dS ) substitutions over the pvr2/eIF4E1 coding sequences from chiltepin populations was estimated by the Pamilo-Bianchi-Li method as implemented in Mega 6 . The dN/dS ratio was also estimated at individual codons in the pvr2/eIF4E1 coding sequences , using different methods implemented in the HYPHY program ( SLAC , Single Likelihood Ancestor Counting; FEL , Fixed Effects Likelihood; IFEL , Internal Fixed Effects Likelihood; REL , Random Effects Likelihood; FUBAR , Fast Unbiased Bayesian Approximation ) [84–87] to determine whether each of the 228 codons of pvr2/eIF4E1 were under negative ( dN/dS<1 ) , neutral ( dN/dS = 1 ) , or positive ( dN/dS>1 ) selection . These analyses were performed after confirmation of the absence of recombinant sequences in our dataset by two methods implemented in the HYPHY program ( SBP , Single Breakpoint Recombination; GARD , Genetic Algorithms for Recombination Detection ) [86] and using the tree topology previously obtained for pvr2/eIF4E1 . The Saccharomyces cerevisiae strain JO55 [cdc33-D LEU2 leu2 ura3 his3 trp1 ade2 ( YCp33supex-h4E URA3 ) ] [88] , carrying a disrupted endogenous eIF4E gene ( cdc33 ) , was used as in Charron et al [23] to verify the functionality of the pvr2/eIF4E1 allelic variants identified in chiltepin populations . The coding sequence of the pvr2+ allele was cloned into the p424GBP/TRP1 glucose-dependent vector , and all pvr2/eIF4E1 allelic variants were obtained by mutagenesis of this construct using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene , Agilent Technologies , Santa Clara , CA , USA ) . Each construct was sequenced to confirm the presence of the introduced mutations and then independently used to transform S . cerevisiae strain JO55 . After transformation , yeast cells were grown in appropriate selective nutrient drop-out media containing 2% glucose . Control transformations were performed with no DNA ( untransformed yeast JO55 ) and empty p424GBP/TRP1 plasmids ( negative controls ) , and with p424GBP/TRP1::At-eIF4E ( eIF4E form of Arabidopsis thaliana , At4g18040 ) as a positive control . After transformation , yeast colonies were grown to stationary phase , were suspended in sterile water , and then were adjusted to an OD600nm of 5 . 10−2 , 5 . 10−3 , and 5 . 10−4 before spotting 10 μl aliquots onto the appropriate media in order to test for their ability to complement the lack of endogenous eIF4E at 30°C [89] . For each pvr2/eIF4E1 allelic variant , 3 independent colonies were randomly selected to perform the complementation assay . The Matchmaker GAL4 two-hybrid system 3 ( Clontech , Mountain View , CA , USA ) was used according to the manufacturer’s recommendations to evaluate the interaction of the proteins encoded by the pvr2/eIF4E1 allelic variants with the potyviral VPg . The constructs previously developed by Charron et al [23] were used . The eIF4E1/pvr2+ coding sequence was cloned in-frame with the GAL4 activation domain into the pGADT7 vector ( Clontech , Mountain View , CA , USA ) , and all pvr2/eIF4E1 allelic variants were obtained by mutagenesis with the QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) . All the constructs were sequenced to confirm the presence of the introduced mutations before yeast transformation . The VPg of PVY ( avirulent isolate LYE84 ) [90] and of TEV ( avirulent isolate HAT ) [48] were cloned in-frame with the GAL4 binding domain into the pGBKT7 vector , respectively [23] . The pGADT7- and pGBKT7-derived vectors were transformed into AH109 and Y187 yeast strains , respectively , which contain two independent reporter genes , HIS3 and ADE2 , to confer histidine and adenine auxotrophy , respectively , driven by hybrid GAL4 promoters . After yeast mating , double-transformed yeast colonies were grown to stationary phase , were suspended in sterile water , and then were adjusted to an OD600nm of 5 . 10−2 before spotting 10 μl aliquots onto various selective media including synthetic medium lacking leucine and tryptophan ( hereafter named -LW ) and medium lacking leucine , tryptophan and histidine ( -LWH ) . Plates were incubated at 30°C , and yeast growth was checked daily from 2 to 7 days after spotting . The yeast growth on the selective–LWH medium reflects the pvr2/eIF4E1-VPg physical interactions . Empty pGADT7 and pGBKT7 vectors were used as negative controls and interaction between murine p53 and SV40 large T antigen as positive controls . Three independent yeast-two hybrid assays were performed , in which 3 independent colonies of each pvr2/eIF4E1-VPg combination were randomly selected . For complementation and yeast-two hybrid assays , growth intensities were monitored with ImageJ software [91] , and raw data were normalized to positive and negative controls and expressed as a percentage of the growth of the reference yeast colonies ( transformed with p424GBP/TRP1::eIF4E1/pvr2+ for complementation assays , and co-transformed with pGADT7::eIF4E1/pvr2+ and pGBKT7::VPg-PVY for yeast two-hybrid assays ) as previously described in Hébrard et al [92] . The secondary structure of eIF4E proteins used in this study from Capsicum annuum pvr2+ allele; Triticum aestivum , 2IDR; Pisum sativum , 2WMC; and the mammalian eIF4Es used as outgroup from Homo sapiens , PDB ID: 4DT6; Mus musculus , 1L8B [54 , 93–95] was predicted using the server NPS , which deduced the consensus secondary structure of protein from 12 different methods ( http://npsa-pbil . ibcp . fr ) [96] . The tertiary structure of all the pvr2/eIF4E1 alleles identified in chiltepin populations was modelled with the Iterative Threading ASSEmbly Refinement ( I-TASSER ) hybrid method [97–99] . Starting from an amino acid sequence , I-TASSER first generates 3D atomic models from multiple threading alignments and iterative structure assembly conducted by Monte Carlo simulations under an optimized knowledge-based force field . The lowest free-energy conformations are identified by structure clustering and final atomic structure models are constructed from the low-energy conformations by means of a two-step atomic-level energy minimization approach . The correctness of the models is assessed by a confidence score ( C-score ) and a measure of structural similarity ( TM-score ) . In all cases , the 3D structures were constructed from scratch without resorting to previous models of other alleles . Among the five models predicted by I-TASSER , that having the best values of both C-score and TM-score was finally selected . The main pvr2+ structure had C-score = 0 . 09 ( C-score is typically in the [−5 , 2] range , with a higher value meaning a model with higher confidence ) and TM-score = 0 . 73 ± 0 . 11 ( a TM-score > 0 . 5 indicates a model of correct topology ) . For the remaining alleles , C-score ranged from -1 . 57 and +0 . 28 and TM-score ranged between 0 . 52 ± 0 . 15 and 0 . 75 ± 0 . 10 so that all the 3D models presented here for the different pvr2/eIF4E1 alleles may be considered as having significant confidence and being topologically correct . The 3D model structures were first visualized and analyzed with Swiss-PdbViewer 4 . 1 . 0 [100] , software which was also used for rendering van der Waals ( VdW ) surfaces , obtaining pairwise structural superpositions and computing the corresponding root mean square deviation ( RMSD ) values . All structure models of pvr2/eIF4E1 alleles showed an N-terminal unstructured segment spanning the first 45–50 residues in their amino acid sequences . To further assess this result , we applied the following predictors of protein disorder: DisEMBL [101] , DISOPRED [102] , and IUPred [103] to the amino acid sequence of the main pvr2+ allele . Given that they employ disparate algorithms based on rather different assumptions , their close agreement in predicting disorder for segments 1–44 ( DisEMBL ) , 1–50 ( DISOPRED ) , and 1–45 ( IUPred ) lend further support to the structural models generated by I-TASSER . Poisson-Boltzmann ( PB ) electrostatic potentials mapped onto the protein surface of all the pvr2/eIF4E1 alleles were computed by solving the PB equation with APBS 1 . 4 [104] using AMBER99 [105] atomic charges and radii assigned with PDB2PQR 1 . 7 [106] . The nonlinear PB equation was solved at 298 . 15 K and 0 . 150 M ionic concentration in sequential focusing multigrid calculations in 3D meshes of 1603 or 1923 points with step sizes about 0 . 35 or 0 . 50 Å depending on the particular pvr2/eIF4E1 allele . Dielectric constants 4 for proteins and 78 . 54 for water were used . The output of PB electrostatic potentials thus computed were obtained in scalar OpenDX format and these numerical meshes were then mapped onto molecular surfaces of proteins and rendered with PyMOL 1 . 6 ( PyMOL , Schrodinger , LLC ) . PB electrostatic potential values are given in units of kT per unit charge ( k , Boltzmann's constant and T , absolute temperature ) . All plants were grown under greenhouse conditions and transferred into growth chambers before inoculation ( 16h light/8h dark; 24°C/18°C ) . Chiltepin plants were mechanically inoculated at the cotyledon stage with PVY-LYE84 ( pathotype PVY-0 ) and TEV-HAT [48 , 90] as previously described [107] . The C . annuum accessions Yolo Wonder ( pvr2+ homozygous , susceptible to PVY-LYE84 and TEV-HAT ) and Florida ( pvr22 homozygote , resistant to PVY-LYE84 and TEV-HAT ) were used as susceptible and resistant controls , respectively . Plants mock-inoculated with buffer were used as negative controls . Systemic infection was assessed by determining the presence/absence of symptoms on non-inoculated leaves and confirmed by DAS-ELISA using PVY or TEV antibodies . Infection by Potyvirus species in natural chiltepin populations was detected by DAS-ELISA , using the complete kit of detection PSA 27200/0288 according to the manufacturer’s recommendation ( AGDIA , Elkhart , IN , USA ) . This kit is based on the broad reactivity of a monoclonal antibody reacting to a highly conserved amino acid sequence on the coat protein of the Potyvirus genus . A total of 955 plants from 24 wild and cultivated populations were analysed in this way , plus 238 plants from let-standing populations . Differences in potyvirus incidence or symptom frequency in infected plants were assessed by the analysis of contingency tables using the Fisher exact test . The presence of virus in the ELISA-positive samples was confirmed by RT-PCR using the potyvirus-specific degenerated primers designed by Zheng et al [108] , which amplify a region of the NIb gene ( positions 7619–7968 ) highly conserved between Potyvirus species . Once the Potyvirus species was identified by NIb sequencing , species-specific primers bordering the VPg and the CP were designed . These primers were: for PepMoV , F-PepMoV . VPg: GTGCATCACCAGTCCAAGTCTT and R-PepMoV . VPg: CAGTCAACTTGCAAACAGTTTGG , F-PepMoV . CP: GCTGACTTGGCATCTGAAGGA and R-PepMoV . CP: TTCATCCCAGAGACCACATCAG; for TEV-like virus , F-TEVlike . VPg: GTATCATCCAAGACTTCAATCACCTGGAAAC and R-TEVlike . VPg: GATGTTGTGTGCCCATCAGATTCATTC , F-TEVlike . CP: CACAGCTTGCAGARGAAGGAAAGGC and R-TEVlike . CP: CTTAAAAGCGGAAAGCAAAGACACGC ) . | Viruses cause plant diseases , whose severity is considered to increase under plant cultivation . Hence , it is highly relevant to understand the genetics of plant virus resistance , and its variation in wild and cultivated plants . Analyses of plant pathogen resistance have focused on R proteins , which recognize pathogen molecules triggering defenses according to a gene-for-gene interaction . Alternatively , infection may require the interaction of plant and pathogen molecules , mutations impairing this interaction resulting in recessive resistance according to a matching-alleles model . We analyse here the variation of a recessive resistance gene in wild and cultivated populations of a plant , focusing on chiltepin , a wild pepper currently undergoing incipient cultivation in Mexico . The pvr2 gene encodes the translation initiation factor eIF4E1 , which must interact with the viral VPg for potyvirus infection . A high genetic variation was found for pvr2/eIF4E1 but , at odds with reports for R genes , there was no evidence for selection of resistance in wild chiltepin populations . However , data supported selection for resistance in cultivated populations , in spite of no phenotypic differences between wild and cultivated plants , and similar potyvirus incidences . Results demonstrate that cultivation has profound effects on the diversity and evolution of resistance . | [
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"biology... | 2016 | Human Management of a Wild Plant Modulates the Evolutionary Dynamics of a Gene Determining Recessive Resistance to Virus Infection |
Cardiomyopathy is the main clinical form of Chagas disease ( CD ) ; however , cerebral manifestations , such as meningoencephalitis , ischemic stroke and cognitive impairment , can also occur . The aim of the present study was to investigate functional microvascular alterations and oxidative stress in the brain of mice in acute CD . Acute CD was induced in Swiss Webster mice ( SWM ) with the Y strain of Trypanosoma cruzi ( T . cruzi ) . Cerebral functional capillary density ( the number of spontaneously perfused capillaries ) , leukocyte rolling and adhesion and the microvascular endothelial-dependent response were analyzed over a period of fifteen days using intravital video-microscopy . We also evaluated cerebral oxidative stress with the thiobarbituric acid reactive species TBARS method . Compared with the non-infected group , acute CD significantly induced cerebral functional microvascular alterations , including ( i ) functional capillary rarefaction , ( ii ) increased leukocyte rolling and adhesion , ( iii ) the formation of microvascular platelet-leukocyte aggregates , and ( iv ) alteration of the endothelial response to acetylcholine . Moreover , cerebral oxidative stress increased in infected animals . We concluded that acute CD in mice induced cerebral microvasculopathy , characterized by a reduced incidence of perfused capillaries , a high number of microvascular platelet-leukocyte aggregates , a marked increase in leukocyte-endothelium interactions and brain arteriolar endothelial dysfunction associated with oxidative stress . These results suggest the involvement of cerebral microcirculation alterations in the neurological manifestations of CD .
Chagas disease ( CD ) , which is caused by the protozoan Trypanosoma cruzi ( T . cruzi ) , is endemic in Latin America and affects approximately 10 million people worldwide [1] . Cardiomyopathy is the main clinical manifestation of CD , but digestive and neurological forms can also occur [2] . Meningoencephalitis is an important manifestation of acute CD in children under 2 years of age [3] , and it is also frequently observed in immunosuppressed patients suffering from acute CD reactivation [4] . Ischemic stroke is the main neurological manifestation observed in chronic CD [5] , and cognitive impairment and depression can also occur [6] , [7] . Moreover , experimental studies in mice have shown that depressive-like behavior is independent of central nervous system inflammation but is associated with high levels of systemic tumor necrosis factor ( TNF ) [8] . Acute CD has re-emerged in oral transmission outbreaks in countries where vector transmission has been controlled [9] . During acute CD , the peripheral inflammatory response is characterized by the presence of macrophages [10] , NK cells [11] and intense lymphocyte polyclonal activation [12] . This response is followed by the systemic synthesis of pro-inflammatory cytokines [13] , nitric oxide ( NO ) [14] and reactive oxygen species [15] . Microvascular alterations have been implicated in the pathogenesis of Chagas cardiomyopathy and include vascular constrictions , microaneurysms , dilatations and platelet aggregation , resulting in the formation of transient occlusive thrombi . These alterations contribute to myocytolytic necrosis followed by inflammatory infiltration and interstitial fibrosis . Moreover , vasoactive substances , including endothelin-1 and thromboxane , are involved in the modulation of vascular responses during T . cruzi infection , contributing to platelet aggregation , microvascular spasms and endothelial dysfunction [16] . Using intravital video-microscopy ( IM ) , our research group recently demonstrated that cerebral functional microvascular alterations are pathophysiologically relevant in models of systemic severe infectious syndromes , such as sepsis and malaria , in mice [17] , [18] . Moreover , IM has been used as an important tool with which to evaluate the microcirculation during T . cruzi infection , e . g . , using the hamster cheek pouch and cremaster muscle models [19]–[21] . In experimental models of CD , despite reports that inflammatory cells migrate to the cerebral tissue in a VLA-4+-VCAM-1-dependent manner [22] and that T . cruzi infects cerebral endothelial cells [23] , no studies have directly characterized the functional brain microcirculation during T . cruzi infection . In this paper , we present results from analyses of the consequences of acute CD on cerebral microcirculation in mice . We present evidence that acute infection by T . cruzi increases oxidative stress in the brain and causes severe cerebral vasculopathy , which may contribute to the neurological manifestations of CD .
All procedures were approved by the Oswaldo Cruz Foundation Animal Welfare Committee ( License numbers LW-40/13 and LW-74/12 ) and were consistent with the USA National Institutes of Health Guide for the Care and Use of Laboratory Animals ( NIH Publication No . 85-23 , revised 1996 ) . We used outbred male Swiss Webster mice ( SWM ) ( age 6 to 8 weeks ) , weight 18 to 20 g ) obtained from the Oswaldo Cruz Foundation Animal Facilities ( CECAL , Rio de Janeiro , Brazil ) . The animals were housed for at least 1 week before parasite infection under conditions of controlled light ( 12∶12 h light-dark cycle ) and temperature ( 22±1°C ) . The mice were randomly divided into two groups: a non-infected ( NI ) control group ( n = 5/experiment ) and a T . cruzi ( Y strain ) -infected experimental group ( n = 15/experiment ) . Infection was performed by intraperitoneal injection of 104 bloodstream trypomastigote forms of T . cruzi . Age-matched , non-infected mice were maintained under identical conditions . Two to three independent experiments were performed depending on the procedure . Parasitemia was individually assessed using the Pizzi-Brener method by direct microscopic counting of parasites in 5 µl of tail blood . Body weight and mortality were regularly monitored for twenty-two days post-infection ( dpi ) in three independent experiments ( n = 15 animals/experiment ) . We anesthetized animals from the NI control and T . cruzi-infected groups at 8 and 15 dpi by intraperitoneal injection with a mixture of xylazine ( 10 mg/kg ) and ketamine hydrochloride ( 75 mg/kg ) . The animals were tracheostomized and artificially ventilated with room air . We cannulated the jugular vein to allow the injection of fluorescent tracers . Body temperature was maintained at 37°C with a homeothermic blanket system . The animals were immobilized in a stereotaxic frame , and a cranial window was created by craniotomy with a high-speed drill to expose the cerebral microcirculation [24] . The animals were then placed on an upright fixed-stage of an intravital microscope with a mercury lamp ( Olympus BX51/WI , USA ) attached to a CCD digital video camera system . The microscopic field was continuously superfused with artificial cerebrospinal fluid at 37°C , pH 7 . 35 by an infusion pump ( Harvard apparatus plus , USA ) connected to catheters fixed over the opened cranial window . The superfusate was continuously aerated with 10% O2 , 6% CO2 and 84% N2 to maintain tension and a gas composition comparable to physiological pH and to avoid local inflammation . We performed two independent experiments with IM ( n = 4 animals/experiment ) . After the intravenous administration of 0 . 1 mL of 5% FITC-labeled dextran , microscopic images of the cerebral microcirculation were acquired by Archimed 3 . 7 . 0 software ( Microvision , Evry , France ) for online counting of capillaries using Saisam software ( Microvision , Evry , France ) . The functional capillary density , or the total number of spontaneously perfused capillaries ( i . e . , vessels with diameters less than 10 µm ) per square mm of surface area ( 1 mm2 ) , was determined by counting each capillary branch in 4 fields over a period of 4 minutes , as previously described in detail [25] . The capillaries measured approximately 5 to 10 µm in diameter , connected arterioles to venules and contained a single column flow of red blood cells [26] . These cells are biconcave-shaped cells with highly deformable membranes , which allows the cells to traverse narrow passages with small diameters ( e . g . , capillaries ) [26] . We labeled circulating leukocytes by injecting the mice with intravenous rhodamine-6G ( 0 . 3 mg/kg ) , which also stained circulating platelets [17] . The fluorescent leukocytes were made visible by epi-illumination through the cranial window . We observed five randomly selected venular segments ( 30 to 100 µm in diameter and 100 µm long ) for 60 seconds in each preparation . Leukocyte-endothelial interactions were evaluated by determining the number of ( i ) rolling leukocytes , defined as cells crossing the venular segment ( 100 µm ) at a speed less than that of the circulating red blood cells ( presented as the number of cells/min/100 µm ) , and ( ii ) leukocytes that adhered for at least 30 seconds to the venular wall . Considering that rhodamine-6G stained both platelets and leukocytes , we also investigated the percentage of microvessels exhibiting platelet-leukocyte aggregates ( PLAs ) [27] in five microscopic fields per animal . To characterize the oxidative stress in the brains of mice , we measured levels of thiobarbituric acid reactive species ( TBARS ) [28] . The brains of NI and T . cruzi-infected mice were homogenized in a cold phosphate buffer , pH 7 . 4 , with 2 , 6-bis ( 1 , 1-dimethylethyl ) -4-methylphenol ( BHT , final concentration 0 . 2% ) . Briefly , the samples ( 0 . 5 mL ) were mixed with an equal volume of 0 . 67% thiobarbituric acid and then heated at 96°C for 30 min . The TBARS level was determined by the absorbance at 535 nm . Results are presented as malondialdehyde ( ε = 1 . 56×105 M−1 cm−1 ) per milligram of protein ( BCA assay ) . We performed two independent experiments ( n = 4 animals/experiment ) . We evaluated vasodilator responses to the topical application of endothelium-dependent vasodilator acetylcholine ( Ach; 10−6 M ) in cerebral arterioles of both animal groups . The cranial window was suffused with Ach for five minutes , and the arteriolar diameters were measured before and after exposure to the vasoactive substance . Vascular responses are expressed as the percent ( % ) change from baseline . We expressed the results as the mean ± SEM for each group , and comparisons between groups were performed using unpaired t-tests or analysis of variance ( ANOVA ) followed by Bonferroni's multiple comparison test . Differences with p values of less than 0 . 05 were considered statistically significant . We used a commercially available , computer-based statistical package ( GraphPad InStat 5 . 0 , GraphPad Software Inc . , La Jolla , CA , USA ) for all calculations .
The analysis of trypomastigote forms of T . cruzi in the blood of animals revealed that the peak of parasitemia occurred at 8 dpi ( Figure 1A ) . At 8 dpi , T . cruzi infection induced significant changes in body weights in a time-dependent manner . At 22 dpi , the average weight of mice in the NI group was 31 . 4±1 . 9 g , while that of mice in the T . cruzi-infected group was 23 . 8±2 . 5 g ( p<0 . 001; Figure 1B ) . Cardiac parasitism and inflammation were also observed at 15 and 22 dpi ( data not shown ) . Only 20% of the infected animals survived to 22 dpi ( Figure 1C ) . The visualization of FITC-labeled dextran by intravital video-microscopy revealed that at 15 dpi , T . cruzi-infected mice presented cerebral microcirculatory collapse , characterized by a significant change in the pattern of microvascular perfusion ( Figure 2C ) compared with the NI group ( Figure 2A ) . This collapse was not observed in animals at 8 dpi ( Figure 2B ) . The quantification of perfused capillaries showed that at 15 dpi , the infected group presented a significant reduction of perfused capillaries ( 405±31 . 4 capillaries/mm2 ) compared with the NI controls ( 514±11 capillaries/mm2; p<0 . 05 ) and the infected animals at 8 dpi ( 535±31 . 2 capillaries/mm2; p<0 . 01; Figure 2D ) . The analysis of rhodamine-labeled leukocytes by intravital microscopy showed an increased number of leukocytes in the cerebral venular segment in T . cruzi-infected animals at both 8 ( Figure 3B ) and 15 ( Figures 3C and D ) dpi compared with the NI controls ( Figure 3A ) . At 15 dpi , microvascular PLAs were present in a large number of venules ( Figures 3C and D ) . The quantitative analysis showed 3±0 . 5 cells/min rolling in venules of NI animals ( Figure 4A ) and 6 . 3±0 . 8 cells/min rolling at 8 dpi ( p<0 . 01 ) . This number further increased to 16±0 . 6 cells/min at 15 dpi ( p<0 . 001 ) . Statistical analysis showed that the difference between 8 and 15 dpi was significant ( p<0 . 001 ) . As shown in Figure 4B , leukocyte adhesion in infected animals increased at 15 dpi ( 8 . 6±1 . 7 cells/min/100 µm ) compared with the NI group ( 1±0 . 2 cells/min/100 µm , p<0 . 001 ) and with the values in the infected mice at 8 dpi ( 0 . 4±0 . 2 cells/min/100 µm , p<0 . 001 ) . A high percentage of cerebral venules ( 56 . 5% ) in the infected group presented PLAs at 15 dpi , compared with 5% at 8 dpi ( p<0 . 05 ) , while the NI control group ( p<0 . 01 ) did not exhibit any microvascular PLAs ( Figure 4C ) . We measured malondialdehyde production in the brain of infected mice to assess changes in the local oxidative stress . Infected mice presented an increase in brain malondialdehyde production at 8 dpi , indicating increased oxidative stress ( Figure 4E , p<0 . 01 ) , which corresponded to the peak of parasitemia ( Figure 1A ) and to the initial increase in leukocyte rolling ( Figure 4A ) . At 15 dpi , the production of malondialdehyde returned to the control group level ( Figure 4E ) , which corresponded to the decrease in parasitemia . We evaluated the endothelial function of cerebral arterioles after the topical application of Ach to the cranial window . The acute T . cruzi infection significantly impaired the endothelium-dependent vasodilatation induced by Ach . In the NI group , the inner diameter ( ID ) of cerebral arterioles increased 8 . 3±1 . 7% from the baseline values , indicating a preserved endothelial function . In T . cruzi-infected mice , Ach induced a marked vasoconstrictor response of 5 . 4±3 . 3% at 8 dpi and 23 . 10±11 . 2% at 15 dpi ( p<0 . 05 compared with the NI group at 8 and 15 dpi; Figure 5A ) , which indicated cerebral endothelial dysfunction . Figure 5 presents representative images of cerebral arterioles before ( Figures 5B and D ) and after ( Figures 5C and E ) the application of Ach in infected animals at 8 and 15 dpi , showing that Ach induced vasoconstriction , reducing the arteriolar ID ( Figures 5C and E ) .
In the present work , we showed for the first time that experimental acute CD increases oxidative stress in the brain and induces cerebral microvasculopathy in mice , suggesting the involvement of these alterations in the pathophysiology of CD . We utilized SWM , which are susceptible to the Y strain of T . cruzi when infected with trypomastigote forms of this parasite . Our results showed that the infected animals had a peak of parasitemia at 8 dpi , body weight loss starting at 12 dpi and high mortality ( 80% ) at approximately 22 dpi; these results agree with observations of acute CD in previous studies [29] , [30] . Using IM , we observed significant alterations in cerebral microcirculation , such as functional microvascular rarefaction , increased leukocyte rolling and adhesion , a high number of microvessels presenting PLAs and significant endothelial dysfunction . Cerebral manifestations , such as meningoencephalitis , occur during the acute phase of CD , mainly in children or during the reactivation of the disease in immunosuppressed patients [4] , [31] . Furthermore , cognitive impairment [6] and depressive behavior have also been associated with chronic CD in humans [7] . Moreover , in experimental models of acute and chronic CD in mice , an association between systemic inflammation and depressive-like behavior was observed [8] . Despite the association of ischemic stroke with chagasic cardiomyopathy [5] , [32] , ischemic stroke also occurs in T . cruzi-infected patients without left ventricle dysfunction . In addition , small-vessel disease occurs in these patients , supporting the idea that stroke subtypes , other than cerebral embolism of cardiac origin , should be considered in CD patients . This idea suggests an association of this manifestation with microcirculatory alterations [33] . The involvement of microcirculation in the pathophysiology of chagasic cardiomyopathy is well established [16] . Nevertheless , cerebral microvascular alterations in CD are much less studied but potentially damaging . Previous reports showed endothelial activation [22] , [34] and the presence of T . cruzi nests in cerebral endothelial cells in experimental CD [23] . The advanced experimental method of the cranial window , which has been developed for intravital microscopy of the cerebral microcirculation , has been successfully used by different research teams , including our group . This technology allows the visualization of cerebral arterioles , venules and capillaries and has been used to evaluate functional capillary density [17] , [26] , [27] , [35] . In the present study , we observed notable cerebral functional capillary rarefaction , verified by a reduction in the number of perfused capillaries at 15 dpi , indicating microcirculatory collapse in the brain of infected mice . Abnormal cardiac microcirculation with focal vascular constriction , microaneurysm formation , dilatation and microvessel proliferation has also been demonstrated in T . cruzi-acutely infected animals [36] . Moreover , T . cruzi-infected mice showed a significant decrease in the flow of red blood cells in arterioles and venules of the cremaster muscle [21] . Vasoactive substances are involved in the modulation of the vascular response during T . cruzi infection [16] , [37]–[40] . Endothelin-1 is an endothelium-derived contracting factor [41] that participates in the microvascular dysfunction in CD [38] and is involved in the invasion of host cells by T . cruzi [20] . Recently , it has been demonstrated that the blockade of endothelin receptors increased the parasitemia and decreased the initial resistance of the central nervous system to T . cruzi infection in rats [42] . In this context , endothelin may be involved in the cerebral functional capillary rarefaction observed in the present study . Another vasoactive molecule considered to be a key regulator of CD pathogenesis is eicosanoid thromboxane . Thromboxane is a vasoconstrictor , promotes platelet aggregation , increases vascular permeability and is a potent pro-inflammatory molecule . High levels of thromboxane B2 and increased platelet aggregation were observed in T . cruzi-infected mice , suggesting that endothelial cell dysfunction and increased platelet reactivity could contribute to microvascular spasm and occlusion in acute CD [37] . Moreover , it was demonstrated that thromboxane A2 , produced by T . cruzi , accounts for most of the circulating thromboxane in infected animals [40] . Therefore , thromboxane may be implicated in the high occurrence of PLAs in cerebral microvessels observed in this study at 15 dpi , which could contribute to functional capillary rarefaction in the brains of animals . We also investigated the leukocyte-endothelium interaction in cerebral microcirculation . We observed a high number of leukocytes rolling and adhering to the cerebral venules of infected mice . At 8 dpi , there was already a significant increase in the number of rolling leukocytes , and this increase was even greater at 15 dpi . Leukocyte adhesion was quite pronounced at 15 dpi and correlated with the high number of inflammatory cells in the heart at the same time post-infection ( data not shown ) . Previous studies demonstrated the activation of the cerebral vascular endothelium by an increase in the expression of VCAM-1 in experimental acute CD and in immunosuppressed , chronically infected animals . In addition , the role of the VLA-4/VCAM-1 pathway in the establishment of meningoencephalitis induced by T . cruzi has been suggested [22] , [34] . Although we did not characterized the profile of the inflammatory cells , it was previously demonstrated that during experimental meningoencephalitis induced by T . cruzi , the infiltrating cerebral lymphocytes in the brain consisted mainly of CD8+ T cells [34] . T . cruzi induces an increase in the production of reactive oxygen species ( ROS ) in cardiomyocytes , which is enhanced by IL-1β , TNF-α and IFN-γ [43] . Moreover , the mitochondrial generation of ROS was observed in the myocardium of T . cruzi-infected mice [44] and of patients with CD [45] . Here , we evaluated the production of malondialdehyde , a marker of lipid peroxidation in the brain of mice , as an indirect measure of oxidative stress [46] . We found an increase in the production of malondialdehyde in the brain of infected animals at 8 dpi , which corresponds to the parasitemia peak . Because high parasitemia is associated with systemic inflammation [47] , our data suggest that oxidative damage could be associated with an initial inflammatory response to the parasite , which could lead to an increase in pro-inflammatory cytokines and chemokines in the blood , endothelial and inflammatory cell activation and the release of ROS in the brain during acute CD . The NADPH-oxidase system is a source of ROS in phagocytic cells , and malondialdehyde production can enhance the activity of the system , especially during the response to pathogens [48] . Furthermore , a recent study showed that oxidative stress contributes to the persistence of T . cruzi in mouse tissues [49] . The inflamed pro-thrombotic endothelium and the excess oxidative excess observed in the present study in the brain of T . cruzi-infected mice have also been known to be involved in the reduced vascular reactivity in other pathologies [50] . Therefore , we investigated cerebral endothelial function . Our results showed a severe alteration in the microvascular reactivity to acetylcholine stimulation in infected animals , characterized by the vasoconstriction of arterioles , suggesting damage to the cerebral microvascular endothelial layer . It is well known that the injury of endothelial cells can result in the direct action of acetylcholine on smooth muscle cells of the mural layer , resulting in arteriole contractions [51] . In fact , in the presence of endothelial dysfunction , muscarinic agonists produce vasoconstriction by direct M3 receptor activation [52] , [53] . Endothelial dysfunction was present at 8 dpi , when the cerebral oxidative stress increased and the leukocyte-endothelium interaction began , as observed by an increase in the number of rolling leukocytes . The endothelial dysfunction persisted to 15 dpi , concurrently with an increased number of adherent inflammatory cells , microvascular PLAs and cerebral capillary rarefaction . The NI mice showed a preserved endothelial-dependent response that was characterized by vascular dilatation in response to acetylcholine , suggesting the release of nitric oxide by endothelial cells after stimulation [54] . These results indicate that increased oxidative stress and the augmented leukocyte-endothelium interaction contribute to cerebral microvascular endothelial dysfunction and the reduction of functional capillary density in T . cruzi-infected animals . Our findings corroborate the results of previous studies that also observed alterations in the mechanisms involved in the regulation of vascular function in CD . Patients with CD without heart failure presented venous endothelial dysfunction in response to acetylcholine [55] . In another study , flow-mediated , endothelium-dependent vasodilatation and nitroglycerin-mediated vasodilatation of the humeral artery were evaluated in patients with chagasic cardiomyopathy . There were no differences in flow-mediated vasodilatation . However , the activity of nitroglycerin , which induces endothelium-independent vasodilatation , was lower in the patients , suggesting a dysfunction of vascular smooth muscle cells [56] . In conclusion , the results of the present study demonstrate that acute CD causes significant cerebral microvasculopathy as well as increased cerebral oxidative stress in mice . An acute T . cruzi infection leads to functional capillary rarefaction , an increase in rolling and adhered leukocytes , microvascular PLA formation and noticeable endothelial dysfunction in the cerebral microcirculation . Furthermore , our data support the idea that these cerebral microcirculatory changes may result in long-term consequences , contributing to neurological manifestations of chronic CD . Finally , the mechanisms involved in cerebral microvascular alterations and increased cerebral oxidative stress could be further investigated as novel therapeutic targets for the treatment of CD . | Chagas disease ( CD ) is a neglected tropical illness caused by the parasite Trypanosoma cruzi ( T . cruzi ) . It is endemic in Latin America and affects 10 million people worldwide . Meningoencephalitis occurs in children with acute CD and in immunosuppressed patients suffering acute CD reactivation . During the chronic phase , cerebral manifestations , including ischemic stroke and cognitive impairment , can also occur . Although microvascular alterations have been implicated in Chagas cardiomyopathy , the main clinical form of the disease , there is a lack of discussion in some studies regarding alterations of the cerebral microcirculation in CD . In the present study , we evaluated the functionality of the cerebral microcirculation in mice infected by T . cruzi . Utilizing an intravital video-microscope , we observed in the brain of infected mice a reduction in the number of perfused capillaries , an increased interaction between inflammatory cells and venules , the presence of microvascular platelet-leukocyte aggregates and alterations in the dilatation capacity of arterioles . Moreover , cerebral oxidative stress was increased in infected animals . We concluded that acute CD induced cerebral microvasculopathy . | [
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"pathogenesi... | 2014 | Acute Chagas Disease Induces Cerebral Microvasculopathy in Mice |
Cell-to-cell movement of plant viruses occurs via plasmodesmata ( PD ) , organelles that evolved to facilitate intercellular communications . Viral movement proteins ( MP ) modify PD to allow passage of the virus particles or nucleoproteins . This passage occurs via several distinct mechanisms one of which is MP-dependent formation of the tubules that traverse PD and provide a conduit for virion translocation . The MP of tubule-forming viruses including Grapevine fanleaf virus ( GFLV ) recruit the plant PD receptors called Plasmodesmata Located Proteins ( PDLP ) to mediate tubule assembly and virus movement . Here we show that PDLP1 is transported to PD through a specific route within the secretory pathway in a myosin-dependent manner . This transport relies primarily on the class XI myosins XI-K and XI-2 . Inactivation of these myosins using dominant negative inhibition results in mislocalization of PDLP and MP and suppression of GFLV movement . We also found that the proper targeting of specific markers of the Golgi apparatus , the plasma membrane , PD , lipid raft subdomains within the plasma membrane , and the tonoplast was not affected by myosin XI-K inhibition . However , the normal tonoplast dynamics required myosin XI-K activity . These results reveal a new pathway of the myosin-dependent protein trafficking to PD that is hijacked by GFLV to promote tubule-guided transport of this virus between plant cells .
Plant viruses are intracellular parasites that recruit numerous host factors for their replication and movement within plants . Virus cell-to-cell movement involves transport from replication factories to the cell periphery , passage through plasmodesmata ( PD ) interconnecting adjacent cells , and long-distance transport via the phloem vasculature [1] . All plant viruses encode one or more specialized movement proteins ( MP ) facilitating virus transport . The structurally and mechanistically diverse MP employ at least three different movement strategies . The first movement strategy is represented by Tobacco mosaic virus ( TMV ) MP that directly binds and chaperones viral RNA genome via modified PD [2]–[4] . The second movement strategy involves MP that heavily modify PD structure by forming tubules through which the assembled virions traverse PD [5] , [6] . The third type of movement strategies is used primarily by the filamentous viruses , which usually require more than one MP and capsid protein for efficient intercellular transport [7] . The longest known filamentous viruses , closteroviruses , have evolved the most complex machinery that includes a virion-associated movement device and a membrane-targeted MP [8] . Although a number of cellular factors that interact with MPs and/or are localized to PD have been identified , their functional relevance in intercellular transport processes remained largely hypothetical [9] . A new family of PD-resident proteins , Plasmodesmata Located Proteins ( PDLPs ) , was recently characterized in Arabidopsis thaliana [10] . PDLPs are type-I membrane proteins that traffic along the secretory pathway to reach the plasma membrane ( PM ) lining the PD interior . We have recently demonstrated functional significance of PDLP isoforms for movement of tubule-forming viruses including Grapevine fanleaf virus ( GFLV ) , an RNA nepovirus causing severe grapevine disease [11] . We showed that PDLPs act as receptors required for assembly of the PD-traversing tubules by the GFLV MP 2B . Inactivation of PDLPs resulted in defective tubule formation and GFLV transport . PDLPs appear to represent essential host components for the tubule-forming movement machinery , because the cell-to-cell movement of the evolutionary dissimilar pararetrovirus , Cauliflower mosaic virus ( CaMV ) , was also affected by PDLP down-regulation [11] . One of the central problems in virus transport research is the physical nature of virus translocation within and between cells . Two principal possibilities include diffusion through compartmentalized cytosol and/or endomembrane system and active transport involving cytoskeletal motility . A cytoskeleton-dependent transport route was described in several animal virus models [12] including microtubular motor-driven transport of Human immunodeficiency virus ( HIV ) [13] and actin tail-propelled transport of Vaccinia virus [14] . The transport mechanisms of plant viruses remain to be a matter of debate , ironically so for the first virus ever discovered , TMV . For the PD targeting of TMV ribonucleoprotein complexes , evidence has been provided for microtubule-dependent [15] , [16] and actomyosin-dependent [17] , [18] transport , as well as for diffusion in the endoplasmic reticulum ( ER ) network [19] . Although these mechanisms are not necessarily mutually exclusive , it seems that the growing number of plant viruses are reported to recruit actomyosin for moving their genomes , virions , or MPs to or through PD [20] . The actomyosin motility in plants , from algae to angiosperms , is driven by two classes of myosin motors , VIII and XI , which are evolutionary related to class V myosins present in protists , fungi , and animals [21] . The model plant Arabidopsis thaliana encodes 13 class XI and four class VIII myosins [22] . Class XI myosins function in the trafficking of Golgi stacks , peroxisomes , mitochondria , and ER streaming [23]–[25] . Because inactivation of Arabidopsis class XI myosins affects cell growth and plant development [26] , [27] , these molecular motors are likely to transport the secretory vesicles required for cell expansion . Although myosins VIII were proposed to associate with PD , ER , plasma membrane , and endosomes [28]–[30] , in the absence of genetic evidence , their functional significance remains a mystery . The first experimental support for actomyosin-dependent PD targeting of a viral protein was provided for a closteroviral Hsp70 ( Heat shock protein 70 ) homolog , a virion component required for viral movement [31] , [32] . It was also shown that Hsp70 localization to PD specifically relies on class VIII myosins [33] . Very recently , it was found that MP of a dissimilar tenuivirus also relies on myosins VIII for PD targeting [34] . In contrast , myosins XI were recently implicated in TMV movement [18] . In this study , we investigate the role of the actomyosin motility in PD-targeting of PDLP , and consequently , in tubule-guided cell-to-cell movement of GFLV . We demonstrate that myosins XI , but not VIII , mediate intracellular trafficking and PD targeting of the GFLV MP receptor PDLP . We show that inactivation of certain class XI myosins affects GFLV cell-to-cell movement . Furthermore , we explore the roles of myosins XI in the subcellular targeting of several compartment-specific fluorescent reporters . Taken together , our data delineate a specific , myosin XI-dependent , endomembrane transport pathway for PD-localised plant proteins that contributes to GFLV transport between the cells .
To determine if a functional actin cytoskeleton is required for cell-to-cell movement of GFLV , we applied the actin microfilament depolymerising agent Latrunculin B ( LatB ) [35] to Nicotiana benthamiana leaves before infection . GFLV cell-to-cell movement was assessed 3 days post inoculation ( dpi ) by measuring the size of infection foci of a recombinant GFLV encoding red fluorescent protein-fused reporter ( GFLV-RFP ) [11] . Box plot was used as statistical method to study the range of infection foci diameters in the different treatments . Figure 1A shows a ∼2 . 5-fold reduction in mean infection focus area in the LatB treated leaves compared to the control , indicating that GFLV spread requires an intact actomyosin motility system . Because the GFLV MP or small icosahedral virions were unlikely to induce actin tail formation similar to large poxviruses [14] , we assumed that the myosin motors were involved in virus intercellular movement . To address this possibility , we used dominant negative inhibition of myosin function via transient overexpression of the headless myosins that possess C-terminal globular tail domains . Because these domains are specifically involved in myosin cargo binding and motor domain activation [36] , [37] , their ectopic expression suppresses activity of the endogenous myosins . This approach was successfully used for the interference with the functions of myosins VIII and XI in N . benthamiana and Arabidopsis [23] , [24] , [33] . It is important that we expressed N . benthamiana myosin tails in this same plant species , because heterologous myosin expression often results in mislocalization ( Peremyslov VV and VV Dolja , unpublished data ) . The myosin tail-expressing and control leaves were inoculated with GFLV-RFP , and the resulting infection foci were measured at 3 dpi . We found that the inhibition of the myosins XI affected the size of the infection foci ( Figures 1B and C ) . The boxes for all myosin XI tails treatments are compressed in comparison with VIII-2 and control ( Figure 1B ) indicating less distribution around the median showing lower median . These results indicate an affect of the expression of myosin XI tails on virus cell-to-cell movement . Expression of the myosin XI-K tail had the most dramatic effect reducing virus cell-to-cell movement by factor 6 compared to the control . Similar , albeit milder effects were observed upon expression of the myosin XI-2 and XI-F tails ( Figures 1B and C ) . By contrast , virus movement was not significantly different from the control when myosin VIII-2 tails were expressed ( Figures 1B and C ) . The expression of the hemagglutinin epitope ( HA ) -tagged myosin VIII-2 , XI-K , XI-2 , and XI-F tails [23] was detected using immunoblot analysis and anti-HA antibodies . This analysis confirmed that the recombinant proteins had the expected sizes ( myosins VIII possess much shorter tails than those of myosins XI ) and similar levels of accumulation ( Figure 1E ) . The reduction in the size of infection foci could be attributed either to a defect in virus transport between cells , or to a reduction in virus replication in response to myosin tail expression . To address the latter possibility , we quantified GFLV-RFP fluorescence intensity normalised to the area in a large number of infection foci ( N ≥44 for each experimental variant ) . This analysis unequivocally demonstrated that there were no significant differences between the control and each of the myosin tail-expressing variants ( Figure 1D ) . We therefore concluded that the dominant negative inhibition of the myosin XI-K , and to a lesser extent , of myosins XI-2 and XI-F , specifically affected the cell-to-cell movement of GFLV . Because GFLV cell-to-cell movement occurs via tubules assembled by 2B MP [11] , [38] , we were interested to determine if tubule formation is impaired upon myosin XI tail expression . To this end , we used transient co-expression of the myosin tails and the GFP:2B that is able to form tubules [38] and assessed tubule formation using confocal laser scanning microscopy . As expected , no effect on tubule assembly was observed when GFP:2B was transiently co-expressed with the tail of myosin VIII-2 ( Figure 2A ) . Ectopic expression of the myosin XI-2 tail resulted in fewer as well as shorter tubules ( Figure 2B , Compare insets in figure 2A and B ) . The most conspicuous effect on tubule formation was observed upon expression of the myosin XI-K tail . As shown in Figure 2C , no discernible tubules were observed in this case . Instead , GFP:2B was distributed diffusely in the cortical cytoplasm and nucleus , attesting to a major disruption of not only the tubule assembly , but also GFP:2B localization at PD ( Figure 2C ) . Immunoblot analysis using 2B- and HA-specific antibodies confirmed co-expression of GFP:2B and each of the myosin tails ( Figure 2D ) . These data clearly indicated that functional myosins XI in general , and myosin XI-K in particular , are required for proper subcellular targeting of the GFLV MP , and subsequent formation of the PD-traversing tubules by this protein . We demonstrated previously that accumulation of PDLP isoforms at PD is crucial for tubule formation by GFLV MP and virus cell-to-cell movement [11] . To determine if PDLP trafficking along the ER-to-Golgi-to-PD pathway [10] requires actomyosin motility , we co-expressed GFP-tagged PDLP1 ( PDLP1:GFP ) and the spectrally distinct Golgi marker Man1:RFP [39] . Man1:RFP served as an internal control for Golgi motility in experiments using LatB to test whether PDLP1 movement is actomyosin-dependent . Additionally , the ATPase inhibitor 2 , 3 butanedione monoxime ( BDM ) reported to inhibit myosin activity [39] , [40] was applied to assess the role of myosins in PDLP1 trafficking to PD . As shown in Figure 3 , translocation of Man1:RFP ( Figure 3A ) and PDLP1:GFP-labelled bodies ( Figures 3B and video S1 ) under control conditions occurs along considerably overlapping tracks ( compare Figures 3A and B ) and with similar velocities of 1 . 64 µm/sec ± 0 . 18 and 2 . 01 µm/sec ± 0 . 32 , respectively ( Figure 3G ) . Strikingly , LatB treatment nearly abolished trafficking of both Man1:RFP ( Figure 3C ) and PDLP1:GFP ( Figure 3D ) . The resulting measured mean velocities of less than 0 . 11 µm/sec ( Figure 3G ) are attributed most likely to Brownian motion-dependent wobbling , cytosol dynamics due to the activity of microtubule-associated motors , and/or the drift of the entire specimen . Very similar results were obtained upon BDM treatment ( Figures 3E and G ) . Statistical analyses revealed highly significant velocity differences between control ( mock ) and LatB or BDM treatments ( t-test , p<0 . 01 , Figure 3G ) . Taken together , these results suggest that trafficking of PDLP1 bodies occurs via a route similar to that of Man1:RFP-labelled Golgi stacks , and is actomyosin-dependent . To investigate the potential myosin contributions to PDLP1 transport to PD , we co-expressed PDLP1:GFP with the tails of class VIII and XI myosins including VIII-1 , VIII-2 , VIII-B , XI-K , XI-2 , and XI-F . Figure 4A to C shows representative images of this analysis . The normal pattern of PDLP1:GFP localization to PD was observed in empty vector control and in the leaves expressing each of the three class VIII myosin tails ( Figure 4A ) , or the myosin XI-F tail ( not shown ) . In a sharp contrast , expression of the myosin XI-2 ( Figure 4B ) or myosin XI-K ( Figure 4C ) tails resulted not only in disruption of the specific PD targeting as seen by the absence of typical punctate labelling in the cell wall , but also in formation of multiple abnormal PDLP1:GFP aggregates in the cytosol ( Figures 4B and C ) . Three independent experiments revealed that approximately 60% of the epidermal cells expressing myosin XI-2 or XI-K tails presented such aggregates , whereas no PDLP1 aggregates were detected in any other experimental variants ( Figure 4D ) . To determine if PDLP1:GFP mislocalization and/or aggregation was due to excessive protein accumulation , we analyzed the steady-state levels of PDLP1:GFP and myosin tails using immunoblotting and GFP- or HA-specific antibodies , respectively . As expected , GFP-specific antibodies revealed a 62-kDa PDLP1:GFP-specific product in all samples except for the mock-infiltrated control [lane ( - ) in Figure 4E , top panel] . Although protein levels varied slightly between the different experimental conditions , no correlation between high levels of PDLP1:GFP expression and aggregate formation was observed . Indeed , highest PDLP1:GFP accumulation levels were seen with samples expressing myosin XI-F and VIII-2 tails ( Figure 4E ) , where no aggregates were formed ( Figure 4D ) . Conversely , samples expressing myosin XI-2 tails exhibited the lowest PDLP1:GFP accumulation , and nearly 60% of the corresponding cells showed PDLP1:GFP aggregates ( Figures 4D and E ) . In regard to the myosin tail expression , accumulation levels were very similar both for class XI myosin tails ( approximately 100 kDa ) and VIII ( approximately 40 kDa ) ( Figure 4E , asterisks ) . We concluded that , among the 6 tested myosins , only expression of the myosin XI-2 and XI-K tails specifically induced mislocalization and abolished PD targeting of PDLP1:GFP . The myosins XI-K and XI-2 are the principal drivers of cell dynamics including organelle trafficking and F-actin organization , as well as diffuse and polarized cell growth [25]–[27] . Therefore , we were interested to determine if the contributions of these same myosins to PDLP1 localization were general or affected a specific targeting pathway . Firstly , we addressed a potential role of myosins in protein targeting to the plasma membrane ( PM ) using the PM marker TM23:GFP [41] . As shown in Figure 5A and 5B , the distribution pattern of TM23:GFP was not affected by the expression of myosin VIII-2 or XI-K tails; in both cases the marker was localized throughout the PM . Co-expression of the marker and myosin tails in all experimental conditions were validated by immunoblot analysis ( Figure 5C ) . Secondly , we investigated localization of the green fluorescent protein-tagged remorin ( GFP:REM ) , a membrane microdomain marker localized in the PM and in PD [42] . Contrarily to PDLP1 , remorin down regulate virus cell-to-cell movement by interacting directly with , MP of a potexvirus . As described [42] , GFP:REM clustered in discrete PM domains; this localization pattern was not altered upon the transient expression of myosin tails VIII-2 ( Figure 5D ) or XI-K ( Figure 5E ) . Immunoblot analysis confirmed expression of GFP:REM and myosin tails in each experimental variant ( Figure 5F ) . Thirdly , we examined the targeting of the plasmodesmata callose binding 1 ( PDCB1 ) protein fused to mCherry ( PDCB1:mCherry ) , which is localized to the PD neck region [43] . Once again , overexpression of the tails of myosin VIII-2 ( Figure 5G ) or XI-K ( Figure 5H ) had no observable effect on PDCB1:mCherry targeting to PD-enriched areas at the cell periphery ( see Figure 5I for the PDCB1:mCherry and myosin tail expression ) . Collectively , these results show that in contrast to PDLP1:GFP or GFP:2B the transport or retention of the three tested protein markers targeted to the PM and/or PD was not affected by overexpression of the myosin VIII-2 or XI-K tails , indicating a distinct PD-transport route for PDLP1:GFP . Finally , we were interested to determine if the transport along the secretory pathway directed to the vacuole membrane ( tonoplast ) rather than to the PM is myosin-dependent . This question was addressed using the tonoplast-specific marker γ-TIP1 fused to mCherry ( γ-TIP1:mCherry; [44] ) . The vacuoles in fully expanded plant cells usually account for the most of cell volume [45] . The tonoplast surrounding these gigantic organelles is constantly reshaped via formation of the transvacuolar strands and spherical tonoplast invaginations often called bulbs [46] . The tonoplast and bulbs were readily visualized in the γ-TIP1:mCherry-expressing control cells , as well as in myosin VIII-2-expressing cells ( Figure 5J ) . Interestingly , expression of the myosin XI-K tails abolished the bulb formation and led to enrichment of γ-TIP1:mCherry in the perinuclear tonoplast domain ( Figure 5K ) . As in the previous experiments , similar levels of the marker and myosin tail accumulation were confirmed using immunoblot analysis ( Figure 5L ) . We concluded that although the transport of tonoplast-targeted protein was unaffected upon myosin tail expression , the normal tonoplast dynamics required myosin XI-K activity . In general , our observations using dominant negative expression of myosin tails suggest that specific inhibition of myosin XI-K activity affected a specific trafficking pathway of PDLP1 to PD rather than caused an indiscriminate suppression of the endomembrane transport .
The role of cytoskeletal motility in viral infection is a rapidly progressing albeit relatively young field of research at the frontiers of virology and cell biology . The microtubule-dependent transport of retroviruses to nucleus [47] and Herpesvirus to axon endings [48] , actin-dependent formation of the virological synapses through which HIV moves between cells [49] , and an actin-tail propelled transport of poxviruses [50] are a few illuminating discoveries in this field . Animal and plant viruses share multiple replication mechanisms that rely on conserved features of eukaryotic cells [51] , [52] . In contrast , virus cell-to-cell movement in plants occurs via the plant-specific PD , channel-like organelles providing symplasitc continuity between adjacent cells [53] . To accomplish movement through PD , plant viruses have evolved dedicated MPs that target and modify PD to mediate virus passage . One of the principal mechanisms of MP action is a tubule-guided PD transport used by a wide variety of the RNA and retroid DNA viruses [1] whereby MP modifies PD by assembly into multimeric tubules through which virion movement occurs . Most of the previous work on plant virus-cytoskeleton relationships involved chemical inhibitors [20] . Although useful for an initial insight , this approach is not unlike a sledgehammer because global disruption of microtubules or microfilaments causes dramatic changes in cell physiology that are difficult to associate with specific mechanisms of virus replication or transport . Even in the cases like TMV , where genetic and other more subtle approaches were used [16] , [18] , [19] , [54] , the picture is less than clear . In a large part , difficulties in reconciling work from different labs stem from the incomplete understanding of the cellular partners required for the MP function . Our recent discovery of PDLPs as host receptors [10] , [11] that mediate PD targeting of the tubule-forming MPs of the nepovirus GFLV and the caulimovirus CaMV provided a unique opportunity to address the role of actomyosin motility in virus transport using both the chemical and the more specific dominant negative inhibition of myosins [23] , [33] . Combining these approaches , we revealed critical contributions of the myosin motors in the GFLV transport between the cells . We identified myosin XI-K as a principal driver of this process with additional contributions provided by other class XI , but not class VIII myosins . Furthermore , we obtained important new insight into myosin-driven endomembrane transport in plants by showing that myosin XI-K acts in a specific pathway within a general ER-to-Golgi-to-PM transport network . Because GFLV transport is tubule-dependent , it was important to determine if myosin inactivation interfered with tubule formation or PD localization . Our previous work using suspension cell culture has shown that tubule assembly requires ER-to-Golgi pathway , whereas cytoskeletal systems appeared to contribute to tubule targeting [38] . Here , we found that the inhibition of myosin XI-K resulted in a conspicuous nucleo-cytosolic redistribution of the GFP:2B with no detectable PD-associated tubules . Thus , tubule formation was specifically affected by myosin inactivation . As was demonstrated recently , 2B assembles tubules at PD via interaction with the host PDLP receptors [11] that , in turn , are transported to PD along the ER-to-Golgi pathway [10] . Therefore , both GFLV movement and tubule formation at PD require proper PDLP targeting . To determine if PDLP targeting was actomyosin dependent , we investigated PDLP1:GFP transport pathway using cytoskeletal inhibitors and dominant negative inhibition of the individual myosins . We found that PDLP1:GFP was present in mobile bodies whose rapid trafficking was abolished by application of LatB or BDM similarly to Golgi stacks whose transport in plants relies entirely on myosins XI [27] . Furthermore , we showed that the myosins XI-K and XI-2 , but not XI-F , VIII-1 , VIII-2 , and VIII-B are required for PDLP1 delivery to PD . Inactivation of the two former myosins resulted in PDLP1:GFP redistribution in the cortical cytoplasm and inclusion bodies that were never observed in the cells where other myosins were inhibited . Given the strong correlation between disruption of PDLP targeting and GFLV movement by interference with myosins XI-K and XI-2 ( Figures 1B and 4B-D ) , we propose that the primary contribution of these myosins to virus transport is the delivery of PDLP-receptors to PD . It is important to stress that this result is also the first indication of myosin XI function in the trafficking of secretory vesicles to the PM/PD compartment . The next question to ask was if PDLP transport occurred along a common post-Golgi secretory pathway , or represented a specialized route within this pathway driven primarily by myosins XI-K and XI-2 . To address this question , we assessed a role of myosin XI-K in the targeting of markers differentially localized to: i ) entire PM; ii ) lipid raft subdomains within PM and PD; iii ) PD neck or iv ) vacuolar membrane ( tonoplast ) . We found that proper targeting of the former three markers was not affected by myosin XI-K inhibition suggesting that the myosin XI-K-dependent PDLP targeting represents a specific route within a broad endomembrane transport network . In addition , we found that myosin XI-K is required for the normal tonoplast reshaping via transient invaginations . It was previously demonstrated that PD targeting of the closteroviral Hsp70 homolog requires myosins VIII [33] , although significance of this process for virus movement was not addressed . It was also found that myosin XI-2 knockdown reduced TMV movement [18] , but this effect was not linked to a specific mechanism . Together with our previous work [10] , [11] , [38] , this study provides a basis for an advanced mechanistic model of myosin-dependent virus movement . According to this model ( Figure 6 ) , the GFLV MP and its host receptor , PDLP , traffic to the cell periphery along distinct pathways . 2B reaches PD by diffusion or by association with microtubules [38] . The transport route employed by PDLP is dependent on the myosins XI with XI-K playing the principal role . At PD , MP binds PDLP for anchorage and tubule assembly . Because transient inhibition of PDLP traffic to PD reduces virus movement ( Figure 1 ) , it seems that steady-state supply of this receptor is required for the formation of tubules that restructure PD . Finally , assembled GFLV virions enter tubules and translocate into adjacent cells . It remains to be determined if virion transport to and through tubules involves cytoskeleton-dependent motility . The emerging picture of the plant-virus interactions with myosin motors is complex and nuanced . It appears that closteroviral Hsp70 homolog directly recruits myosins VIII for virion delivery to PD [33] , whereas tenuiviral MP uses myosin VIII-assisted vesicular transport for the same task [34] . Currently , the PD-directed transport of these viral proteins remains the only experimentally supported function of the class VIII myosins . On the other hand , TMV MP targeting to PD does not require myosins [34] , whereas myosin XI-2 facilitates TMV movement likely via delivering the ER-associated viral replication complexes to PD [18] , [20] , [55] , [56] . This latter hypothesis resonates well with the role of myosins XI-2 and XI-K in ER transport [25] . In the case of GFLV presented here , the virus relies on the myosins XI-K and XI-2 for the trafficking of the host MP receptor PDLP to PD . In addition to important insight into virus-cytoskeleton interactions , our work suggests novel functions of the myosins XI-K and XI-2 in vesicle trafficking and vacuole remodelling . These myosins were previously shown to drive the trafficking of Golgi stacks , peroxisomes , and mitochondria [26] , [27] , as well as the ER flow [25] . Here we show that these same myosins are also involved in PDLP delivery to PD via a specific endomembrane transport pathway , as well as in remodelling of the vacuolar membrane . Further inquiries into the mechanisms of myosin-dependent transport are certain to deepen our understanding of the cell interior dynamics and the importance of these processes for virus movement .
All experiments were performed using N . benthamiana , an experimental GFLV host that supports the complete systemic infection cycle . The plants were grown in growth chambers under 16/8h light/dark cycles , 24/20°C day/night temperatures and approximately 70% humidity . Agroinfiltrated and/or virus-infected leaves were of the same age and size and were maintained at the same conditions . Approximately 300 ng of purified GFLV-RFP virions was mechanically inoculated into N . benthamiana leaves . The binary vectors designed to express HA-epitope tagged N . benthamiana myosin tails VIII-1 , VIII-2 , VIII-B , XI-K , XI-F , and XI-2 were described earlier [33] . The fluorescent reporter proteins used to visualize subcellular compartments were as follows: GFP:2B , the GFLV MP forming tubules at PD of virus-infected cells [11]; PDLP1:GFP localized in the PM lining PD channel [10]; PDCB1:mCherry targeted to the PD neck [43]; GFP:REM localized to lipid rafts within PM and PD [42]; TM23:GFP labelling the entire PM [41]; Man1:RFP associated with Golgi-stacks [39]; tonoplast-specific γ-TIP1:mCherry [44] . All plasmids were transformed into Agrobacterium tumefaciens ( strain LBA4404 ) that was used for agroinfiltration at a final optical density ( OD 600 nm ) of 0 . 3 [11] . Leaf samples were processed for imaging or immunoblot analysis at 48 hours post infiltration . To analyze the effect of the actin microfilament disassembly drug LatB on GFLV infection , N . benthamiana leaves were infiltrated with 10 µM LatB in 0 . 1% DMSO 6 hours prior to inoculation with GFLV-RFP . In addition , 10 µM LatB or 10 mM 2 , 3 butanedione monoxime in water solution ( BDM; an ATPase inhibitor that disrupts myosin function ) were vacuum infiltrated into N . benthamiana leaf disks 36 hours after agroinfiltration to examine the effects of these inhibitors on the trafficking and localization of PDLP1:GFP and Man1:RFP . Leaf disks were kept in a moisture chamber and were observed at 12 hours after the treatment . Control infiltrations were performed either with 0 . 1% DMSO or with water . Total protein extracts were obtained by grinding N . benthamiana leaf disks in Laemmli buffer , separated by SDS-PAGE , and transferred by electroblotting to a polyvinylidene difluoride membrane ( Immobilon-P; Millipore ) . To detect myosin tails , membranes were probed with anti-HA-peroxidase antibodies ( Sigma-Aldrich ) at 1∶5 , 000 dilution . For the GFLV movement protein 2B , affinity purified GFLV 2B-specific rabbit antibody [57] was used in 1∶10 , 000 dilution . The expression of all other GFP-tagged proteins was assayed using , the monoclonal anti-GFP antibodies ( Clontech ) diluted to 1∶5 , 000 . The expression of mCherry-fused γ-TIP1 and PDCB1 was detected using polyclonal anti-DsRed antibodies as recommended by manufacturer ( Clontech ) . Cells expressing fluorescent proteins were imaged using a Zeiss LSM510 laser scanning confocal microscope with a C-Apo-chromat ( 63X/1 . 2 W Korr ) water objective lens under multitrack mode . Excitation/emission wavelengths were 488 nm/505 to 545 nm for GFP and 543/long pass 560 nm for RFP . Confocal images were processed using LSM510 software version 2 . 8 ( Zeiss ) . GFLV-RFP infection foci were examined under a Leica MacroFluo epifluorescent microscope equipped with the apochromatically corrected zoom system Z16 APO , a 5x objective and a DFC 360FX camera . All imaging was conducted under identical illumination and exposure conditions to allow comparisons . Following acquisition , images were processed using ImageJ ( 1 . 38u ) , and Adobe Photoshop ( v7 . 0 ) software . Statistical evaluations were made using ANOVA R software or Student's t-test where appropriate . | To establish infection , plant viruses spread cell-to-cell via narrow channels in the cell wall , the plasmodesmata ( PD ) . Movement proteins ( MP ) are virus-encoded proteins essential for virus intercellular transport through PD . Plasmodesmata located plant proteins ( PDLPs ) , are specifically recognised by the MPs of tubule-forming viruses . Here we show that PDLP targeting to PD depends on the molecular motors myosin XI-K and XI-2 . Consistently , and in support of a function of PDLP as PD receptor for MP , overexpression of dominant negative myosin mutants inhibits tubule formation by Grapevine fanleaf virus ( GFLV ) MP and dramatically reduces virus movement . | [
"Abstract",
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"Results",
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"Methods"
] | [
"plant",
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] | 2011 | Tubule-Guided Cell-to-Cell Movement of a Plant Virus Requires Class XI Myosin Motors |
A recent study has shown that treatment of visceral leishmaniasis ( VL ) with the standard dose of 15 mg/kg/day of paromomycin sulphate ( PM ) for 21 days was not efficacious in patients in Sudan . We therefore decided to test the efficacy of paramomycin for a longer treatment duration ( 15 mg/kg/day for 28 days ) and at the higher dose of 20 mg/kg/day for 21 days . This randomized , open-label , dose-finding , phase II study assessed the two above high-dose PM treatment regimens . Patients with clinical features and positive bone-marrow aspirates for VL were enrolled . All patients received their assigned courses of PM intramuscularly and adverse events were monitored . Parasite clearance in bone-marrow aspirates was tested by microscopy at end of treatment ( EOT , primary efficacy endpoint ) , 3 months ( in patients who were not clinically well ) and 6 months after EOT ( secondary efficacy endpoint ) . Pharmacokinetic data were obtained from a subset of patients weighing over 30 kg . 42 patients ( 21 per group ) aged between 4 and 60 years were enrolled . At EOT , 85% of patients ( 95% confidence interval [CI]: 63 . 7% to 97 . 0% ) in the 20 mg/kg/day group and 90% of patients ( 95% CI: 69 . 6% to 98 . 8% ) in the 15 mg/kg/day group had parasite clearance . Six months after treatment , efficacy was 80 . 0% ( 95% CI: 56 . 3% to 94 . 3% ) and 81 . 0% ( 95% CI: 58 . 1% to 94 . 6% ) in the 20 mg/kg/day and 15 mg/kg/day groups , respectively . There were no serious adverse events . Pharmacokinetic profiles suggested a difference between the two doses , although numbers of patients recruited were too few to make it significant ( n = 3 and n = 6 in the 20 mg/kg/day and 15 mg/kg/day groups , respectively ) . Data suggest that both high dose regimens were more efficacious than the standard 15 mg/kg/day PM for 21 days and could be further evaluated in phase III studies in East Africa . ClinicalTrials . gov NCT00255567
According to the WHO estimates , visceral leishmaniasis ( VL ) is a parasitic disease that affects more than 500 , 000 people globally each year [1] , and has a fatality rate of up to 100% if left untreated [2] . 90% of cases occur in five countries: India , Bangladesh , Nepal , Sudan , and Brazil [1] , with the affected communities mostly located in remote regions of these endemic areas without ready access to treatment . Although drugs ( mainly antimonials such as sodium stibogluconate [SSG] ) currently exist to treat this parasitic infection , their use has been limited because of high cost , toxicity , or development of parasite resistance [3]–[5] . A multi-center phase III study in India showed that PM is a very efficacious , affordable , and safe treatment [6] , and is now registered for VL treatment in India . In an effort to identify an effective treatment for VL in East Africa , we had previously initiated a multi-center phase III study in Sudan , Ethiopia , and Kenya comparing the efficacy of PM alone at the dose shown to be efficacious in India ( 15 mg/kg/day for 21 days ) against SSG alone ( 20 mg/kg/day for 30 days ) and against a combination treatment of SSG and PM ( same dose of individual treatments but for 17 days ) . PM monotherapy did not show adequate efficacy , particularly in Sudan where parasite clearance was below 50% in patients at 6 months after end of treatment ( EOT ) , and the study had to be prematurely stopped [7] . In the current study , we sought to find an efficacious dose of PM for the treatment of VL in Sudan and to explore possible reasons for the failure of the drug at the previous dose studied of 15 mg/kg/day for 21 days . In our previous study using this dose , conducted in 5 sites in Ethiopia , Kenya and Sudan , we found an overall end of treatment cure of 67 . 4% and 6-month post-treatment cure of 63 . 8% [7] . Cure at both sites in Sudan was below 50% [7] . The cure rate in this study of SSG was 92 . 2% at 6 months post-treatment [7] . Therefore a total dose increase of 33% was attempted through two possible regimens- an increased dose of 20 mg/kg for 21 days or a prolonged course of 15 mg/kg for 28 days . The former regimen has been evaluated in some clinical trials in India [8] , [9] . There was no previous clinical experience with the 15 mg/kg dosage given for 28 days . The rationale was that the longer course of treatment would provide additional time for the patient's general condition to improve , and for their immunological response to develop , and that this might translate into a better clinical response without increasing the daily dosage .
The main objective was to assess the efficacy of two dosing regimens of PM monotherapy for the treatment of VL: 20 mg/kg/day for 21 days and 15 mg/kg/day for 28 days . Secondary objectives were to assess the safety of PM and compare the pharmacokinetic ( PK ) profiles of the two groups in a subset of patients . Patients with clinical symptoms and signs suggestive of VL and confirmed by visualization of parasites in bone-marrow aspirates were eligible for enrollment according to the National VL guidelines for Sudan for treatment and control . To be included in the study , patients had to: be between 4 and 60 years of age; be able to comply with the protocol ( Protocols S1 , S2 and S3 ) ; and provide written informed consent signed by themselves or by parents or legal guardians . Patients were excluded from the study if they: had negative bone-marrow smears; were clinically contraindicated to having a bone-marrow aspirate; received any anti-leishmania drug in the past 6 months; had severe protein or caloric malnutrition ( Kwashiorkor or marasmus ) ; had previous hypersensitivity reaction to aminoglycosides; suffered from a concomitant severe infection , ie tuberculosis , HIV , or any other serious underlying disease ( cardiac , renal , hepatic ) ; suffered from other conditions associated with splenomegaly such as schistosomiasis; had previous history of cardiac arrhythmia or an abnormal electrocardiogram ( ECG ) ; were pregnant or lactating; or had pre-existing clinical hearing loss . If tuberculosis or schistosomiasis were suspected , these were screened through laboratory testing . Additionally , patients with the following laboratory values were excluded: hemoglobin less than 5 g/dL; white blood cell less than 103/mm3; platelets less than 40 , 000/mm3; liver function test values more than three times the normal range; and serum creatinine values outside the normal range for age and gender . This was a two-arm , randomized , open-label , dose-finding study done at a single site in Sudan ( Kassab Hospital , Ministry of Health , Gedaref State ) . This site participated in the previous study conducted on PM [7] . Eligible patients were randomly assigned to 20 mg/kg/day PM for 21 days ( n = 21 ) or 15 mg/kg/day PM for 28 days ( n = 21 ) , and started a treatment regimen upon allocation to their treatment . Restricted-block randomization was done for the two groups . Randomization was done using sequentially numbered sealed envelopes that were prepared according to a centrally generated randomization list . Treatment was administered via daily intramuscular injection , and patients remained in the hospital for the duration of treatment . Patients were followed up at 3 and 6 months after treatment as outpatients . Parasitological assessments ( bone-marrow aspirates only ) were done at baseline , end of treatment ( EOT ) , 3 months ( only on patients who were not clinically well ) and 6 months after treatment . Safety and clinical laboratory assessments were done at baseline , day 7 and day 14 of drug administration , EOT , and at 3 and 6 months follow-ups . These included a clinical assessment , ( clinical symptoms , vital signs , weight , spleen and liver size ) , ECG , HIV testing ( at baseline only ) , hemoglobin , white cell count , platelets , urea , creatinine , liver function tests ( bilirubin , aspartate aminotransferase , alanine aminotransferase , alkaline phosphatase ) , urinalysis and audiometry . Audiometry was performed using a standardized procedure by site investigators who were trained by a qualified audiometrist and recorded as hearing levels in dB at 0 . 25 , 0 . 5 , 1 , 2 , 4 and 8 kHz frequencies [10] . All reported abnormal audiometric readings were reviewed by the audiometrist . An audiometric shift was defined in patients for whom there was one of the following: an increase in hearing level between baseline and EOT of ≥25 dB at ≥1 threshold frequency; an increase in hearing level between baseline and EOT of ≥20 dB at ≥2 adjacent threshold frequencies . Disabling hearing impairment was determined as an average of at least 41 dB across 0 . 5 , 1 , 2 and 4 kHz frequencies in adults ( ages 15 years and above ) and at least 31 db across 0 . 5 , 1 , 2 and 4 kHz frequencies in children ( less than 15 years of age ) [10] . Parasitology slides were prepared from bone-marrow aspirates , read , and reported according to a standardized , approved WHO method [11] , [12] . Standardised parasitology readings were done from freshly prepared bone-marrow aspirates taken directly from the patients to the laboratory . Slide fields were examined and counted for parasites under oil emersion 100× magnification for 30 minutes ( timed ) before being declared negative ( absence of parasites on microscopy slide ) . All parasitology was performed by a trained laboratory technician . For the PK analysis , the first six consenting patients weighing 30 kg or more were selected from each treatment group and had additional venous blood and urine samples on day 1 and day 14 in the 20 mg/kg/day group , and on day 1 and day 26 in the 15 mg/kg/day group . The timing for blood sampling was 0 ( before treatment ) and at 0 . 25 , 0 . 5 , 1 , 2 , 4 , 6 , 8 , 12 , and 24 hours after administration of the drug , and at 0–2 , 2–4 , 4–6 , 6–8 , 8–12 , and 12–24 hours after administration of the drug for urine sampling . The trial was done in accordance with the Declaration of Helsinki ( 2002 version ) for the conduct of research on human subjects and followed the International Committee for Harmonization guidelines for the conduct of clinical trials . All trial site personnel received relevant training in Good Clinical Practices . The Ethics Committee of the Institute of Endemic Diseases , University of Khartoum , and the Directorate of Health Research , Federal Ministry of Health , Sudan approved the study protocol ( July 8 , 2005 ) , which was submitted as a protocol amendment ( Protocol S3 ) to our previous study [7] . All participants or their parents or legal guardians gave their written informed consent before entry into the trial . Children were included in this study because they represent more than 50% of VL cases in this endemic area , and were included in the PK sampling if they met the weight criteria ( >30 kg ) . This study was registered at ClinicalTrials . gov ( registration number NCT00255567 ) . The study medication was 1 g/2mL paromomycin sulphate ( Gland Pharma , India ) . Doses in the study groups were 20 mg/kg/day paromomycin sulphate ( equivalent to 15 mg/kg/day of paromomycin base ) and 15 mg/kg/day paromomycin sulphate ( equivalent to 11 mg/kg/day paromomycin base ) . The rescue medication was AmBisome ( a liposomal formulation of amphotericin B , Gilead , USA ) , which was reconstituted according to the manufacturer's instructions for a dosage of 3 mg/kg/day for 10 days .
104 patients with suspected VL were screened for entry into this study . Of these , 42 patients were enrolled in the study ( 21 per group; figure 1 ) . Demographics and baseline characteristics were similar in the two groups ( table 1 ) . One patient in the 20 mg/kg/day PM group was considered lost by the 6-month follow-up . The first patient was recruited in October 2005 and the last patient followed-up in October 2006 . Data were available for all patients at EOT ( figure 1 ) . 18 patients in the 20 mg/kg group and 19 in the 15 mg/kg group had parasite clearance at EOT , indicating an efficacy of 85 . 7% ( 95% CI: 63 . 7% to 97 . 0% ) and 90 . 5% ( 95% CI: 69 . 6% to 98 . 0% ) , respectively ( table 2 ) . At 3-months follow-up , two patients had relapsed in the 20 mg/kg/day for 21 days regimen and three in the 15 mg/kg/day for 28 days regimen; however , there were no additional relapses at 6 months . At 6-months follow-up , the complete-case analysis efficacy in both groups was similar ( 80 . 0% in the 20 mg/kg/day group versus 81 . 0% in the 15 mg/kg/day group ) ( table 2 ) . All treatment failures were given rescue medication . An exception was one patient in the 20 mg/kg/day PM group who was parasite positive at EOT but clinically responded . This patient was lost to follow-up at 6 months , leading to a lower efficacy estimate in the worst-case analysis ( table 2 ) . There was one slow responder ( ie , parasite-positive patient at EOT , but clinically well and ultimately recovered ) in the group treated with 15 mg/kg/day PM for 28 days . PM was well tolerated in this study . 48 AEs were reported in total; 20 in the 20 mg/kg for 21 days group and 28 in the 15 mg/kg for 28 days group ( table 3 ) , and none was regarded as serious . This gives an AE rate of 0 . 05 per person-day on treatment in both groups . All AEs , except diarrhea and malaria , were judged to be related to the treatment . The most frequent AE was injection site pain ( n = 33 ) . Audiometric shifts were seen in five patients at EOT ( n = 3 in the 15 mg/kg group and n = 2 in the 20 mg/kg group ) , but completely resolved by 6 months follow-up . Disabling hearing impairment , detected at EOT , which improved but persisted at 6 months ( ie still met the criteria for audiogram shift ) , occurred in one patient in the 20 mg/kg group . Although six patients from each group should have taken part in the PK study , only data from three patients in the 20 mg/kg/day PM group and six in the 15 mg/kg/day PM group were obtained . Only one patient was a child ( age of 12 years and weight of 39 kg in the 15 mg/kg group ) . The others were aged between 17 and 28 years . Mean plasma PM concentrations at the earlier time points were similar between the two treatment groups ( figure 2 ) . Nevertheless , the peak mean plasma PM concentration on day 1 was slightly higher in the 20 mg/kg/day group compared with that in the 15 mg/kg/day group ( 7 . 8±4 . 9 µg/mL versus 5 . 6±4 . 2 µg/mL ) . Six hours after administration , PM was not detected in the plasma of patients receiving 15 mg/kg/day PM but was seen at concentrations slightly lower than peak in the plasma of patients receiving 20 mg/kg/day PM ( figure 2 ) .
To date , the standard treatment for VL in East Africa still consists of antimonials . This study is part of the first large-scale multi-centre clinical trial to assess the efficacy of PM for the treatment of VL for the East African region . The initial study [7] showed poor efficacy results when 15 mg/kg/day PM was administered for 21 days to VL patients . This finding is in contrast to an earlier phase III study in India [6] . The results of this study show that increasing the total dose of PM from 15 mg/kg/day for 21 days to 15 mg/kg/day for 28 days or 20 mg/kg/day for 21 days improves efficacy in VL patients in Sudan . However , it should be cautioned that the results found in this study apply to one site only and might not apply to the whole East African region . Although efficacy is normally assessed as parasite clearance at 6 months in trials for VL , in this study we chose to use parasite clearance at EOT as the primary endpoint because a chance of loss to follow-up of just a few patients would significantly affect the result . In addition to the small sample size , another potential limitation is the use of bone-marrow aspiration for diagnosis and test of cure . However , spleen aspiration remains contraindicated in rural hospitals in Sudan , making bone marrow the best viable alternative . At 6 months after treatment , efficacy was 80 . 0% ( 95% CI: 56 . 3% to 94 . 3% ) and 81 . 0% ( 95% CI: 58 . 1% to 94 . 6% ) in the 20 mg/kg/day and 15 mg/kg/day groups , respectively , compared with less than 50% ( in Sudan ) at 6 months observed in the previous study [7] . This result shows that efficacy improved to levels closer to those obtained in trials in India ( ∼95% ) [6] . Serious safety issues that would limit the evaluation of PM at high doses were not identified in this study . Otoxicity , which has been seen as a transient side-effect of PM in other studies [6] , was also identified as a potential issue in this study because one patient had audiometric shift at 6 months . This shift occurred at high frequencies , as expected with aminoglycosides [6] . We suggest that this adverse event needs to be monitored closely in subsequent studies . PK analyses showed that peak plasma PM concentration occurred 1–2 hours after administration and suggest that , at the high daily dose of 20 mg/kg , elevated plasma PM concentrations may be maintained for a longer period of time ( up to 8 hours ) . Unpublished data ( Mahmoud Mudawi , personal communication ) of PM administration ( 15 mg/kg ) to healthy Sudanese volunteers showed peak PM plasma concentrations similar to those in American volunteers who received a similar dose [14] . Sudanese VL patients had a much lower plasma concentration ( 30–40% ) than that of healthy Sudanese ( 19 . 5±7 . 6µg/mL; n = 6 ) and American volunteers . Therefore , Sudanese VL patients may have different PK characteristics from both Sudanese and American healthy volunteers , and Indian VL patients . However , PK data were very limited and derived from only a small subset of patients . A PK study with more patients is currently underway as part of the larger phase III study . Even though interpretation of our results is limited because of the small sample size , we identified what seems to be a more efficacious dose of PM than the one previously used in Sudan [7] . A meeting of the principal investigators was held to discuss the PM efficacy and PK dose-finding results . The group chose to use in the large multi-center phase III study , a dose of 20 mg/kg/day for 21 days for a comparison with the previously used doses of SSG and SSG and PM in combination . Our initial study [7] showed that efficacy of PM can vary greatly between geographical regions , and in addition to this study , suggests that different doses may be required to obtain similar levels of efficacy . If confirmed , these results emphasize the importance of considering regional differences in the treatment of VL and show that drugs of proven efficacy in Asian patients might not have the same efficacy in African patients . | Visceral leishmaniasis ( VL ) is a parasitic disease transmitted through the bite of sandflies . The WHO estimates 500 , 000 new cases of VL each year , with more than 90% of cases occurring in Southeast Asia , East Africa , and South America . If left untreated , VL can be fatal . We had previously conducted a large multi-center study in Sudan , East Africa , to assess the efficacy of paromomycin ( PM ) alone or in combination with sodium stibogluconate . Clinical studies in India have shown that 15 mg/kg/day PM for 21 days was an effective cure . However , the same treatment regimen was not efficacious in two study sites in Sudan . Here , our aim was to assess two high-dose regimens of PM in Sudan: 15 mg/kg/day for 28 days and 20 mg/kg/day for 21 days . The results suggest that , at these total doses , PM is more efficacious than when given daily at 15 mg/kg for 21 days , and that high doses are required to treat VL in Sudan . Efficacy of 20 mg/kg/day PM for 21 days is currently being evaluated in a prospective , comparative phase III trial in East Africa . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
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] | 2010 | Paromomycin for the Treatment of Visceral Leishmaniasis in Sudan: A Randomized, Open-Label, Dose-Finding Study |
Protein Phosphatase type 2A ( PP2A ) represents a family of holoenzyme complexes with diverse biological activities . Specific holoenzyme complexes are thought to be deregulated during oncogenic transformation and oncogene-induced signaling . Since most studies on the role of this phosphatase family have relied on the use of generic PP2A inhibitors , the contribution of individual PP2A holoenzyme complexes in PP2A-controlled signaling pathways is largely unclear . To gain insight into this , we have constructed a set of shRNA vectors targeting the individual PP2A regulatory subunits for suppression by RNA interference . Here , we identify PR55γ and PR55δ as inhibitors of c-Jun NH2-terminal kinase ( JNK ) activation by UV irradiation . We show that PR55γ binds c-SRC and modulates the phosphorylation of serine 12 of c-SRC , a residue we demonstrate to be required for JNK activation by c-SRC . We also find that the physical interaction between PR55γ and c-SRC is sensitive to UV irradiation . Our data reveal a novel mechanism of c-SRC regulation whereby in response to stress c-SRC activity is regulated , at least in part , through loss of the interaction with its inhibitor , PR55γ .
The Src family of nonreceptor tyrosine kinases are integral players in the mediation of various physiological processes such as cell motility , adhesion , proliferation , and survival [1] . Members of the Src family share a conserved structure consisting of four Src homology ( SH ) domains , a unique region , and a short negative regulatory tail . The amino terminal SH4 domain is myristoylated and targets the protein to the membrane , while the carboxy-terminal SH1 domain functions as a tyrosine kinase domain [2] . c-SRC activation is negatively regulated by Carboxy Src Kinase ( CSK ) or its homologue CHK through Tyrosine 527 ( Tyr527 ) phosphorylation [2] . This inhibitory phosphorylation promotes the assembly of the SH2 , SH3 , and kinase domains into a closed conformation [2] . Following stimulation by various stresses and growth factors c-SRC activation is initiated by dephosphorylation of the Tyr527 residue by the protein-tyrosine phosphatase PTPα [3] and PTP1B [4] . Alternatively , c-SRC is activated by the binding of tyrosine-phosphorylated proteins to the SH2 domain , resulting in destabilization of the intermolecular interaction between Tyr527 and the SH2 domain [2] . Subsequently , c-SRC is autophosphorylated at Tyrosine 416 ( Tyr416 ) , a site within a segment of the kinase domain termed the activation loop , promoting a conformational change that allows the kinase to adopt an open active confirmation [2] . c-SRC is overexpressed or activated in a wide variety of tumors [5 , 6] . However , overexpression of c-SRC by itself has only minor oncogenic potential [7] and mutations in c-SRC in cancer have only been found sporadically [8] . This led to the hypothesis that c-SRC has a supportive function in tumorigenesis rather than a role in the actual transformation process [9] . Overexpression of v-Src , a constitutively active form of c-SRC lacking the c-terminal part containing the inhibitory Tyr527 , is a potent activator of c-Jun NH2-terminal kinase ( JNK ) , a growth-regulatory enzyme that can control cell proliferation and cell survival both positively and negatively , depending on the stimulus and the cellular context [10 , 11] . Furthermore , SRC activity is essential for JNK activation following a number of different stress stimuli , including UV irradiation [12–14] . Protein Phosphatase 2A ( PP2A ) is a serine/threonine phosphatase that can influence the phosphorylation state of many signaling enzymes [15 , 16] , and inhibition of this phosphatase can affect cellular responses such as growth , differentiation , and apoptosis [15 , 17] . The holoenzyme generally exists as a core dimer , consisting of a 36-kDa catalytic subunit ( PP2Ac ) and a 65-kDa scaffold subunit ( PR65 ) that associates with a variety of regulatory subunits . These regulatory B subunits can modulate the activity of the PP2Ac/PR65 core unit , thus allowing specific temporal targeting of a wide range of PP2A substrates . To date 15 genes coding for more than 26 ( B ) regulatory subunits have been identified that are subdivided into five different subfamilies [17] . The variable PP2A B subunits are targeted by a number of viral oncogenes , which thereby compete for interaction with the PR65/PP2Ac core dimer . This suggests that specific PP2A holoenzymes play a role in viral propagation and oncogenic transformation [18 , 19] , which is further supported by the finding that general inhibitors of PP2A can cause tumor growth on the skin and liver of rodents [20–23] . Understanding the precise manner in which PP2A is involved in the regulation of these different signaling cascades and its role during oncogenic transformation requires the identification of the specific holoenzymes involved in these processes . Interpretation of a large amount of data using general PP2A inhibitors has been limited by the pleiotropic inhibition of all PP2A holoenzyme complexes by the inhibitors used . Furthermore , ectopic expression of the various B subunits can lead to competition with other subunits for binding to the holoenzyme , making it difficult to draw firm conclusions from the data [24 , 25] . Using a gene family knockdown library targeting all deubiquitinating ( DUB ) enzymes , we previously identified the familial tumor suppressor gene CYLD as a novel regulator of the NF-κB signaling pathway [26] and USP1 as the deubiquitinating enzyme of the FANCD2 DNA repair protein [27] . To study the role of the various PP2A complexes in specific pathways we have constructed a library of 61 independent vectors expressing short hairpin RNAs ( shRNA ) targeting the PP2A regulatory B subunits for suppression . Using this knockdown library in a screen for enhancers of JNK activation following cellular stress , we identified a number of PP2A B subunits as novel regulators of JNK activation , most notably PR55γ and PR55δ . Furthermore we demonstrate that the PP2A B subunit PR55γ negative regulates the JNK effector pathway by acting as a stress sensitive inhibitor of c-SRC activity .
To identify the specific PP2A holoenzyme complexes involved in pathways known to be modulated by PP2A , we constructed a gene family knockdown library targeting all putative human PP2A regulatory B subunits for suppression . We retrieved the cDNA sequences for each of the PP2A subunit family members from the ENSEMBL database and designed two to four unique 19-mer sequences for each transcript for cloning into pSuper and pRetro-Super [26 , 28] . In total 61 knockdown vectors were generated , which were then subsequently pooled into 16 sets of two to four vectors per transcript with each set targeting one of the regulatory B subunits or a specific transcript variant ( Figure 1A; Table S1 ) . To validate the pooled knockdown vectors , we tested six randomly chosen pools of vectors for their ability to effectively knockdown the target proteins . All pools tested show a notable reduction in target protein expression levels ( Figure 1B ) . Studies using viral proteins that target the regulatory B subunits of the PP2A holoenzyme complex indicate that JNK and the proto-oncogene c-Jun can be regulated by PP2A [29] . This suggests that specific PP2A regulatory B subunits are involved in PP2A-mediated regulation of the JNK pathway . To directly assess the putative role of PP2A in JNK regulation we asked if suppression of one or more PP2A regulatory subunits by RNA interference could affect JNK activity following UV irradiation . U2-OS cells were transfected with the different library pools and then assayed by western blotting for the efficiency of UV induced JNK activation as judged by threonine-183/tyrosine-185 phosphorylation . Unsurprisingly , we found that suppression of a number of the B subunits appeared to enhance the levels of phosphorylated JNK following UV . Of these , PR55γ consistently yielded the strongest effect and was chosen for further validation ( Figure 1C and unpublished data ) . To evaluate which of the four individual knockdown vectors in this pool were active against PR55γ , we transfected cells with HA-tagged PR55γ and determined the protein levels of HA-PR55γ in lysates of transfected cells in the presence or absence of the individual PR55γ knockdown vectors . As depicted in Figure 2A , all four shRNA vectors ( A–D ) in this pool were able to suppress HA-PR55γ expression levels , whereas no effect was detected on a cotransfected green fluorescent protein ( GFP ) ( Figure 2A ) . A shRNA targeting the mouse-specific B subunit PR59 was used as a negative control in all experiments . Vectors A and C were more efficient in suppressing HA-PR55γ protein levels than vectors B and D ( Figure 2A ) . A fifth knockdown vector ( E ) was designed , which like vector C , induced strong suppression of ectopic PR55γ expression ( Figure 2A ) . shRNAs C and E will be referred from here on as shRNA#1 and shRNA#2 , respectively . To test whether these shRNAs #1 and #2 could inhibit endogenous PR55γ levels we performed quantitative real-time PCR ( QRT-PCR ) . We found that both shRNAs efficiently suppressed endogenous PR55γ mRNA levels ( Figure 2B ) . Furthermore , inhibition of PR55γ with both validated knockdown vectors could efficiently enhance the activation of JNK by UV irradiation ( Figure 2C ) , arguing against an off-target effect of the shRNAs . This result underscores the validity of the screen and suggests that endogenous PR55γ is a repressor of stress-induced JNK activation . To determine whether the activation of JNK after transfection of PR55γ knockdown vectors was a consequence of the loss of PR55γ expression , we performed an add-back experiment . To do this we restored PR55γ levels to the control situation using a PR55γ construct ( ΔPR55γ ) containing two noncoding mutations within the region targeted by knockdown vector #2 , rendering it refractory to shRNA-mediated suppression ( Figure 2D ) . We found that expression of ΔPR55γ completely abolished the enhanced activation of UV-induced JNK observed with shRNA vector #2 , but not with shRNA vector #1 , which targets a region that was not mutated in ΔPR55γ ( Figure 2E ) . These results argue that the effects of the knockdown vectors targeting PR55γ for shRNA-mediated suppression on JNK activation are the result of loss of PR55γ . To investigate whether the enhanced JNK activation upon PR55γ knockdown is specific for UV irradiation , we asked whether other stimuli that lead to the activation of the JNK pathway might also be enhanced by loss of PR55γ . We found that TNFα , insulin , and osmotic stress-mediated JNK activation could all be enhanced by suppression of PR55γ but not EGF-mediated JNK activation ( Figure 2F ) . These results suggest that PR55γ is a regulator of the JNK signaling pathway when activated by diverse stimuli . It has previously been established that activation of JNK by UV irradiation can enhance apoptosis in cell culture [30] . Since knockdown of PR55γ leads to enhanced JNK activation , we asked whether knockdown of PR55γ could enhance apoptosis following UV irradiation . UV-induced apoptosis was indeed significantly enhanced in PR55γ-depleted cells ( Figure 3A ) as determined by measuring the mitochondrial membrane potential with a fluorescent dye ( 3 , 3′-dihexyloxa-carbocyanine iodide , [DiOC6 ( 3 ) ] ) . Figure 3B represents three independent DiOC6 experiments demonstrating the percentage of apoptosis with or without UV in presence of knockdown vectors targeting PR55γ or a control vector . We also observed an increase in caspase 3 cleavage , a primary executioner of apoptosis , in lysates of cells exposed to UV irradiation , when PR55γ was suppressed ( Figure 3C ) . Similar results were obtained with a second shRNA targeting PR55γ ( unpublished data ) . To investigate whether PR55γ regulates the JNK pathway upstream of JNK we asked if loss of PR55γ affected MKK4 , the kinase acting directly upstream JNK [31] . We indeed found also that MKK4 activity was significantly enhanced in cells with depleted PR55γ ( Figure 4A ) . These data suggest that PR55γ does not directly affect JNK phosphorylation levels . We therefore asked whether the suppression of PR55γ had an effect on the other MAPK pathways , p38 and ERK . Indeed , western blot analyses indicated that knockdown of PR55γ resulted in increased phosphorylation of both JNK and p38 , but not of ERK following UV irradiation ( Figure 4B ) . Thus indicating that PR55γ acts on a key regulatory protein required for activation of both JNK and p38 . Of note , no direct interaction was found between PR55γ and components of the MAPK and JNK kinase pathways including the previously described PP2A interacting proteins JNK , MKK4 , p38 , or c-RAF as determined by coimmunoprecipitation assays ( unpublished data ) [32–35] . One of the major contributors to the activation of the JNK pathway is the nonreceptor tyrosine kinase c-SRC [12–14] . It was previously shown that the polyoma middle t ( MT ) antigen , which binds to c-SRC and has been suggested to compete with the PP2A regulatory B subunit for binding to the holoenzyme complex [36–38] , is also a potent activator of the JNK signaling cascade [38] . It has recently been described that of the ubiquitously expressed SRC family members only c-SRC [39] and LYN [40] play decisive roles in UV-induced JNK activation . Consistent with this , only c-SRC and Lyn have putative PKC sites in the N-terminal region . However , LYN appears to exclusively regulate JNK kinase but not p38 or ERK . Since knockdown of PR55γ in our system regulates not only JNK but also the MAPK p38 ( Figure 4B ) , it would suggest that c-SRC may be the critical target of PR55γ in negatively regulating the JNK pathway in U2-OS cells following stress . To test whether the enhanced activation of JNK after suppression of PR55γ is dependent on c-SRC , we cotransfected a dominant negative version of c-SRC , which has a lysine 295 to methionine mutation , resulting in a kinase deficient c-SRC ( Src295M ) [41] . UV irradiation-induced JNK phosphorylation was attenuated in the presence of Src295M , in agreement with the earlier finding showing that JNK is activated by both c-SRC independent and c-SRC dependent pathways [39] . However , the enhancing effect of PR55γ knockdown was completely abolished upon coexpression of Src295M ( Figure 4C ) . Likewise inhibition of c-SRC by the generic Src family inhibitor PP2 also inhibited the enhanced JNK activity caused by suppression of PR55γ ( Figure 4D ) . Similarly , cotransfection of a hairpin targeting PR55γ with an shRNA targeting c-SRC completely abolished the enhancing effects of PR55γ on JNK activity ( Figure 4E ) . To further investigate whether PR55γ can influence the levels of phosphorylated JNK by a non c-SRC family kinase–associated stimulus , we cotransfected a constitutively active form of the GTPaseCdc42 ( Cdc42V12 ) , which functions upstream of MKK4 in the JNK pathway , in the presence or absence of PR55γ . As expected , transfection of Cdc42V12 resulted in activation of JNK [42] . However , the knockdown of PR55γ had no significant affect on JNK activation , whereas it did enhance phosphorylation of JNK following exposure to UV , which served as a control ( Figure 4F ) . Since suppression of a number of the B subunits appeared to enhance the levels of phosphorylated JNK following UV ( Figure 1C ) , we wanted to determine whether the increased levels of phosphorylated JNK observed with knockdown of the other B subunits are dependent on c-SRC . As expected knockdown of PR55δ enhanced the activity of JNK following UV irradiation . Furthermore , like PR55γ , the increased JNK activity was completely attenuated upon cotransfection with kinase dead Src295M ( Figure 4G ) . Together these results suggest that PR55γ and PR55δ negatively regulate JNK signaling in a c-SRC-dependent manner . Since PR55γ is primarily expressed in neuronal tissues and PR55δ is more ubiquitously expressed , it may be that PR55γ and PR55δ mediate the same biochemical responses to stress in different tissues . Several studies have suggested a role for PP2A in the regulation of c-SRC [43–45] . For instance , both polyoma MT antigen and adenovirus E4orf4 were previously shown to interact with both c-SRC and PR55α independently , but the relevance of these interactions remained elusive [46–48] . To further address the functional relationship between PR55 and c-SRC , we asked whether PR55γ could physically interact with c-SRC . To investigate this we performed coimmunoprecipitation experiments . We found that immunoprecipitation of c-SRC from lysates of cotransfected cells resulted in coprecipitation of PR55γ ( Figure 5A ) . We also detected this interaction reciprocally by immunoprecipitating GFP-tagged PR55γ with a GFP antibody and then probing the blotted precipitate with a c-SRC antibody ( Figure 5B ) . Importantly , endogenous c-SRC also coimmunoprecipitated with GFP-PR55γ ( Figure 5B ) . Together , these data suggest that PR55γ and c-SRC can form a complex in vivo . Since PR55γ binds to c-SRC we asked if PR55γ could form physical complexes with other SRC family members . We cotransfected FLAG-PR55γ with either the c-SRC family kinases LYN or FYN . We found that LYN and FYN do not share the ability of c-SRC to interact with PR55γ in coimmunoprecipitation assays ( Figure 5C and 5D ) To test if the PR55γ/c-SRC interaction was specific for the B subunit PR55γ we cotransfected c-SRC with PR55γ or the PP2A B'' subunit PR72 . As shown in Figure 5E , c-SRC physically associated with PR55γ but failed to coimmunoprecipitate with PR72 . Moreover , the specific binding of c-SRC to the B subunit PR55γ suggests that PR55γ is able to recruit the holoenzyme complex to c-SRC . We therefore asked if PR55γ could mediate binding of the PR65/PP2Ac core dimer to c-SRC . We transfected HEK293 cells with constructs expressing FLAG-SRC , HA-PR65 , and HA-PP2Ac in the presence or absence of GFP- PR55γ and performed coimmunoprecipitation assays for FLAG-SRC . We found that c-SRC formed a complex with the PP2A holoenzyme exclusively in the presence of PR55γ , indicating that PR55γ is required as bridging factor between c-SRC and the PR65/PP2Ac core dimer ( Figure 5F ) . These observations demonstrate that PR55γ specifically interacts with c-SRC and mediates the recruitment of the PR65/PP2Ac core dimer to c-SRC . The physical interaction between PR55γ and c-SRC suggests a role as a modulator of c-SRC activity . Since c-SRC activity is increased following UV irradiation , we asked whether UV irradiation could affect the interaction between PR55γ and c-SRC . We followed the interaction between PR55γ and c-SRC after UV irradiation by performing immunoprecipitation experiments . We found that the interaction between PR55γ and c-SRC was gradually lost over time ( Figure 5G ) demonstrating that the interaction between c-SRC and PR55γ is sensitive to UV irradiation . Since PR55γ appears to regulate JNK activation at the level of c-SRC , we examined the role of PR55γ on c-SRC- activated transcription of a JNK responsive luciferase reporter . We found that suppression of PR55γ enhanced the ability of c-SRC to activate this reporter ( Figure 6A ) . Conversely , overexpression of PR55γ represses the ability of c-SRC to activate this reporter ( Figure 6B ) . Consistent with these results , western blot analyses demonstrate that overexpression of c-SRC causes an increase in JNK phosphorylation after UV ( Figure 6C ) . Moreover , when we cotransfected short hairpins targeting PR55γ in the presence of c-SRC , we observed that suppression PR55γ enhanced the levels of phosphorylated JNK compared to c-SRC alone ( Figure 6D ) . Consistent with this , ectopic expression of PR55γ inhibited the synergistic activation of JNK mediated by c-SRC and UV ( Figure 6E ) . These results demonstrate that PR55γ is able to influence c-SRC-mediated signaling to the JNK pathway . To assess whether PR55γ directly modulates c-SRC kinase activity , we evaluated c-SRC Tyr416 phosphorylation , a hallmark of its activity [2] , by western blot analyses . We found that knockdown of PR55γ could further enhance c-SRC Tyr416 phosphorylation following stimulation by UV irradiation ( Figure 6F ) . In contrast overexpression of PR55γ could reduce c-SRC Tyr416 phosphorylation following stimulation by UV irradiation ( Figure 6G ) . Together , these results suggest that PR55γ is an inhibitor of c-SRC activity . Several studies have indicated that pretreatment with the PP2A inhibitor okadaic acid ( O . A . ) induces the phosphorylation of the PKC phosphorylation site , Ser12 on c-SRC , while simultaneously stimulating c-SRC kinase activity [45 , 49] . To further investigate if PR55γ alters the phosphorylation status of SRCSer12 , we stimulated U2-OS cells with UV irradiation in the presence of [32P] orthophosphate and performed a 2D tryptic phospho-peptide analysis of phosphorylated c-SRC . Indeed , comparisons with tryptic phosphopeptide maps indicate that overexpression of PR55γ decreased the levels of the phosphorylated Ser12 peptide while okadaic acid slightly increased the phosphorylation levels of the Ser12 peptide , when compared to control samples ( Figure 7A–7E ) . Of note peptide maps showed similar patterns to those performed by Moyers et al . [47] . Similar results were observed when the cells were treated with phorbal 12-myristate 13-acetate ( PMA ) and Forskolin , potent activators of PKC and PKA , respectively ( unpublished data ) . From these data we conclude that treatment with either UV or PMA induces the phosphorylation of the PKC site Ser12 on c-SRC and that this specific phosphorylation event is significantly diminished in cells overexpressing PR55γ . Of note , no direct interaction was observed between PR55γ and PKCδ , the kinase that directly phosphorylates Ser12 of c-SRC , as determined by coimmunoprecipitation assays ( unpublished data ) . Since phosphorylation of a specific target sequence has been suggested to be one of the requirements for the targeting of the B regulatory subunit and the subsequent recruitment of the PP2A holoenzyme to the substrate , we wanted to address whether recruitment of PR55γ to c-SRC is dependent upon the phosphorylation status of residue Ser12 . To answer this question we generated a gain-of-function ( SRCS12D ) and a loss-of-function ( SRCS12A ) mutant of this phospho-site . To test if phosphorylation of Ser12 was required for the binding of PR55γ we cotransfected HA-PR55γ with either: FLAG-SRC , FLAG- SRCS12D , or FLAG- SRCS12A in the presence or absence of UV irradiation and performed coimmunoprecipitation assays . As shown in Figure 7F , mutation of serine 12 to an alanine decreased the association with PR55γ compared to wild-type c-SRC , while the association of PR55γ with SRCS12D significantly increased . However , the association of PR55γ with either wild-type c-SRC or the mutant forms of SRCSer12 were completely abolished following UV irradiation . Together these results suggest that phosphorylation of Ser12 is one of the factors determining PR55γ affinity towards c-SRC and demonstrates that the interaction between PR55γ and c-SRC is sensitive to UV irradiation regardless of the presence of a phospho-moiety on Ser12 . To determine whether the effects of PR55γ on SRCSer12 phosphorylation were the cause of PR55γ-mediated c-SRC regulation , we examined the role of SRCSer12 on a JNK-responsive luciferase reporter . We found that overexpression of the SRCS12A mutant significantly inhibited c-SRC's ability to activate a JNK-responsive luciferase promoter ( Figure 7G ) . Conversely , overexpression of SRCS12D enhanced c-SRC's ability to activate the JNK-responsive luciferase promoter ( Figure 7G ) . Since SRCS12D and SRCS12A significantly enhanced and diminished c-SRC's ability to activate the JNK-responsive luciferase promoter respectively , we wanted to determine whether phosphorylation of this site affects c-SRC kinase activity . Kinase activity was assayed by monitoring the levels of phosphorylated enolase as an exogenous substrate . As shown above ( Figure 6F and 6G ) , exposure to UV irradiation increases the activation of c-SRC compared to nonstimulated cells ( Figure 7H ) . However , the c-SRC kinase activity was severely crippled in cells expressing SRCS12A . Furthermore , c-Src kinase activity was significantly enhanced in SRCS12D cells compared to controls in unstimulated cells ( Figure 7H ) . Similar results were detected when measuring the autophosphorylation of c-SRC at Tyr416 ( Figure 7I ) . Taken together these results demonstrate that phosphorylation of Ser12 on c-Src is one of the requirements for full activation of the protein following stress . To determine if the observed effect on JNK activity by PR55γ following UV stimulation is dependent on Ser12 , we cotransfected hairpins targeting PR55γ with wild-type c-SRC or the SRCS12A mutant and measured the levels of phosphorylated JNK by western blotting following UV irradiation . As expected knockdown of PR55γ intensified the effect on JNK phosphorylation compared to c-SRC alone . However , the enhancing effect of PR55γ knockdown on JNK phosphorylation was completely attenuated upon coexpression on the SRCS12A mutant ( Figure 8A ) . In agreement with this result , cotransfection of SRCS12D interfered with PR55γ's ability to inhibit JNK phosphorylation following exposure to UV ( Figure 8B ) . Our data collectively demonstrate that modulation of SRCS12 phosphorylation by PR55γ is critical for PR55γ's effects on JNK activation . We next asked whether SRCS12A could abrogate the enhanced apoptosis observed with knockdown of PR55γ . We cotransfected hairpins targeting PR55γ with either SRCS12A or wild-type c-SRC and quantified apoptosis using DiOC6 staining following exposure to UV irradiation . In line with results above ( Figure 3A ) , knockdown of PR55γ increased UV-induced apoptosis in the presence of wild-type c-SRC . However , coexpression of SRCS12A completely curtailed the enhancing effect of PR55γ suppression ( Figure 8C ) . In contrast , coexpression of PR55γ and c-SRC repressed UV-induced apoptosis compared to c-SRC alone , while these effects were completely abolished when PR55γ was coexpressed with SRCS12D ( Figure 8D ) . Taken together , these results demonstrate that the regulation of c-SRC by PR55γ and its subsequent effects on cell survival are mediated through regulation of Ser12 phosphorylation on c-SRC .
It has previously been proposed that the stress response to environmental stimuli mediated by the JNK pathway is regulated by PP2A protein phosphatase activity [33 , 50 , 51] , although the way in which this pathway is regulated and the specific PP2A holoenzyme responsible for this regulation have not been identified . Via an RNAi-mediated gene family–knockdown screen of regulatory PP2A B subunits , we now identify PR55γ as a specific negative regulator of stress-induced JNK activation . We find that PR55γ regulates the JNK pathway through negative regulation of c-SRC kinase activity . Importantly , we identify here c-SRC serine 12 as a critical residue for the regulation of the c-SRC kinase activity during stress signaling . We show that PR55γ physically interacts with c-SRC and has a higher affinity for c-SRC when it is phosphorylated on serine 12 . Since the interaction of the PP2A holoenzyme complex with c-SRC is dependent on PR55γ , this would suggest a transient interaction between PR55γ-containing PP2A holoenzyme and c-SRC , which is reduced as soon as serine 12 dephosphorylation has occurred . Previous work has indicated that PP2A might play a role in the regulation of c-SRC activity , since treatment of cells with okadaic acid , a chemical inhibitor of PP2A [22] , resulted in enhanced c-SRC activity [45] , and PP2A can inactivate c-SRC in vitro [43] . Interestingly , polyoma MT is able to compete with the PP2A B regulatory subunit for interaction with the PR65/PP2Ac core dimer [38] , and overexpression of polyoma MT is able to activate c-Jun kinase by virtue of its interaction with PP2A [29] . Furthermore , polyoma MT was also found to interact with c-SRC [36] leading to its activation [37] . Moreover , it was reported that adenovirus E4orf4 can interact with both c-SRC and PR55α independently and that the interaction with c-SRC is required for E4orf4 to induce apoptosis [46 , 52] . Overexpression of E4orf4 phenocopies loss of PR55 in yeast [53] , allowing the possibility that inhibition of PR55α is a prerequisite for E4orf4-induced apoptosis in mammalian cells . Our data identifying PR55γ as a negative regulator of c-SRC are in agreement with these studies and could suggest that these viral proteins may function to displace PR55γ from c-SRC . It has previously been reported that JNK is activated by both c-SRC independent and c-SRC dependent pathways [39] . This present study confirms and extends these results by demonstrating that the inhibition of SRC by PR55γ does not completely inhibit JNK activation but rather results in an overall decrease , similar to the effects observed with a kinase dead mutant of c-SRC . In contrast , knockdown of PR55γ increases SRC kinase activity following UV resulting in enhanced levels of phosphorylated JNK . These results suggest that modulation of one of the upstream activator pathways may result in a prolonged and amplified JNK effect . It has previously been shown that the majority of c-SRC is present in the perinuclear region where it was found to be inactive as judged by Tyr 416 phosphorylation [54] . c-SRC was also found in the cytoplasm at lower levels correlating with increasing activity and moved to the membrane in response to various stimuli where it was fully active [54] . Similarly we found PR55γ to be primarily expressed in the perinuclear region indicating that PR55γ may colocalise with c-SRC ( unpublished data ) . These data suggest that PR55γ may interact with c-SRC within the perinuclear region inhibiting the induction of c-SRC by PKC by limiting the phosphorylation status of Ser12 . Since PR55γ did not decrease the overall levels of other phosphorylation sites within the unique region of c-SRC primarily SER17 , phosphorylation of which has been shown to be involved in SRC dependent ERK signaling [55] , it suggests that the selective response of c-SRC following PKC phosphorylation at Ser12 may reflect the restricted activation of the JNK downstream effector pathway through either , phosphorylation dependent changes in subcellular localization , as suggested by Liebenhoff et al . who demonstrated that cytoskeletal association of pp60c-src is dependent on phosphorylation of pp60c-src at Ser12 by PKC [56] or by regulation of the binding of proteins that may function to regulate the activity of c-SRC towards JNK . One of the intriguing findings of this study is that upon treatment of cells with UV irradiation the interaction between c-SRC and PR55γ is lost . We propose a model in which we suggest that in response to stress c-SRC is activated in part by losing the interaction with its inhibitor allowing c-SRC to be localized to the plasma membrane and subsequent activation of the downstream JNK effector pathways ( Figure 9 ) . Similarly , it was described for PR55α that its interaction with the PP2Ac/PR65 dimer is sensitive to gamma irradiation [57] . Further work will be required to reveal the mechanism of UV-induced dissociation of the c-SRC/PR55γ in response to stress .
pcDNA3- FLAG-SRC , pEGFP-SRC , pMT-SRC , pMT-SRC ( 527 ) , pMT-SRC ( 295 ) , pcDNA-LYN , and pcDNA-FYN ( Table S2 ) were kindly provided by P . Stork , G . Superti-Furga , W . Molenaar , and J . Borst . All other Flag- , GFP- , and HA-coding constructs were generated using pcDNA ( Invitrogen ) . Detailed cloning information will be provided upon request . PP2A knockdown library vectors were generated by annealing the individual oligonucleotide primer pairs and cloning them into pSuper as described in [58] . The bacterial colonies of each B subunit were then pooled and used for plasmid preparation . The extra shRNA ( E ) that gave the most efficient knockdown against PR55γ as described in Figure 2A was generated by ligating synthetic oligos ( Sigma ) against the target sequence 5′-CATGGAGGCAAGACCCATAG-3′ into pSuper . The c-SRC knockdown sequence was obtained from Gonzalez et al . and cloned into pSuper [59] . Antibodies anti-p-JNK , anti-p-MKK-4 , anti-p-Src ( 416 ) , and cleaved caspase-3 were from Cell Signaling; anti-SRC , anti-JNK ( C-17 ) , anti-MKK4 , HA ( Y11 ) , anti-GFP , and anti-FYN were purchased from Santa Cruz Biotechnology Inc . The anti-LYN antibody was a kind gift from J . Borst . All cells were cultured in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum , D-Glutamate , and Penicillin/Streptomycin . U2-OS cells were divided in 10-cm dishes 1 d prior to transfection . Subconfluent cells were transfected using the calcium phosphate transfection method [60] . Cells were incubated overnight , washed in PBS , and puromycin selected ( 1 . 5 μg/ml ) for 48 h . When required cells were serum starved for 48 h prior to stimulation . The cells were not allowed to reach confluency . For the screen and subsequent knockdown experiments , U2-OS cells were cotransfected with 20 μg of pooled PP2A shRNAs and 1 μg of pBabe-puro . After 72 h , selected cells were trypsinized and 5 × 105 cells were plated out in a 10-cm dish . After incubating overnight cells were exposed to UV irradiation ( 100 J/m2 ) and incubated for a further 60 min in the same medium . The following agents were used to stimulate cells: 50 ng/ml EGF ( Upstate ) , 10 ng/ml TNF ( Sigma ) , 10 ng/ml Insulin ( Sigma ) , 500 mM NACL , or UVC ( 254 nm , 100 J/m2 ) . Luciferase assays were performed using the Dual luciferase system ( Promega ) . AP1 luciferase vector ( 300 ng ) was transfected in the presence of CMV-c-SRC ( 0 . 5 μg ) or a control vector and CMV-Renilla ( 0 . 25 μg ) . For loss of function , 2 . 5 μg of pSuper vector [58] was cotransfected , and luciferase counts were measured 72 h post-transfection using a TD-20/20 Luminocounter ( Promega ) . For gain-of-function assays , 0 . 5 μg of CMV construct or control vector ( empty CMV ) was cotransfected , and luciferase counts were measured 48 h post-transfection . For detection of apoptotic cells , selected cells were incubated for 72 h , trypsinized , and incubated for another 10–12 h in new media . The cells were washed twice in PBS and incubated for 18–24 h following UV treatment ( 50 J/m2 ) , trypsinized , washed once with PBS , and resuspended for 10–15 minutes in 250 μl PBS containing 40 nM DiOC6 ( 3 ) . After incubation the cells were analyzed by FACS analysis . Cells were lysed in solubilizing buffer ( 50 mM Tris [pH 8 . 0] , 150 mM NaCl , 1 % NP-40 , 0 . 5% deoxycholic acid , 0 . 1% SDS , 1 mM Sodium Vanadate , 1 mM pyrophosphate , 50 mM sodium fluoride , 100 mM β-glycerol phosphate ) , supplemented with protease inhibitors ( Complete; Roche ) . Whole cell extracts were then separated on 7%–12% SDS-Page gels and transferred to polyvinylidene difluoride membranes ( Millipore ) . Membranes were blocked with bovine serum albumin and probed with specific antibodies . Blots were then incubated with an HRP-linked second antibody and resolved with chemiluminescence ( Pierce ) . For coimmunoprecipitations , cells were lysed in ELB ( 0 . 25 M NaCl , 0 . 1%NP-40 , 50 mM HEPES [pH 7 . 3] ) supplemented with protease inhibitors . Lysates were then incubated for 2 h with 2 μg of the indicated antibodies conjugated to protein A or protein G sepharose beads , washed three times in ELB buffer , and separated on SDS-PAGE gels . When appropriate cell lystates were immunoprecipitated with ANTI-FLAG M2 Affinity Gel ( Sigma ) . For tryptic phosphopeptide analysis U2-OS cells were cotransfected with 4 μg Flag-Src or 4μg or Flag-Src ( 12A ) and 20 μg PR55γ or control vector . Cells were phospho-starved for 45 min and 2 mCi of [32 P] orthophosphate was then added to the cells and incubated an additional 3 h . PMA at a final concentration of 200 nM was added for 30 min at 37 °C or the cells were treated with UV irradiation ( 100 J/m2 ) and incubated for a further 30 min at 37 °C . c-SRC was immunoprecipitated with Flag antibody ( Sigma ) as described above . The entire sample was loaded onto an SDS-PAGE gel , run , and then dried . The film was then exposed for 3 h at room temperature . The radioactive bands were then isolated , proteins eluted , digested with trypsin , and phosphopeptide mapping was performed as described previously [61 , 62] . For phospholabeling analysis HEK 293 cells were cotransfected with 4 μg Flag-PR55γ , 10 μg HA-PR65 , and 10 μg HA-PP2Ac . Cells were phospho-starved for 45 min , UV stimulated as above , and then 2 mCi of [32 P] orthophosphate was added to the cells and incubated for a further 2 h in the same medium . c-SRC was immunoprecipitated with Flag antibody ( Sigma ) and an HA antibody ( Y11 , Santa Cruz ) as described above . The entire sample was loaded onto an SDS-PAGE gel , run , and then dried . | Protein Phosphatase type 2A ( PP2A ) represent a family of holoenzyme complexes involved in wide range of activities such as growth , differentiation , and cell death . The PP2A holoenzyme complex is made up of a catalytic , a structural , and one of various “B” subunits . These “B” subunits are thought to provide the substrate specificity required for PP2A activity . Previous work on PP2A has mostly been derived by inhibiting the catalytic subunit through chemical inhibition , as such inhibiting all of the pathways associated with PP2A . To identify individual “B” subunits involved in specific cellular processes we have generated a “B” subunit gene knockdown library , which allows us to inhibit each of the known “B” subunits individually . One of the many pathways regulated by PP2A is the c-Jun NH2-terminal kinase ( JNK ) kinase pathway , which , depending on stimulus , can affect either cell survival or cell proliferation . Here we report that the “B” subunit PR55γ acts as a negative regulator of JNK activity and cell death . We show that PR55γ influences JNK activity by inhibiting one of its upstream regulators , the proto-oncogene c-SRC , through dephosphorylation at one of the key residues on c-SRC , a site we show to be critical for c-SRC activation following cell stress . Overall our work describes the novel function of a specific PP2A subunit involved in cell survival and identifies a novel mechanism of c-SRC regulation . | [
"Abstract",
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] | 2007 | A RNA Interference Screen Identifies the Protein Phosphatase 2A Subunit PR55γ as a Stress-Sensitive Inhibitor of c-SRC |
Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random ( MNAR ) . Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses . However , few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics . Thus , a practical left-censored missing value imputation method is urgently needed . We developed an iterative Gibbs sampler based left-censored missing value imputation approach ( GSimp ) . We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline . The results show that GSimp outperforms other imputation methods in terms of imputation accuracy , observation distribution , univariate and multivariate analyses , and statistical sensitivity . Additionally , a parallel version of GSimp was developed for dealing with large scale metabolomics datasets . The R code for GSimp , evaluation pipeline , tutorial , real-world and simulated targeted metabolomics datasets are available at: https://github . com/WandeRum/GSimp .
Missing values are commonly existed in mass spectrometry ( MS ) based metabolomics datasets . Many statistical methods require a complete dataset , which makes missing data an inevitable problem for subsequent data analysis . Generally speaking , missing at random ( MAR ) , missing completely at random ( MCAR ) , and missing not at random ( MNAR ) are three commonly accepted missing types [1 , 2] . When the probability of a missing value is depended on other observed variables but not the value itself , it is regarded as MAR [1 , 2] ( e . g . , false peak matching , deconvolution of co-eluting compounds ) . MCAR is from completely unexpected missingness without any relationships with other variables ( e . g . , stochastic fluctuations , random errors from incomplete derivatization or ionization ) . Targeted metabolomics studies have been widely used for the accurate quantification of specific groups of metabolites . Due to the limit of compound quantifications ( LOQ ) , missing values are usually caused by signal intensities lower than LOQ , also known as left-censored missing , which can be assigned to MNAR . The processing of missing values has been developed and studied in MS data , which is an indispensable step in the metabolomics data processing pipeline [3] . One simple solution is the substitution of missing by determined values , such as zero , half of the minimum value ( HM ) or LOQ/c where c denotes a positive integer . Determined value substitutions , although commonly applied for dealing with missing values in metabolomics studies [4–6] , can significantly affect the subsequent statistical analyses in different ways ( e . g . , underestimate variances of the variable , decrease statistical power , fabricate pseudo-clusters among observations , etc . ) [1] . Advanced statistical imputation methods have been developed for high-dimensional–omics studies ( e . g . , k-nearest neighbors ( kNN ) [7] , singular value decomposition ( SVD ) [8 , 9] , random forest ( RF ) [10] ) that are available to users on several metabolomics data analysis software [11–15] . MetaboAnalyst [15–17] is a popular metabolomics data processing web-tool providing kNN , Probabilistic PCA ( PPCA ) , Bayesian PCA ( BPCA ) , SVD , or substitution by determined values ( HM , mean , median , minimum ) . However , most advanced statistical imputation methods are mainly aiming at imputing MCAR/MAR and not suitable for the situation of MNAR . So far , a limited number of approaches dealing with left-censored missing values were applied by researchers [18 , 19] . Quantile regression approach for left-censored missing ( QRILC ) imputes missing data using random draws from a truncated distribution with parameters estimated using quantile regression [18] . Although this imputation keeps the overall distribution of missing parts compared to determined value substitutions , it may produce stochastic imputed values since no extra information is used for the prediction of missing parts . Another imputation method recently developed for MNAR is k-nearest neighbor truncation ( kNN-TN ) [19] . This approach applies Maximum Likelihood Estimators ( MLE ) for the means and standard deviations of missing variables based on truncated normal distribution . Then a Pearson correlation based kNN imputation method was implemented on standardized data . Although the author stated that kNN-TN could impute both MNAR and MAR , the imputed values were entirely dependent on the nearest neighbors while no constraint was placed upon the imputation . Thus , this approach might cause an overestimation of MNAR missing values . To reduce adverse effects caused by missing values in following metabolomics data analyses , we developed a left-censored missing value imputation framework , GSimp , where a prediction model was embedded in an iterative Gibbs sampler . Next , we compared GSimp with HM , QRILC , and kNN-TN on two real-world metabolomics datasets and one simulation dataset to demonstrate the advantages of GSimp regarding imputation accuracy , observation distribution , univariate and multivariate analysis [20] , and sensitivity . Our findings indicate that GSimp is a robust method in handling left-censored missing values in targeted metabolomics studies .
A variable containing missing elements from free fatty acids ( FFA ) dataset was randomly selected to track the sequence of corresponding parameters and estimates across the first 500 iterations out of a total of 2000 ( 100 × 20 ) iterations using GSimp . From Fig 1 , we can observe that both fitted value ŷ and sample value ỹ reach to the convergence after several iterations and the standard deviation estimate σ drop to a steady state of small values . In addition , an upper constraint for the distribution of ỹ indicated that it was drawn from a truncated normal distribution . We evaluated four different MNAR imputation/substitution methods on FFA , bile acids ( BA ) targeted metabolomics and simulation datasets . First , we measured the imputation performances using label-free approaches . Sum of ranks ( SOR ) was used to measure the imputation accuracy regarding the imputed values of each missing variable . From the upper panel of Fig 2 , we can observe that GSimp has the best performance with the lowest SOR across all varying numbers of missing variables in both FFA and BA datasets . To measure the extent of imputation induced distortion on observation distributions , the PCA-Procrustes analysis was conducted between the original data and imputed data . The lower panel of Fig 2 shows that GSimp has the lowest Procrustes sum of squared errors compared to other methods , which means GSimp kept the overall observation distribution of original dataset with the least distortions . Then , we measured the imputation performances with clinical group information provided . We compared the results of univariate and multivariate analyses for imputed and original datasets . Since this is a case-control study , student’s t-tests were applied for univariate analyses . Then we compared the results by calculating Pearson’s correlation between log-transformed p-values calculated from imputed and original data for missing variables . Again , GSimp performs best with the highest correlations among four methods ( upper panel of Fig 3 ) along with different numbers of missing variables , and it implies GSimp keeps the most original biological variations regarding the univariate analyses results . For the multivariate analyses , we applied PLS-DA to distinguish the group differences . Similarly , we conducted PLS-Procrustes analysis while PLS was employed as a supervised dimension reduction technique . The lower panel of Fig 3 demonstrates that GSimp preferably restores the original observation distribution with the lowest Procrustes sum of squared errors among four imputation methods . On the simulation dataset , we compared QRILC , kNN-TN , and GSimp using same approaches . Consistent results were recognized ( S1 Fig ) , and GSimp presents the best performances on the simulation dataset with the lowest SOR and PCA/PLS-Procrustes sum of squared errors and the highest correlation of univariate analysis results . Moreover , to examine the influences of statistical power using different imputation methods , we calculated the true positive rate ( TPR ) as the capacities to detect differential variables on different imputation datasets . Again , with both p-cutoff of 0 . 05 and 0 . 01 , GSimp shows the overall highest TPR over different missing numbers ( Fig 4 ) . This implies that GSimp impairs the sensitivity to the least extent among three methods , which is reasonable since GSimp also keeps the highest correlation of p-values in previous comparisons .
The purpose of this study is to develop a left-censored missing value imputation approach for targeted metabolomics data analysis . We evaluated GSimp with other three imputation methods ( kNN-TN , QRILC , and HM ) and suggested that GSimp was superior to others using different evaluation methods . To illustrate the performance of GSimp , we randomly selected one variable containing missing values from FFA dataset ( Fig 5 ) to compare the imputed values and original values . Although determined value substitution ( e . g . , HM ) were widely used by researchers in the field of metabolomics , our results indicated that HM could severely distort the data distribution ( upper left panel of Fig 5 ) , thus impairing subsequent analyses . In comparison , QRILC kept the overall data distribution and variances ( upper right panel of Fig 5 ) . However , stochastic values would be generated by this approach since QRILC imputes each missing variable independently without utilizing the predictive information from other variables . Statistical learning based method , kNN-TN , applied a correlation based kNN algorithm with parameters of missing variables estimated with truncated normal distributions . This method utilized the information of highly correlated variables of targeted missing variable , thus kept a linear trend between original values and imputed values . However , since no constraint was applied for the imputation , a right shift of missing part occurs , causing imputed values to exceed the truncation point ( lower left panel of Fig 5 ) . In contrast , GSimp utilized the predictive information of other variables by employing a prediction model and held a truncated normal distribution for each missing element simultaneously , which ensured a favorable linear trend between imputed and original values as well as a reasonable bound for the imputed values ( lower right panel of Fig 5 ) . In this study , we comprehensively evaluated our algorithm on targeted metabolomics datasets for the MNAR situation . We additionally tested a non-targeted GC/MS profiling metabolomics dataset and found that most of missing values are manually retrievable due to the miss-identification of peaks . These retrievable missing elements were randomly distributed across the dataset and irrelevant to their true abundances ( S1 Appendix ) . Based on this , we assumed the majority of missing values are MCAR/MAR situation for non-targeted GC/MS data before manually missing retrieval . For the rest un-retrievable missing elements , we found much lower signal to noise ratios which could be assigned to the situation of left-censored MNAR . Thus , for non-targeted profiling datasets , missing retrieval from raw spectral data will be most recommended . Since we applied the minimum observed value of missing variable as an informative upper truncation point and -∞ as a non-informative lower truncation point for left-censored missing , GSimp with this default settings might be applicable for the imputation of post-missing retrieval non-targeted data . GSimp is more than that , other truncation values could also be applied in real-world analyses , such as known LOQ/LOD of metabolites or quantile of observed values ( e . g . , 10% ) can be set as upper truncation points for different conditions . Additionally , when signal intensity of certain compound is larger than the upper limit of quantification range or saturation during instrument analysis , an informative lower truncation point could be correspondingly applied for the right-censored missing value . What’s more , when non-informative bounds for both upper and lower limits ( e . g . , +∞ , -∞ ) were applied , GSimp could be extended to the situation of MCAR/MAR . With the flexible usage of upper and lower limits , our approach may provide a versatile and powerful imputation technique for different missing types . For other–omics datasets with missing values ( especially MNAR ) ( e . g . , single cell RNA-sequencing data ) , we could also apply this method with few modifications of default settings . Thus , it is worthy to evaluate our approach , GSimp , in other complex scenarios in the future . Since GSimp employed an iterative Gibbs sampler method , a large number of iterations ( iters_all = 20 , iters_each = 100 ) are preferable for the convergence of parameters in Markov chain Monte Carlo ( MCMC ) method . However , as we tested on the simulation dataset with different number of iterations , a smaller number of iterations ( iters_all = 10 , iters_each = 50 ) won't severely affect the imputation accuracy ( S2 Fig ) . Among iterations for the whole data matrix , we applied a sequential imputation procedure for missing variables from the least number of missing values to the most . To improve the computational efficiency of GSimp on large scale datasets , we additionally implemented a parallel version which can run Gibbs sampler on multiple missing variables simultaneously , then update all imputed values of missing elements . Increasing the number of cores will significantly decrease the running time of GSimp as we tested on a random generated dataset ( S1 Table ) . In conclusion , we developed a new imputation approach GSimp that outperformed traditional determined value substitution method ( HM ) and other approaches ( QRILC , and kNN-TN ) for MNAR situations . GSimp utilized predictive information of variables and held a truncated normal distribution for each missing element simultaneously via embedding a prediction model into the Gibbs sampler framework . With proper modifications on the parameter settings ( e . g . , truncation points , pre-processing , etc . ) GSimp may be applicable to handle different types of missing values and in different -omics studies , thus deserved to be further explored in the future .
We employed datasets from a study of comparing serum metabolites between obese subjects with diabetes mellitus ( N = 70 ) and healthy controls ( N = 130 ) where N represents the number of observations . Dataset 1: a total of 42 free fatty acids ( FFAs ) were identified and quantified in those participants in order to evaluate their FFA profiles [21] . Dataset 2: a total of 34 bile acids ( BAs ) were identified and quantified in a similar way using different analytical protocol [22] . For the simulation dataset , we first calculated the covariance matrix Cov based on the whole diabetes dataset ( P = 76 ) where P represents the number of variables . Then we generated two separated data matrices with the same number of 80 observations from multivariate normal distributions , representing two different biological groups . For each data matrix , the sample mean of each variable was drawn from a normal distribution N ( 0 , 0 . 52 ) and Cov was kept using SVD . Then , two data matrices were horizontally ( column-wise ) stacked together as a complete data matrix ( N×P = 160×76 ) so that group differences were simulated and covariance was kept . For two real-world targeted metabolomics datasets , we generated a series of MNAR datasets by using the missing proportion ( number of missing variables/number of total variables ) from 0 . 1 to 0 . 6 in a step of 0 . 05 with quantile cut-off for each missing variable drawn from a uniform distribution U ( 0 . 1 , 0 . 5 ) . The elements lower than the corresponding cut-off were removed and replaced with NA . For the simulation dataset , we generated a series of MNAR datasets by using the missing proportion from 0 . 1 to 0 . 8 step by 0 . 1 with MNAR cut-off drawn from U ( 0 . 3 , 0 . 6 ) for a more rigorous testing . A prediction model was employed for the prediction of missing values by setting a targeted missing variable as outcome and other variables as predictors . Different prediction models ( e . g . , linear regression , elastic net [23] , regression trees [24] and random forest [25] , etc . ) could be embedded in our imputation framework . Elastic net was applied in our approach as an ideal prediction model considering its stability , accuracy , and efficiency . This model is a regularized regression with the combination of L1 and L2 penalties of the LASSO [26] and ridge [27] methods . The estimates of regression coefficients in elastic net are defined as β^=argminβ ( ‖y−Xβ‖2+λ[ ( 1−α ) /2‖β‖22+α‖β‖1] ) ( 1 ) The L2 penalty ( 1−α ) /2‖β‖22 improves the model’s robustness by controlling the multicollinearities among variables which are widely existed in high-dimensional–omics data . And the L1 penalty α‖β‖1 controls the number of predictors by assigning zero coefficients to the "unnecessary" predictors . From a Bayesian point of view , the regularization is a mixture of Gaussian and Laplacian prior distributions of coefficients which can pull the full model of maximum likelihood estimates argminβ‖y−Xβ‖2 towards the null model of prior coefficients distribution , thus controls the risk of overfitting and increase the model robustness . R package glmnet was used for the elastic net . We set hyperparameters λ as 0 . 01 ( default setting for high-dimensional data ) and α as 0 . 5 ( an equally mixture of LASSO and ridge penalties ) [28] . Gibbs sampler is a MCMC technique that sequentially updates parameters while others are fixed . It can be used to generate posterior samples . For each missing variable in the dataset , we applied a Gibbs sampler to impute the missing values by sampling from a truncated normal distribution with prediction model fitted value as mean and root mean square deviation ( RMSD ) of missing part as standard deviation while truncated by specified cut-points . Assuming we have a n × p data matrix X = ( X1 , X2 , X3 , … , Xp ) with only one variable Xj containing left-censored missing values . We denote Xj as y and the missing part as ym with length m and non-missing part as yf with length f , and the rest of matrix X-j as X’ . We can then set the lower truncation point lo as -∞ ( centralized data ) or 0 ( original data ) and upper hi as the minimum/quantile value of yf or a given LOQ . The truncation bounds ensure imputation results are constrained within [lo , hi] . Then , the Gibbs sampler approach can be described as following steps: We iteratively repeat step-2 to step-4 and update Xj . A whole data matrix X = ( X1 , X2 , X3 , … , Xp ) contains a number of k ( k ≤ p ) left-censored missing variables . We present our imputation framework as following algorithm . Algorithm: Gibbs sampler based left-censored missing value imputation approach Require: X an n × p data matrix , iters_all the number of iterations for imputing the whole matrix X , iters_each the number of iterations for imputing each missing variable , a vector of upper limits U ( +∞ for non-missing variables ) with length p and a vector of lower limits L ( -∞ for non-missing variables ) with length p . 1 . Ximp ← initialize the missing values for X; 2 . K ← vector of indices of missing variables in X with increasing amount of missing values; 3 . for 1:iters_all do 4 . for j in K do 5 . y′ ← Xjimp , y′ can be divided into two parts: ym′ is a vector of the imputed part ( original missing part ) with length m and yf′ is a vector of the non-missing part with length f while n = m + f; 6 . X′ ← X−jimp , represents the matrix X with jth column removed; 7 . lo ← Lj and hi← Uj; 8 . for 1:iters_each do 9 . Gibbs sampler step 2 to 4; 10 . end for 11 . Update Xjimp; 12 . end for 13 . end for 14 . return Ximp Other three left-censored missing imputation/substitution methods were conducted in our study for performance comparison: Normalized Root Mean Squared Error ( NRMSE ) [30] has been commonly used to evaluated the differences between true values and imputed values . Considering the skewed distribution of missing values in MNAR , NRMSE based sum of ranks ( SOR ) was derived , a robust non-parametric measurement , to compare different imputation methods . The formula is as follows [31]: SOR=∑i=1kRanki ( NRMSE ) ( 2 ) where Ranki ( NRMSE ) represent the NRMSE ranks of different imputation methods in ith missing variable . Procrustes analysis , a statistical shape analysis , could be used to evaluate the similarity of two ordinations by calculating the sum of squared errors [32] . We applied principal component analysis ( PCA ) as the unsupervised ( un-labeled ) ordination measurement and Procrustes analysis to measure the alteration of the original sample distribution and the imputed sample distribution in the space of top PCs . R package vegan was applied for Procrustes analysis [33] . Labeled measurements include correlation analysis for log-transformed p-values between true data and imputed data from Student’s t-test , partial least square ( PLS ) - Procrustes analysis that measures the differences between original and imputed sample distributions on top PCs using supervised PLS for the dimensional reduction . R package ropls was applied for PLS analysis [34] . These measurements were done using our imputation evaluation pipeline from our previous study [31] , which is also accessible through: https://github . com/WandeRum/MVI-evaluation . Furthermore , we evaluated the impacts of different imputation methods on the statistical sensitivity of detecting biological variances . On the simulation dataset , we calculated p-values from student’s t-tests between two groups from original and imputed datasets . We marked a set S as real differential variables at a significant level of p-cutoff ( e . g . , 0 . 05 ) from original simulation data , and a set S’ as detected differential variables at the same significant level from imputed simulation data . Then we calculated the true positive rate TPR=#of ( S∩S′ ) #ofS to evaluate the effects of different imputation methods in terms of detecting differential variables . | Missing values caused by the limit of detection/quantification ( LOD/LOQ ) were widely observed in mass spectrometry ( MS ) -based targeted metabolomics studies and could be recognized as missing not at random ( MNAR ) . MNAR leads to biased parameter estimations and jeopardizes following statistical analyses in different aspects , such as distorting sample distribution , impairing statistical power , etc . Although a wide range of missing value imputation methods was developed for–omics studies , a limited number of methods was designed appropriately for the situation of MNAR currently . To alleviate problems caused by MNAR and to facilitate targeted metabolomics studies , we developed a Gibbs sampler based missing value imputation approach , called GSimp , which is public-accessible on GitHub . And we compared our method with existing approaches using an imputation evaluation pipeline on both of the real-world and simulated metabolomics datasets to demonstrate the superiority of our method from different perspectives . | [
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... | 2018 | GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies |
Mechanisms by which hepatitis C virus ( HCV ) evades cellular immunity to establish persistence in chronically infected individuals are not clear . Mutations in human leukocyte antigen ( HLA ) class I-restricted epitopes targeted by CD8+ T cells are associated with persistence , but the extent to which these mutations affect viral fitness is not fully understood . Previous work showed that the HCV quasispecies in a persistently infected chimpanzee accumulated multiple mutations in numerous class I epitopes over a period of 7 years . During the acute phase of infection , one representative epitope in the C-terminal region of the NS3/4A helicase , NS31629-1637 , displayed multiple serial amino acid substitutions in major histocompatibility complex ( MHC ) anchor and T cell receptor ( TCR ) contact residues . Only one of these amino acid substitutions at position 9 ( P9 ) of the epitope was stable in the quasispecies . We therefore assessed the effect of each mutation observed during in vivo infection on viral fitness and T cell responses using an HCV subgenomic replicon system and a recently developed in vitro infectious virus cell culture model . Mutation of a position 7 ( P7 ) TCR-contact residue , I1635T , expectedly ablated the T cell response without affecting viral RNA replication or virion production . In contrast , two mutations at the P9 MHC-anchor residue abrogated antigen-specific T cell responses , but additionally decreased viral RNA replication and virion production . The first escape mutation , L1637P , detected in vivo only transiently at 3 mo after infection , decreased viral production , and reverted to the parental sequence in vitro . The second P9 variant , L1637S , which was stable in vivo through 7 years of follow-up , evaded the antigen-specific T cell response and did not revert in vitro despite being less optimal in virion production compared to the parental virus . These studies suggest that HCV escape mutants emerging early in infection are not necessarily stable , but are eventually replaced with variants that achieve a balance between immune evasion and fitness for replication .
Hepatitis C virus ( HCV ) currently infects an estimated 3% of the world's population ( ∼170 million people ) [1] , [2] , causing a myriad of health problems including fibrosis and cirrhosis of the liver [3] , [4] . Infection considerably increases the probability of hepatocellular carcinoma , and HCV-related hepatic disease has become the leading cause of orthotopic liver transplantation in the United States [5] . The majority of those infected with the virus are unable to spontaneously resolve the infection despite the presence of humoral and cellular immune responses that are at least occasionally robust [6]–[8] . There have been many reasons proposed as to why the immune system fails in the face of chronic HCV infection , including early T cell exhaustion , particularly of the CD4+ helper subset [9] , [10] , dendritic cell ( DC ) dysfunction [11] , [12] , impairment of effector cells [6] , [13] , [14] , and cytotoxic T lymphocyte ( CTL ) viral epitope escape [15]–[18] . Like most small RNA viruses , HCV has an extremely high replication rate ( ∼1010–1012 virions/d , [19] ) , and the highly error prone NS5B polymerase allows for robust production of minor viral variants that may outpace cellular immune responses [6] , [20] , [21] . These variants are under constant immune pressure in the infected host , and Darwinian selection processes lead to domination of the viral quasispecies by the most fit virus that can also evade immune recognition . Viremia varies widely in individuals with chronic HCV infection with steady state values that range from a few thousand to several million genomes per milliliter of plasma . Factors that regulate virus load in persistent HCV infection are not known but could conceivably influence the rate and severity of progressive liver disease . CTL mutational escape could have positive or negative effects on virus replication depending on the site and nature of the amino acid substitution ( s ) within structural or non-structural HCV proteins . Some substitutions might be expected to result in loss of immune control and thus higher levels of virus production , but it is also plausible that mutations facilitating CTL escape have negative consequences for replication if they impair production , assembly , or release of virions . Impaired replicative fitness as a result of escape mutation has been associated with reduced viremia and slower disease progression in HIV-1-infected humans and SIV-infected rhesus macaques [22]–[25] . Despite the importance of CTL epitope viral mutation for immune evasion , in HCV infection many highly targeted epitopes have a low mutation frequency . Epitopes such as HLA-A2 restricted NS31073–1081 are consistently targeted by CD8+ T cells , but amino acid mutations facilitating immune evasion are rarely observed [26] , [27] . Since the NS3 protein shares both protease and NTPase-dependent helicase functions , it has been proposed that mutations in these epitopes may be lethal to the virus [28] . However , few studies have examined how CTL escape directly correlates with HCV fitness . Cell culture models of HCV replication utilizing viral replicons have been valuable in identifying adaptive mutations that facilitate robust replication in hepatocytes in vitro [29] , [30] . Using this tool along with recently developed systems allowing actual infection rather than just replication [31]–[34] , we extend these models to study the impact of CTL escape mutation on virus replication and virion production . In this report , we assessed the evolution of a dominant MHC class I epitope during the acute and chronic phases of infection in a chimpanzee studied through seven years of follow-up . A C-terminal epitope of the NS3 protein , NS31629–1637 , restricted by the chimpanzee Patr-B1701 molecule , has previously been shown to serially acquire several distinct mutations in amino acid residues that impair MHC binding or TCR recognition [15] , [35] , [36] . The availability of longitudinal samples from this chimpanzee facilitated a careful examination of epitope evolution and an integrated assessment of the fitness of viral variants that arose in vivo , as well as the host immune response directed against these variants . Our results indicate that genomes encoding CTL escape mutations that emerge early in infection are not necessarily optimized for replication and are eventually replaced by variants that successfully balance escape from cellular immune pressure and replicative fitness in the chronic phase of infection . We predict that this could be an important factor influencing virus load in HCV-infected chimpanzees and humans , with as yet unknown consequences for liver disease progression .
Wild-type ( GAVQNEITL ) and mutant ( GAVQNEITP , GAVQNEITS , GAVQNETTL ) NS31629–1637 peptides were synthesized by Genemed Biosynthesis ( San Francisco , CA ) and purified by high-performance liquid chromatography ( HPLC ) . All peptides were stored at a concentration of 1 mg/ml at –20°C . Huh-7 . 5 cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum ( FBS , Hyclone , Logan , UT ) at 37°C in 5% CO2 . The Huh-7 . 5/B1701 cell line was generated as follows . Huh-7 . 5 cells were trypsinized , washed with DMEM-10 media , and 4×106 cells electroporated with 2 . 5 µg pcDNA3 . 1Zeo ( - ) B1701 plasmid using the AMAXA T-028 program ( AMAXA , Gaithersburg , MD ) . Cells were resuspended in DMEM-10 , plated in a p100 petri dish , and allowed to rest for 24 h before addition of 300 µg/ml zeocin ( Invitrogen , Carlsbad , CA ) . Cell foci surviving selection were trypsinized , transferred to a 24-well plate , and allowed to grow under selection up to a p150 petri dish . Huh-7 . 5/B1701 cells were stained with 20 µl of anti-human pan-HLA-A , B , C FITC-conjugated antibody ( BD Biosciences , San Jose , CA ) in FACS buffer ( 0 . 5% ( w/v ) bovine serum albumin+1% ( v/v ) of 10% sodium azide in PBS ) in parallel with untransfected Huh-7 . 5 cells , and visualized on a Becton Dickinson FACScalibur flow cytometer . Huh-7 . 5 and Huh-7 . 5/B1701 were transfected with replicons harboring wild-type or mutated NS31629–1637 epitopes as previously described [29] , [30] . The NS31629–1637-specific CD8+ T cell clone has been previously described [15] , and was stimulated using αCD3 mAb ( Immunotech , Beckman Coulter , Fullerton , CA ) in a ratio of 1×106 CD8+ clone to 2×106 irradiated peripheral blood mononuclear feeder cells ( PBMC ) and maintained in RPMI media supplemented with 10% FBS , Gentamicin ( Gibco , Invitrogen , Carlsbad , CA ) , Penicillin/Streptomycin ( Lonza , Walkersville , MD ) , T-stim culture supplement ( human-no PHA , BD Biosciences , San Jose , CA ) and human recombinant IL-2 ( rIL-2 , Roche , Indianapolis , IN ) . Autologous chimpanzee B cells were EBV-transformed following established protocols using whole blood and conditioned medium from the marmoset cell line B95-8 [38] . Huh-7 . 5 cells and Huh-7 . 5/B1701 cells with or without subgenomic replicons were lysed directly on 6-well plates using 150 µl lysis buffer ( 100 mM Tris pH 6 . 8 , 20 mM dithiothreitol , 4% ( w/v ) sodium dodecyl sulfate , 20% ( v/v ) glycerol , 0 . 2% ( w/v ) bromophenol blue ) and passed through a 271/2 gauge needle 3–5 times before being stored at –80°C . Lysates were denatured at 92°C for 10 min , run on 5% stacking/8% resolving SDS-polyacrylamide gels , and transferred to Immobilon-P membranes ( Millipore Corporation , Bedford , MA ) . Membranes were blocked with TBS-T ( 20 mM Tris pH 7 . 4 , 150 mM NaCl , 0 . 1% ( v/v ) Tween-20 ( polyoxyethylene sorbitan monolaurate ) plus 5% ( w/v ) non-fat dry milk , and probed with antibodies against NS3 and NS5 ( Virostat , Portland , ME ) , or β-actin in the same buffer overnight at 4°C . Membranes were washed 5 times with TBS-T , probed with HRP-conjugated secondary antibodies for 1 h at room temperature , washed 5 times , and detected using ECL Western detection reagents ( Amersham Biosciences , Piscataway , NJ ) . Total RNA from 1×106 infected Huh-7 . 5 cells was isolated using an RNeasy Mini Kit ( QIAGEN , Valencia , CA ) . 80 ng of total cellular RNA was used to perform Real-Time Quantitative Reverse Transcription PCR using Taqman® One Step RT-PCR Master Mix Reagents ( Applied Biosystems , New Jersey , USA ) , primers specific for the HCV 5′ NTR ( forward , 10 µM: 5′-CTTCACGCAGAAAGCGCCTA-3′ and reverse , 10 µM: 5′-CAAGCGCCCTATCAGGCAGT-3′ ) , and a probe ( 10 µM: 6-FAM-TATGAGTGTCGTACAGCCTC-MGB NFQ ) . Thermal cycling conditions were designed as follows: 48°C for 30 min , 95°C for 10 min , and 40 cycles of 15 s at 95°C , followed by 1 min at 60°C . All amplification reactions were carried out in duplicate . A standard curve was similarly generated using 10-fold dilutions of pJFH1 RNA transcripts generated by in vitro transcription , DNAse treatment , purification by RNeasy Mini Kit and quantification by spectrophotometry . To determine lysis capability of the NS31629–1637-specific CD8+ T cell clone , Huh-7 . 5/B1701 cells with or without subgenomic replicons and EBV-transformed autologous B cells were spun at 1500 rpm for 5 min in a Beckman Coulter Allegra X-15R centrifuge , media aspirated , and tubes vortexed to resuspend the pellet . Cells were pulsed with 51Cr per standard protocol ( NEN Radiochemicals , Perkin Elmer , Waltham , MA ) for 1 h , and cells not harboring subgenomic replicons were simultaneously pulsed with 1 µg/ml wild-type NS31629–1637 peptide resuspended in a total volume of 100 µl RPMI-10 . Pulsed cells were washed 5 times to eliminate residual radiation and exogenous peptide , and mixed at different effector ( NS31629–1637-specific CD8+ T cell clone ) to target ratios in 200 µl RPMI-10 in a 96-well round-bottom plate . Lysis was allowed to occur for 4 h at 37°C in 5% CO2 before transferring 100 µl of supernatant to a flat-bottom 96-well plate . Supernatants were frozen at –80°C for at least 1 h to eliminate cellular carryover before being counted using a 1450 Microbeta Wallac Trilux liquid scintillation counter ( Perkin Elmer , Waltham , MA ) . To transfect viral RNA , 20 µg of full-length JFHxJ6 Cp7 genomes with or without NS31629–1637 mutations were linearized by 4 h digestion with XbaI and subsequently blunt end-digested with Mung Bean Nuclease ( New England Biolabs , Ipswich , MA ) . Linearized DNA was extracted twice with 25∶24∶1 phenol∶choroform∶isoamyl alcohol pH5 . 2±0 . 2 and once with chloroform , quantified by spectrophotometry , and 2 µg of purified product was RNA transcribed using a MEGAscript T7 High Yield Transcription Kit ( Ambion , Austin , TX ) . RNA was purified again using phenol∶chloroform∶isoamyl alcohol followed by chloroform , and integrity was checked on an agarose gel . After RNA quantification by spectrophotometry , Huh-7 . 5 or Huh-7 . 5/B1701 cells were trypsinized for exactly 3 min , washed twice with ice cold PBS , and resuspended at 2×107 cells/ml . 10 µg of purified RNA was electroporated into 8×106 cells with 5 pulses of 99 µs at 820 V over 1 . 1 s in an ECM 830 electroporator using a 2 mm-gap electroporation cuvette ( BTX Genomics , Harvard Apparatus , Holliston , MA ) . Cells were resuspended in DMEM-10 and plated in 6-well plates . To infect Huh-7 . 5 or Huh-7 . 5/B1701 cells using whole virus , cells were plated at 10–20% confluency in six-well plates . Media was aspirated , and viral supernatants harvested from transfected cells were added to the plates in a volume of at least 200 µl . Plates were placed on a rocker at 37°C and 5% CO2 for 4 h before readdition of media . Huh-7 . 5 and Huh-7 . 5/B1701 that had not been electroporated ( either with subgenomic replicons or full-length viral RNA ) or infected with whole virus were pulsed for 1 h with wild-type or mutated NS31629–1637 peptides at decreasing concentrations as in the 51Cr-release experiments . Cells harboring sugenomic replicons were harvested and used directly . Cells that had been transfected with full-length viral RNA were harvested 4 d post-transfection , and cells that had been infected with whole virus were harvested 5 d post-infection . Cocultures were established in a 24-well plate using a 1∶1 ratio of NS31629–1637-specific CD8+ T cells to APCs , and allowed to incubate overnight at 37°C in 5% CO2 in the presence of GolgiStop ( BD Pharmingen , San Jose , CA ) at a concentration of 1 µl/ml . After incubation , cells were harvested , washed once in FACS buffer , and permeabilized using BD FACS Permeabilizing Solution 2 ( BD Biosciences , San Jose , CA ) . Cells were stained with mouse anti-human monoclonal antibodies to CD3 ( APC-conjugated , BD Pharmingen , San Jose , CA ) , CD8 ( PerCP-conjugated , BD Biosciences , San Jose , CA ) , and IFNγ ( FITC-conjugated , BD Pharmingen , San Jose , CA ) , and visualized on a Becton Dickinson FACScalibur flow cytometer . Data were analyzed using FlowJo software ( Tree Star , Inc ) . 96-well plates were coated with collagen for 1 h and allowed to dry before plating 6×103 naïve Huh-7 . 5 cells/well . Viral supernatants from transfected or infected Huh-7 . 5 or Huh-7 . 5/B1701 cells were collected , passaged through a 0 . 22 µm filter , and used to inoculate cells at 10-fold dilutions . At 3 d post-infection , cells were immunostained for NS5A as previously described [32] . Briefly , the inoculum was removed , and cells were washed twice with PBS before fixation with methanol at –20°C . Cells were then washed twice with PBS , once with PBS+0 . 1% ( v/v ) Tween-20 ( PBS-T ) ( normal wash ) , and blocked for 30 min at room temperature with PBS-T+1% ( w/v ) BSA+0 . 2% non-fat dry milk , followed by an endogenous peroxidase blocking step ( 3% H2O2 ( v/v ) in PBS ) for 5 min at room temperature . Cells were washed normally and stained overnight at 4°C with an anti-NS5A antibody ( 9E10 ) . Cells were washed normally , and incubated for 30 min at room temperature with a 1∶3 dilution of ImmPRESS goat anti-mouse HRP-conjugated antibody ( Vector Laboratories , Burlingame , CA ) . Cells were washed normally once more before being developed using DAB substrate ( Vector Laboratories , Burlingame , CA ) . Titers were determined by calculating the tissue culture infection dose at which 50% of wells were positive for NS5A antigen [39] . Huh-7 . 5/B1701 cells were infected using viral supernatants from day 4 transfected cells . Post-infection , media was aspirated , cells were washed twice with PBS and lysed using Buffer RLT ( QIAGEN , Valencia , CA ) +1% 2-mercaptoethanol ( Fisher Scientific , Pittsburgh , PA ) . Lysates were placed directly onto QIAshredder columns , and total RNA isolated and purified using an RNeasy kit ( QIAGEN , Valencia , CA ) . RNA integrity was quantified using a spectrophotometer , checked on an agarose gel , and 2 µg used in a first-strand cDNA synthesis reaction as follows . Briefly , RNA was incubated with random hexamer primers and 10mM dNTPs at 65°C for 5 min , then placed on ice . First-strand reverse transcriptase buffer , 0 . 1mM DTT and RNase H ( Invitrogen , Carlsbad , CA ) were added to each reaction and allowed to incubate at room temperature for 2 min before the addition of superscript II reverse transcriptase ( Invitrogen , Carlsbad , CA ) . The reaction was allowed to proceed at 42°C for 2 h , and first-strand cDNA was used directly in an NS31629–1637 epitope-specific PCR reaction using primers “211NS3EpiSeqInF” ( 5′-TCGCGTACCTAGTAGCCTACCAAGC-3′ ) and “212NS3EpiSeqInR” ( 5′-GCTGGTTGACGTGCAAGCGGCCGA-3′ ) to generate a 323bp fragment containing the epitope . PCR products were cleaned using a PCR cleanup kit ( QIAGEN , Valencia , CA ) , cloned into Top10 chemically competent cells using a TOPO TA kit ( Invitrogen , Carlsbad , CA ) , and individual clones were sent for sequencing ( Macrogen , Rockville , MD ) . CD8+ T cells were positively isolated from frozen PBMC using the Dynal CD8+ Positive Isolation Kit ( Invitrogen Dynal AS , Oslo , Norway ) according to manufacturer instructions . Approximately 360 , 000 CD8+ T cells were plated in one well of a 24-well plate in 1 ml complete medium ( RPMI 1640 containing 10% AB human serum and 1% penicillin/streptomycin ) . To serve as APCs , 6 million irradiated autologous PBMC were pulsed for 2 h with 5 µg/ml of the GAVQNETTL peptide . After three washes , APCs were resuspended in 1 ml complete medium and mixed with the CD8+ T cells . Cells were incubated at 37°C in 7% CO2 . Every 3 d , 1 ml of the culture medium was replaced with 1 ml of complete medium containing 50 U/ml rIL-2 . On day 20 , CD8+ T cells were plated in a 96-well plate in AIM-V medium ( Aim-V ( Invitrogen , Carlsbad , CA ) supplemented with 2% AB human serum ) and allowed to rest for 8 h . Irradiated EBV-transformed autologous B cells were pulsed for 2 h with 10 µg/ml of peptide for use as APCs . After three washes , APCs were resuspended in Aim-V medium and mixed with CD8+ T cells at a 1∶1 ratio with 1 µg/mL of anti-CD28 and anti-CD49d antibodies ( BD Pharmingen , San Jose , CA ) . After 1 h , GolgiStop ( BD Pharmingen , San Jose , CA ) was added at a concentration of 1 µl/ml and cells were further incubated 16 h at 37°C in 7% CO2 . After incubation , cells were harvested and washed once in FACS buffer . Cells were blocked with PBS-20% human serum and then stained with mouse monoclonal antibodies to CD8 ( APC-conjugated , BD Pharmingen , San Jose , CA ) and CD4 ( Pacific Blue-conjugated , BioLegend ) . After two washes with FACS buffer , cells were stained with Live/Dead Fixable Blue Stain Kit ( Invitrogen , Carlsbad , CA ) . Cells were washed twice with FACS buffer and permeabilized using BD Cytofix/Cytoperm solution ( BD Biosciences , San Jose , CA ) . Cells were then stained with mouse monoclonal antibodies to CD3 ( PerCP-conjugated , BD Pharmingen , San Jose , CA ) and IFNγ ( PE-conjugated , BD Pharmingen , San Jose , CA ) and visualized on a Becton Dickinson LSR flow cytometer . Data were analyzed using FlowJo software ( Tree Star , Inc ) .
It is well established that the Patr-B1701 restricted NS31629–1637 epitope is a dominant target of CD8+ T cells during HCV infection in chimpanzees [15] , [36] . This epitope displayed a complex pattern of evolution throughout the acute and chronic phases of infection in the chimpanzee , and thus is valuable for the study of viral epitope escape and fitness costs associated with increased immune pressure . In chimpanzee CH503 infected with a known inoculum of HCV1/910 , sequence analysis over seven years showed three distinct mutations at three separate timepoints tested in the NS31629–1637 epitope [15] . The wild-type amino acid sequence of this epitope in the input HCV1/910 inoculum was GAVQNEITL ( and from here on referred to as the “parent” epitope , NS31629–1637 ) , and three months post-infection a L1637P variant was found in the animal . Ten months post-infection , this variant had been replaced by two dominant species , I1635T and L1637S , at P7 and P9 respectively . Eventually , L1637S became fixed in this chimpanzee , and was the only variant recovered up to 82 months post-infection ( Figure 1A ) . There is one nucleotide change from leucine to proline and one nucleotide change from proline to serine and hence two changes to occur to change leucine to serine; this perhaps provides mechanistic insight into the early appearance of L1637P and its later replacement by L1637S . We set out to test directly the fitness cost associated with the mutating NS31629–1637 variants by modeling the in vivo infection using the HCV subgenomic replicon system [29] , [30] . Using site-directed PCR mutagenesis , we engineered the mutants in the NS31629–1637 region that had previously been observed in chimpanzees , starting with the original HCV1/910 parental epitope sequence . The mutated replicons on the BB7 backbone were transfected into Huh-7 . 5 cells , which were then plated under neomycin selection at decreasing cell numbers to determine transduction efficiency ( Figure 1A ) . It is important to note that the original amino acid sequence of NS31629–1637 epitope present in the BB7 subgenomic replicon is GAVQNEVTT , and was modified to insert the parental NS31629–1637 epitope of HCV1/910 . Substitution of the parental HCV1/910 NS31629–1637 epitope resulted in a slight decrease of transduction efficiency in this replicon . The P9 mutations GAVQNEITP ( L1637P ) and GAVQNEITS ( L1637S ) , which show a 400-fold decrease in B1701 binding capacity [15] , showed increased susceptibility to neomycin , with L1637P growing the least efficiently . The P7 GAVQNETTL ( I1635T ) mutation was more resistant to selection than the HCV1/910 parental GAVQNEITL NS31629–1637 sequence . These results suggest that mutations in this epitope at P9 severely hinder the replicative capacity of the virus , while an isoleucine to threonine substitution at P7 ( I1635T ) has no apparent effect . Additionally , a GND clone containing a mutation in the NS5B RNA-dependent RNA polymerase GDD motif thus ablating HCV RNA replication was used as a negative control . A real-time quantitative RT-PCR assay was used to quantify the level of HCV RNA replication 6 d post-transfection of Huh-7 . 5 cells using relevant transcribed RNAs . As shown in Figure 1B , levels of viral RNA replication correlated identically with the transduction efficiencies observed between the different constructs shown in Figure 1A . NS3 and NS5A protein expression for all constructs , including the original HCV replicon BB7 epitope GAVQNEVTT , was similar when assayed by Western blot ( Figure 1C ) . Similar non-structural protein expression suggests that the NS31629–1637 mutations may affect initiation of replication , with a steady-state accumulation of protein expression occurring once replication has been established . It has been shown that replicons under drug-induced selective pressure , such as G418 , display high levels of HCV replication ( 1000–5000 positive strand RNA molecules ) , and therefore may show similar levels of protein expression [40] . However , under non-selective conditions , replicons having lower transduction efficiencies are lost more rapidly than those with high transduction efficiencies , and these differences may be reflected in viral protein levels . Although the P9 L1637S mutation resulted in partially reduced replicative capacity , the intermediate phenotype ( between L1637P and I1635T ) suggests that this mutation represents a balance between replicative fitness and CTL escape . This observation is interesting as the P9 L1637S mutation became a fixed quasispecies through 7 years of persistent replication in animal CH503 . It is also important to note that cells harboring HCV replicons were additionally sequenced to ensure fidelity of previously inserted mutations . Sequencing of at least six clones from each subgenomic-bearing Huh-7 . 5/B1701 cell line ( six , nine , ten , and six clones for NS31629–1637 , L1637P , L1637S , and I1635T , respectively ) showed no variation from expected amino acid sequences . This sequencing was carried out on PCR fragments spanning the inserted NS31629–1637 epitope from total cellular RNA . We next sought to determine the ability of parental or mutated NS31629–1637 epitope to be presented by a cell line expressing the appropriate chimpanzee MHC molecule . Patr-B1701 is a MHC class I molecule expressed in CH503 , and previous data demonstrated that mutations at critical anchor residues in the NS31629–1637 epitope ( including P9 ) hindered peptide binding to Patr-B1701 [15] . We first examined pan-class I surface expression on Huh-7 . 5 cells , and compared those levels to Huh-7 . 5 cells that had been transfected with a plasmid containing Patr-B1701 under zeocin selection ( Huh-7 . 5/B1701 , Figure 2A ) . It is important to note that we are not able to specifically stain for the Patr-B1701 molecule since specific antibodies to this protein do not currently exist . However , overall class I expression was similar in both cell types when compared to the isotype control , which led us to test the ability of Patr-B1701 expression to mediate wild-type NS31629–1637 epitope-directed killing by a B1701-restricted CTL clone , 4A , that had been previously isolated from CH503 11 weeks post-infection with the HCV-1/910 virus stock [15] . When EBV-transformed autologous B cells ( B1701T ) or Huh-7 . 5/B1701 cells were pulsed with exogenous wild-type NS31629–1637 peptide ( 1 µg/ml ) in a standard 51Cr-release assay , the NS31629–1637-specific CTL clone was able to lyse both antigen presenting target cell populations with similar efficiency ( Figure 2B ) . In contrast , peptide-pulsed Huh-7 . 5 cells lacking the Patr-B1701 molecule were not recognized by the CTL clone . In addition , we further determined the magnitude of the T cell interferon response to both the wild-type and mutated epitopes . To do so , we performed an intracellular IFNγ FACS analysis on the CTL clone cocultured with various APCs that had been pulsed with various peptide concentrations ( Figure 2C ) . Huh-7 . 5/B1701 cells that had been pulsed for 1 h with wild-type NS31629–1637 peptide elicited a robust IFNγ response from the CTL clone . This response was not elicited by the three mutant epitopes at concentrations up to 0 . 5 µg/ml . Responses to each epitope could be seen at very high concentrations indicating that the CD8+ T cell clone could be stimulated to produce IFNγ even with mutants that had previously been shown to have lowered MHC-binding capacity [15] if the mutant was present at high ( but not biologically significant ) concentrations . These data collectively show that Huh-7 . 5 cells stably transfected with the Patr-B1701 molecule efficiently present exogenous wild-type peptide in vitro , and that an antigen-specific T cell clone is able to respond by secreting IFNγ and exerting its cytotoxic effect . Conversely , this CTL clone is unable to efficiently respond when Huh-7 . 5/B1701 cells present mutated exogenous NS31629–1637 peptides reflective of in vivo viral species , containing a threonine substitution at P7 or a proline or serine substitution at P9 , at physiologically relevant concentrations . To determine whether Huh-7 . 5 cells expressing the Patr-B1701 expressed in CH503 could adequately process and present the NS31629–1637 epitope during active viral replication for T cell recognition , Huh-7 . 5/B1701 cells were stably transfected with various HCV replicons containing the appropriate mutations under neomycin selection as before ( see Figure 1A ) . Cells exhibiting zeocin resistance ( confirming Patr-B1701 expression ) and neomycin resistance ( confirming presence of replicons ) were used to determine protein expression as well as NS31629–1637-specific CTL lysis and IFNγ production . Huh-7 . 5/B1701 cells harboring HCV replicons showed relatively similar levels of protein expression post-transfection and G418 selection ( Figure 3A ) . Small differences in HCV protein expression were likely due to the stringency of dual selection ( zeocin and G418 ) placed on these cells to maintain both the Patr-B1701 plasmid and the subgenomic construct . Protein expression was monitored to ensure that the replicons containing the parental HCV 1/910 NS31629–1637 epitope and the mutants were able to produce , process and present similar levels of viral peptides to the CTL clone . Huh-7 . 5/B1701 cells harboring subgenomic HCV replicons were labeled with 51Cr , and cocultured with the NS31629–1637-specific CTL clone to determine the ability of these cells to elicit epitope-directed lysis . Cells replicating the wild-type NS31629–1637 replicon were lysed by the CTL clone , with ∼3-fold less efficiency than that seen using Huh-7 . 5/B1701 cells loaded with exogenous peptide ( Figure 3B ) , reflective of lower levels of physiologic peptide generated in the replicon system . Cells harboring mutated NS31629–1637 replicons elicited very low to undetectable levels of lysis , even at the highest effector to target ratios . Additionally , we assessed whether Huh-7 . 5/B1701 subgenomic cell lines replicating HCV RNA could elicit an IFNγ response from the NS31629–1637-specific CTL clone . Similar to the pulsing experiment using low amounts of exogenous peptide shown in Figure 2C , only Huh-7 . 5/B1701 harboring the wild-type subgenomic replicon stimulated the CTL clone to produce IFNγ ( Figure 3C ) . To test the replication fitness and infectivity costs associated with the mutations observed in the in vivo chimpanzee infection , we utilized the Huh-7 . 5/B1701 cell lines in both transfection and infection studies using full-length HCV constructs capable of producing infectious virus . The full-length genotype 2a JFH isolate has previously been shown to both replicate RNA and produce infectious virus in Huh-7 cells without acquiring adaptive mutations [33] , [40] . To study robust replication and virion production in vitro , a recombinant clone was created by exchanging the core to p7 region of the genotype 2a JFH virus with the genotype 2a J6 virus , and the resulting JFHxJ6 Cp7 ( Cp7 ) recombinant genome produces high titers of virus when used to transfect naïve Huh-7 . 5 cells ( Figure 4C ) . As in the BB7 replicon system , the corresponding NS31629–1637 epitope present in the JFH sequence of Cp7 was exchanged ( using single-site PCR mutagenesis ) with that of the parental HCV1/910 , as well as the respective mutations seen in CH503 at 3 , 10 , and 82 months post-infection ( Figure 5A ) . When RNA from parental NS31629–1637 epitope and mutant viruses was transfected into Huh-7 . 5/B1701 cells , no major difference in protein expression was seen after 4 d as compared to the Cp7 backbone recombinant construct ( Figure 5B ) . The GND transfected RNA expectedly did not produce any protein . These results are consistent with other experiments performed in both Huh-7 . 5/B1701 cells and Huh-7 . 5 cells in that these mutations do not affect overall expression of NS3 protein , but may affect initiation of replication ( Figure 1C , 3A , 4A , 5B ) . To determine the efficiency of viral epitope processing and presentation by cells replicating full-length infectious HCV , the NS31629–1637-specific CD8+ T cell clone was co-cultured with Huh-7 . 5/B1701 cells transfected with the Cp7 backbone , parental HCV1/910 NS31629–1637 , and individual mutant infectious clones . These T cells were then analyzed by flow cytometry to determine levels of IFNγ produced by the CD8+ T cell clone 4A . CD8+ T cells that had been stimulated by Huh-7 . 5/B1701 cells transfected with full-length infectious virus containing the wild-type NS31629–1637 epitope had a robust intracellular IFNγ response , while those containing infectious virus with mutant epitopes were unable to elicit a T cell response ( Figure 5C ) . To determine the level of NS31629–1637 epitope presentation during actual viral infection , supernatants from transfected Huh-7 . 5/B1701 cells were harvested after 4 d , passed through a 0 . 22 µm filter , and used to infect naïve Huh-7 . 5/B1701 cells for 5 d . These cells were then cocultured with the NS31629–1637-specific CD8+ T cell clone overnight , and examined via flow cytometry for IFNγ release . As previously shown with transfected cells , only Huh-7 . 5/B1701 cells infected with virus containing the wild-type NS31629–1637 epitope were able to stimulate a response from the CD8+ T cell clone ( Figure 5D ) . Having previously established that mutations in the P7 and P9 residues of HCV NS31629–1637 epitope ablate CD8+ T cell responses , we sought to determine the effect of each mutation on virion production . Cp7 backbone virus along with the parental HCV1/910 NS31629–1637 and mutant viruses were transfected into Huh-7 . 5 cells , and protein expression assessed via Western blotting and virion production assessed using a TCID50/ml reinfection assay up to 4 d post-tranfection . There was little difference in protein expression between mutant , HCV1/910 NS31629–1637 , and Cp7 backbone infectious viruses ( Figure 4A ) . At the level of RNA replication , as detected by a sensitive qRT-PCR Taqman assay , the viral variant L1637P replicated 1–1 . 5 logs less efficiently than parental virus ( Figure 4B ) . Interestingly , L1637S replicated with similar efficiency to parental virus , indicating that this variant was competent in replication . However , several differences in virion production were observed . The leucine to proline switch in P9 of NS31629–1637 epitope ( L1637P ) had a marked effect on the amount of virus secreted into the supernatant . This virus consistently produced 1 . 5–2 logs less virus than parental HCV1/910 NS31629–1637 and Cp7 backbone virus ( Figure 4C ) . The P7 isoleucine to threonine substitution ( I1635T ) produced similar levels of virus to the wild-type and Cp7 backbone , all secreting approximately 105 TCID50/ml . However , the leucine to serine P9 substitution ( L1637S ) displayed increased virion production compared to the L1637P mutation , producing approximately 104 TCID50/ml . The L1637S substitution of the parental HCV1/910 NS31629–1637 epitope was found fixed in CH503 from month 10 post-infection up to 82 months post-infection ( Figure 1A ) , and the recovered virion production phenotype seen in vitro suggests that L1637S is a more fit clone than L1637P , able to survive with intermediate fitness but efficient escape from immune elimination . Having established the relative fitness of each full-length viral clone , we wanted to determine what , if any , mutations arise in the NS31629–1637 epitope during prolonged in vitro viral infection and replication . Huh-7 . 5/B1701 cells were infected with parental HCV1/910 NS31629–1637 and mutant viruses , and cell lysates were used to obtain total cellular RNA up to 1 month post-infection . After first-strand cDNA synthesis and viral epitope-specific PCR , individual clones were sequenced . In the absence of CD8+ T cell selection pressure , we observed several amino acid mutations in the NS31629–1637 epitope 3 d post-infection . For the parental NS31629–1637 virus , one out of eight clones possessed a glutamic acid to glycine ( E1634G ) mutation at position 6 ( Figure 6 ) . This identical mutation was also found in the L1637S mutant virus 3 d post-infection . However , the E1634G mutation was absent in both the parental and L1637S viruses 23 d post-infection , indicating that this clone perhaps was not dominant or stable ( Figure 6 ) . Four out of eleven parental NS31629–1637 clones harbored an A1630D mutation 23 d post-infection , which was unusual given the absence of CD8+ T cell pressure and the fitness of this virus in both the replicon ( Figure 1A ) and cell culture ( Figure 4B , C ) models . The I1635T mutant virus was stable over the infection course , exhibiting no mutations on both 3 and 23 d post-infection . Similarly , the L1637S mutant virus remained fixed 23 d post-infection , demonstrating the stability of this viral variant in our in vitro cell culture model . The stability of the L1637S mutant virus in vitro mirrors that which was observed in vivo with a serine at P9 stable 7 years post infection . Interestingly , the L1637P mutant virus was unchanged 3 d post-infection , but two variants were found 23 d post-infection ( Figure 6 ) . One variant , V1631A , was present at low frequency ( 1/15 ) , while the other , P1637L , represented a significant fraction of the total population ( 5/15 clones ) . The P1637L mutation is particularly interesting because it represents reversion at position 9 in an unstable in vivo variant to the parental NS31629–1637 epitope sequence in the absence of in vitro CD8+ T cell selection pressure . These results demonstrate that in vitro mutation of a CD8+ T cell epitope in hepatitis C virus can occur , that particular amino acid substitutions are not maintained over the course of in vitro infection , and that reversion of less fit viral variants to parental HCV sequence can occur when CD8+ T cell pressure is absent . Variant I1635T was replication competent , escaped CTL recognition , and did not revert to the parental NS31629–1637 sequence in the absence of selective pressure . Because the I1635T variant possesses a mutation in a TCR-contact residue ( P7 ) and not an MHC-binding residue , we hypothesized that it may have stimulated a novel CD8+ T cell response in vivo , resulting in immune pressure preventing I1635T from becoming fixed in the viral population . To test this hypothesis , CD8+ T cells were isolated from frozen CH503 PBMC samples taken more than 7 years post-infection and stimulated with the I1635T peptide . Upon stimulation with autologous EBV-transformed B cells pulsed with I1635T peptide , CD8+ T cells secreted IFNγ ( Figure 7 ) . The I1635T antigen-specific CD8+ T cells did not respond to an irrelevant ( SIINFEKL ) control peptide , or the parental NS31629–1637 peptide ( Figure 7 ) . These results support the hypothesis that although the I1635T variant replicated efficiently and escaped from NS31629–1637-specific T cells , its persistence was hindered by a de novo T cell response .
Growing evidence has shown that CD8+ T cell epitope mutation and subsequent viral escape are associated with persistence of viruses like HIV , SIV , and HCV [17] , [18] , [26] , [41]–[47] . Indeed , the rate of non-synonymous mutation leading to amino acid substitutions is statistically higher in MHC class I restricted epitopes than in non-restricted epitopes or flanking regions , indicating that they are subject to Darwinian selection pressure by CD8+ T cells [18] , [26] , [45] , [48]–[53] . With the development of cell culture models that support HCV infection and replication , it is now possible to model how changes to the genome influence the rate of viral reproduction . In this study we have exploited these in vitro replication models to better understand how the potentially oppositional forces of immune evasion and efficient viral replication shaped evolution of a well-characterized dominant MHC class I epitope that displayed iterative adaptive mutations during establishment of HCV persistence in a chimpanzee . Mutational analyses in the currently available in vitro systems are limited by the necessity to study viral fitness and virion production in the context of genotype 1b and 2a backbones , respectively . However , even with the caveat that unpredictable coordinated effects of introducing epitopes from varying isolates may occur , the ability to study the consequences of single epitope serial sequence mutation on viral fitness and virion production is extremely valuable . Generation of Huh-7 . 5 cells harboring subgenomic replicons allowed for primary analysis of viral RNA replication , and helped establish the initial fitness characteristics of each NS3 mutation that had been observed in vivo ( Figure 1A and 1B ) . Even single amino acid changes in this epitope hindered transduction efficiency , with functional consequences of mutation on specific T cell responses . Interestingly , these changes did not seem to greatly affect protein expression , either in the replicon system or in the cell culture model ( Figures 1C , 3A , 4A , and 5B ) . Similar non-structural protein expression implies that the NS31629–1637 mutations may affect initiation of replication ( fewer G418 resistant colonies per µg of RNA ) , but that once replication has been established similar levels of steady-state replication/protein accumulation would be observed . Similar protein expression levels should result in similar levels of viral peptide production . The inability of P9 mutations to generate a T cell response could be overcome by high amounts of exogenous peptide but not by more physiologic concentrations generated by replicating subgenomic or full-length infectious viruses ( Figures 3 and 5 ) . By engineering the NS31629–1637 epitope mutations into both a subgenomic replicon system and a recombinant full-length clone of Cp7 capable of robust virion production , a correlation between viral fitness and immune escape was established . CD8+ T cell recognition in transfected and infected Huh-7 . 5/B1701 cells occurred only with HCV1/910 parental NS31629–1637 epitope ( Figures 3B , 3C , 5C , and 5D ) , indicating that single amino acid changes in this epitope abrogate T cell recognition . Each of the single substitutions at P9 decreased virion production while the virus containing the observed mutation at P7 ( I1635T ) was unimpaired in virus production . Together with the observation that mutant L1637S was maintained over 7 years despite I1635T having better viral fitness , these results suggest a balance between efficient immune escape and virion production attained by L1637S mutant virus . That is , since the mutant epitope I1635T , with a threonine at T cell receptor contact residue P7 , was detected at month 10 but not later in infection it is possible that its higher fitness and virion production allowed an additional T cell response to be generated against the new epitope . The I1635T mutation has been shown to bind well to Patr-B1701 [15] , so that generation of novel CD8+ T cell clones targeting the I1635T epitope in vivo is plausible . In contrast , mutant L1637S , which abrogates MHC binding [15] , may not select for a new T cell response to develop while still producing sufficient levels of virions . In fact in this study , using frozen PBMCs from CH503 from more than seven years after infection , we were able to isolate T cells specific for I1635T indicating that indeed , this otherwise “perfect” mutation was subject to new immune pressure in vivo ( Figure 7 ) . This de novo T cell response most likely prevented the I1635T variant from becoming stable in the population . Additionally , it is also possible that the P7 I1635T mutant isolated in vivo had fewer compensatory mutations in other highly targeted epitopes , allowing for recognition of the other epitopes by CD8+ T cells . Our data are consistent with a previous human study demonstrating that the variability of HCV sequences within immunological epitopes is limited by viral fitness [28] , but extend these observations by assessing the long-term longitudinal evolutionary pattern of an immunologically and virologically important NS3 epitope . It is noteworthy that the L1637P variant that appeared within 3 months of infection was least fit for replication in our cell culture models and was replaced in the plasma of the chimpanzee seven months later by two more fit variants . These results indicate that escaped viruses ( like L1637P ) may readily revert to a more fit sequence when transmitted from a recently infected donor to an HLA-mismatched recipient . In HIV-1 , a CD8+ T cell-mediated escape mutation in the dominant HLA-B57 TW10 epitope ( TSTLQEQIGW ) within the capsid protein p24 has been shown to impair viral replication in vitro [23] . Reversion of this mutation following transmission to an HLA-mismatched host provided evidence for the impaired fitness cost that was incurred in vivo while escaping from CTL pressure [54] . Another study utilizing a clonal SIV virus ( SIVmac239 ) harboring CTL escape mutations showed that escape can exact a severe replicative fitness cost , and that many of these variant sequences would be unlikely to propagate in HLA-diverse populations [51] . To date there are only two published examples of apparent reversion of escaped HCV epitopes in human subjects , and both involved viruses transmitted from donors during the acute phase of infection [18] , [46] . Ray et al . followed a group of women infected with a common virus from a single acutely infected donor . When HCV genomes from the recipients were compared with a consensus HCV genome assembled from published sequences , mutations trending away from consensus were observed in HLA-restricted epitopes ( representing possible emergence of recipient escape variants ) and toward consensus in non-restricted epitopes ( representing possible reversion of donor escape variants ) [46] . That acute phase escape variants might revert to a more fit sequence is also supported by a second detailed study of CD8+ T cell immunity in a donor-recipient pair [18] . A CD8+ T cell escape mutation that arose during the acute phase of infection in the virus donor was quickly lost from the quasispecies upon transmission to an HLA class I disparate recipient [18] . Our results suggest that this reversion may be less common when mutations are optimized for immune escape and replication over long periods of chronic infection . We hypothesize that variants like L1637S that have been fine tuned by a process of iterative mutation during months of persistent replication might be considerably more stable upon transmission . We predict that these amino acid substitutions will not readily revert upon infection of a new host once escape from immunity has been carefully balanced against replicative fitness , particularly if HCV has a wide ( though not limitless ) tolerance for substitutions that alter replication . In vitro , we infected naïve Huh-7 . 5/B1701 cells with parental NS31629–1637 and mutant viruses , and studied the epitope evolution of individual clones . Interestingly , we found mutations in numerous ( 5/15 ) clones of L1637P 23 d post-infection , with the P9 proline mutating back to the parental leucine ( Figure 6 ) . These results strengthen the hypothesis that the L1637P mutant virus has diminished replicative fitness in vitro as well as in vivo . Additionally , the absence of CD8+ T cell pressure in these experiments suggests that transiently less fit viruses may trend towards input parental sequence in HLA-diverse populations or upon transmission to HLA-mismatched hosts . Importantly , the L1637S and I1635T viruses were relatively stable , confirming replication and virion production data ( Figures 1A , 1B , 4B , and 4C ) . The stability of L1637S suggests that this virus has indeed struck a balance between replicative fitness and immune pressure , and would not likely revert back to the parental sequence upon transmission to a new host . These data correlate with long term NS31629–1637 epitope evolution in chimpanzees with L1637S stable over a 7-year period , and demonstrate that mutants arising in vivo can be recapitulated in vitro . We predict that the L1637S sequence represents a viral variant that has achieved balance between replicative capacity and immune evasion and would be stable upon transfer to naïve hosts regardless of whether they express MHC molecules required for presentation to CD8+ T cells . The work reported here highlights the competing forces influencing the interplay between the virus and the immune system and the multiple varied effects of a single amino acid change on T cell function and virus production . These observations elucidate potential mechanisms by which viral persistence is established . Consequences of stable integration of escape mutations into viral genomes are not clear , but it is formally possible that epitopes presented by the most prevalent MHC class I molecules in human populations will eventually be lost or become less dominant , an outcome that could have implications for vaccine development . In light of the knowledge that HCV mutates nearly one nucleotide per replication cycle , this work provides sobering evidence that the anti-HCV CD8+ T cell response faces daunting challenges for efficient and lasting control of HCV . | Hepatitis C virus ( HCV ) -associated liver disease is a leading indication for liver transplantation in the United States . With more than 170 million people infected with HCV worldwide and more than 70% of those infected unable to clear the virus , it is of paramount importance to elucidate the factors leading to viral persistence . A well-characterized experimental chimpanzee chronically infected with HCV was found to develop multiple viral sequence variations over the course of 7 years . Serial mutations in an HCV epitope that was originally immunogenic in the host were observed during the course of infection , and we sought to better understand how each mutation affected viral persistence . We recreated the viral variants detected in the chimpanzee and assessed the ability of each variant to replicate , produce progeny virus , and evade the host immune system . We found that certain HCV variants present long after initial infection are able to avoid recognition by host immune cells , but have reduced replication fitness . Importantly , in the absence of immune pressure , these viruses mutate back to variants better able to replicate and produce new viral progeny . Our experiments suggest that rapidly mutating viruses like HCV seek a balance between replication efficiency and the ability to evade the host immune system . | [
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] | 2008 | Stable Cytotoxic T Cell Escape Mutation in Hepatitis C Virus Is Linked to Maintenance of Viral Fitness |
Wolbachia are ubiquitous inherited endosymbionts of invertebrates that invade host populations by modifying host reproductive systems . However , some strains lack the ability to impose reproductive modification and yet are still capable of successfully invading host populations . To explain this paradox , theory predicts that such strains should provide a fitness benefit , but to date none has been detected . Recently completed genome sequences of different Wolbachia strains show that these bacteria may have the genetic machinery to influence iron utilization of hosts . Here we show that Wolbachia infection can confer a positive fecundity benefit for Drosophila melanogaster reared on iron-restricted or -overloaded diets . Furthermore , iron levels measured from field-collected flies indicated that nutritional conditions in the field were overall comparable to those of flies reared in the laboratory on restricted diets . These data suggest that Wolbachia may play a previously unrecognized role as nutritional mutualists in insects .
Wolbachia pipientis is arguably the most abundant endosymbiont in the insect world [1]–[4] . It is strictly maternally inherited and has evolved a number of mechanisms , broadly classed as reproductive parasitism , to facilitate its invasion into host populations . The most common of these traits is termed cytoplasmic incompatibility ( CI ) , which is a form of early embryonic developmental arrest seen in the offspring of uninfected female insects that have been mated to an infected male . CI can also be seen in cases where Wolbachia infected females have mated to males carrying an unrelated Wolbachia strain [5] , [6] . CI results in Wolbachia infected females possessing a reproductive advantage over uninfected females and as a result Wolbachia is able to invade host populations [7] , [8] . CI is considered to be the major driving force behind Wolbachia invasion . This paradigm , however , is based primarily on experimental work with a limited number of Wolbachia strains that induce strong CI in Drosophila simulans [7]–[9] . A number of Wolbachia strains , including all of those recovered from D . melanogaster , induce very weak and variable CI especially under field conditions [10]–[12] . Theoretical work indicates that such weak CI is unlikely to be sufficient to drive a Wolbachia invasion in the field . Moreover , strains of Wolbachia have been indentified in D . simulans that induce no CI at all , yet these strains have managed to invade host populations [12] , [13] . Alternatively , these strains may represent defective CI inducing strains that have lost the ability to induce CI and represent a snapshot of a Wolbachia infection that in time will be lost [14] . In the absence of strong CI induction the most obvious explanation for the ability of these strains to invade would be that they do not act as reproductive parasites at all , but possibly serve some mutualistic function for the insect . Wolbachia infections in Drosophila melanogaster have been observed to act positively upon non-reproductive fitness traits , such as the extension of adult lifespan or protection against viral and fungal pathogens [15]–[17] . Yet despite considerable efforts to find a positive fecundity benefit for Wolbachia infected Drosophila melanogaster , none has been identified [18] . Wolbachia also infect filarial nematodes , where the bacterium is an obligate mutualist required for successful reproduction and development of the worm [19] , [20] . Analysis of the full genome sequence of the worm , Brugia malayi revealed that it lacked a complete biosynthetic pathway for both heme and riboflavin [21] . In contrast , the much reduced genome of its infecting Wolbachia strain , wBm contained a complete suite of genes for both pathways [22] . Genes that encode components of the heme biosynthetic pathway were subsequently shown to be under diversifying selection in wBm , providing further support for the hypothesis that this pathway may be a key point of interaction in the association and offering a potential explanation for the basis for the obligate mutualism [22] . Positive selection was also identified on genes in the same pathways in the genome of the Wolbachia strain , wMel that infects D . melanogaster [23] , raising the possibility that the bacterium may play a role in metabolic provisioning in insects as well as nematodes [24] . Although insect hosts are not dependent upon Wolbachia for heme biosynthesis , the microbe could supplement host stores or play a role in iron homeostasis . Iron is an essential micronutrient required for a diverse range of metabolic processes [25]–[27] including maturation and development of the insect egg [28] . Iron also varies in the environment [26] and hence is likely to be variable in the diet of wild insects . Here we examine how the presence of Wolbachia infection alters fitness of the model insect host , Drosophila melanogster when reared under varying levels of dietary iron to test the hypothesis that Wolbachia may function as a nutritional mutualist as well as a reproductive parasite in insects .
The total amount of iron within D . melanogaster was responsive to our dietary manipulations as measured by mass-spectrophotometry . Flies reared on high iron diets contained approximately twice as much total iron as those reared on cornmeal diet ( Figure 1 ) . Flies reared on either tea or BPS diets had approximately half the amount of total iron compared to those reared on cornmeal diet . The presence of Wolbachia did not influence the total iron content of adult D . melanogaster as both infected and uninfected fly lines were estimated to have similar total iron contents ( data not shown ) . The total content of eight other biologically relevant metals ( see Materials and Methods ) did not change in response to the altered diets; the only metal that was responsive to the modified diets was iron . The total iron content of field adult female flies collected from four locations in and around Brisbane , Australia , was also determined . The total iron content of adult female flies from three of the four collection sites were similar to that determined for flies reared on low iron diets ( Taringa , Brisbane; St Lucia , Brisbane; and Byron Bay; Figure 1 ) . At one location ( Chapel Hill , Brisbane ) , the total iron content was higher than other locations , and was similar to that observed for flies reared on standard cornmeal fly diets . Not surprisingly total iron content of wild flies was different at each location , but in general iron content of flies taken from the field was lower ( t test; p<0 . 0001 ) than that of lab reared flies on cornmeal diet and more similar to lab flies reared on restricted diet . To investigate if Wolbachia could provide a fecundity benefit to D . melanogaster in a low iron environment , two fly lines both derived from the same genetic background were used [11] . The first line was infected with wMel , the second was uninfected due to the prior application of antibiotics . Wolbachia had no effect on the fecundity of D . melanogaster when reared on cornmeal fly diets . ( Controls 1–4; Figure 2 ) . In contrast , when D . melanogaster females were reared on low iron diets due to the addition of black tea , Wolbachia conferred a fecundity advantage in three of four independent experiments . In one experiment ( Figure 2 , Expt 3 ) no significant difference in fecundity was observed . Where a fecundity advantage was observed , Wolbachia infected D . melanogaster females laid on average 20% more eggs than uninfected females ( Expt 2 and 4; Figure 2 ) , and for one experiment a 50% advantage was observed ( Expt 1; Figure 2 ) . In no experiments was there a fecundity reduction in the presence of Wolbachia . In addition to using black Tea as an iron chelating agent bathophenanthroline disulfonate ( BPS ) was used to specifically chelate iron ( II ) [29] in two independent experiments . Again variable results were obtained with a 20% fecundity advantage seen in one experiment and no difference seen in a subsequent experiment ( Figure 3 ) . To ensure the Tea or BPS diets were efficiently chelating iron we measured the total iron content of flies that showed no fecundity advantage and compared these to flies that did . We observed no statistical difference among these treated flies ( t test; p>0 . 9999 ) and conclude the diets used successfully reduced the total iron of D . melanogaster flies . When D . melanogaster were reared on diets that contained high levels of iron , due to the addition of FeCl3 we observed a significant reduction in fecundity for both infected and uninfected fly lines relative to flies reared on cornmeal diets . However , the presence of Wolbachia in flies on high iron diets resulted in significant gains in fecundity in two independent experiments ( p<0 . 05 and p<0 . 001 Mann-Whitney U; Figure 4 ) . As the FeCl3 diet contains both higher concentrations of iron as well as chloride , we reared flies on NaCl diets to determine if the Wolbachia associated effects were due to the addition of iron and not chloride . We observed no fecundity difference between infected and uninfected D . melanogaster females when reared on a diet rich in chloride ( data not shown ) . Therefore we conclude that the fitness benefits conferred by Wolbachia were in response to the high iron content . Assessments of Wolbachia effects on male fertility in response to changes in dietary iron were performed using Wolbachia infected and uninfected BNE lines reared on Tea , BPS and FeCl3 diets . In no experiment did we observe a cost or benefit to male fertility associated with Wolbachia infections ( data not shown ) , and conclude that Wolbachia only benefits female fecundity and not male fertility during periods of iron deficiency or overload .
Metabolic provisioning of hosts by endosymbionts is commonly observed in obligate associations [30] . Wolbachia strains that infect filarial nematodes are one such example , and are thought to provide their host with essential vitamins , nucleotides and cofactors , including heme [22] . These same biosynthetic pathways exist in insect Wolbachia , and evolutionary analyses have previously identified signatures of positive selection on pathway genes [23] . Given the multiple predictions of heme and iron as potential interaction points for Wolbachia and their hosts , we experimentally determined if Wolbachia could influence iron homeostasis and female fecundity in an insect host . Wolbachia had no effect on Drosophila melanogaster fecundity when reared on cornmeal diets , consistent with previous observations [31] . In contrast , Wolbachia did provide a significant fecundity benefit to female Drosophila when subjected to low or high iron environments in the majority of experiments conducted . This is the first report of a Wolbachia conferred compensatory effect during periods of nutritional stress or deficiency to an insect host . The observed variability in fecundity measures is consistent with previous experiments in D . melanogaster , which have shown that laboratory measurements of fecundity are highly sensitive to local assay conditions , and are notoriously difficult to replicate even under controlled laboratory conditions [32]–[34] . Given the observed Wolbachia fecundity advantage in perturbed iron environments and the observed low iron content of flies from the wild , it is likely that the results of the laboratory experiments reported here may have ecological relevance , providing a variable but positive fitness benefit to Wolbachia infected flies across a range of environments . Previous studies have shown that if Wolbachia can simultaneously induce cytoplasmic incompatibility and increase female fecundity , the rate at which Wolbachia invades naïve host populations is increased [35] . Benefits observed under high iron conditions , while not ecologically relevant based on the estimates of total iron in field caught flies , are interesting mechanistically . Increases in dietary iron result in an increase in oxidative stress for most insects [36] , and in our experiments severely reduced the fecundity of both infected and uninfected females . The fecundity cost incurred by infected females was , however , reduced relative to uninfected females suggesting that Wolbachia might provide protection against oxidative stress .
The Drosophila melanogaster strain BNE was derived from field caught female flies from Brisbane , Australia , and is described in detail elsewhere [11] . In brief , field caught flies were treated with tetracycline to remove endosymbiont bacteria and a wMel infection introgressed by crossing to yw67c23 females . Subsequent offspring were backcrossed with males derived from the original field collection for a minimum of five generations to re-establish the original BNE genetic background . All flies were maintained at ∼25°C on a 12/12hr light/dark schedule throughout the study . Tetracycline treatments were performed as described previously [37] to generate a genetically identical fly line that lacked the Wolbachia infection . To reconstitute gut flora , stock bottles containing cornmeal fly diet were seeded with non-tetracycline treated males for a period of three days . These males were then excluded from the diet and newly emerged tetracycline treated adult flies allowed to mate and lay eggs on the diet . Assessments of fecundity were performed at least three generations post tetracycline treatment and reconstitution of gut flora to minimise maternal or grandmaternal mitochondrial effects [38] , [39] . To minimize genetic drift between these fly lines , approximately every 10 generations reciprocal crosses ( BNE-wMel Female × BNE Tet male; BNE Tet female × BNE-wMel Male ) were performed using one-week-old adults . Fly lines were reared on four types of diets . Cornmeal fly diet was made from yellow corn meal medium ( Sigma ) . Two low iron diets were made by either substituting the water that was used to make up the medium with an aqueous extract of black tea ( Camellia sinensis; 3 Tetley tea bags infused in 1litre of water for 5 minutes [40] ) or through the addition of 20 μM bathophenanthroline disulfonate ( BPS; Sigma ) to melted cornmeal fly diet at 65°C . In both instances the amount of available iron to the developing Drosophila larvae was reduced by iron chelating agents [41] , [42] . A high iron diet was made by the addition of a FeCl3 solution to the cornmeal fly diet to a final concentration of 10mM [42] . In a single experiment ( Female fecundity: n = 30 Wolbachia-BNE and n = 28 Tet-BNE individuals; Male fertility: n = 20 Wolbachia-BNE and n = 21 Tet-BNE individuals ) cornmeal fly diets supplemented with 30mM NaCl were generated as a control to test if the addition of chloride ions from FeCl3 could influence fecundity . Females were allowed to lay eggs onto molasses/agar plates in the absence of yeast . First instar D . melanogaster larvae were introduced to vials containing modified or cornmeal fly diets at low densities ( 50–80 larvae ) and reared to adulthood at 25°C . Virgin males and females were collected and maintained separately on the same diet for a period of three days . Individual crosses among males and females of identical infection status were allowed to mate once within a 60-minute window . Once mating was complete , males were discarded and mated females allowed to oviposit onto molasses plates seeded with yeast for a period of three days . A new plate was introduced every 24 hours and the total number of eggs laid was scored . To determine the impact of Wolbachia infection on female fecundity under iron limitation or overload , females reared on modified diets were mated with males reared on cornmeal diet . The reciprocal cross permitted assessment of male fertility . Once the total number of eggs laid over the three-day period had been scored , comparisons of fecundity between Wolbachia infected or uninfected Drosophila were made using parametric ( ANOVA ) or non-parametric ( Mann-U Whitney ) tests where appropriate . The total content of biologically relevant metals ( manganese , iron , cobalt , nickel , copper , zinc , Cadmium , lead and arsenic ) present in flies reared on each of the food types or collected from the field , were determined using inductive coupled plasma mass-spectrometry ( ICP-MS ) at the Advanced Centre for Isotope Research Excellence at the University of Queensland . The only metal responsive to diet was iron . Pools of 10 female flies were used for each analysis and replicated ten times for lab reared flies or four times for field caught flies . Flies were caught using modified banana traps , such that flies were attracted to the bait but excluded from feeding upon it to ensure that total iron levels were not affected . | Wolbachia are bacteria that infect millions of insect species worldwide . Wolbachia aren't infectious , but are maternally inherited symbionts passed from mother to offspring . To infect a host population , Wolbachia behave as reproductive parasites and alter the host reproductive system in a manner that increases infected female reproductive success . Some strains of Wolbachia , however , cannot manipulate their host's reproductive systems—yet they can successfully infect insect populations . How is this possible ? Here we show that a Wolbachia strain that naturally infects Drosophila melanogaster , and induces very low levels of reproductive parasitism , can also act as a nutritional mutualist . When D . melanogaster flies were reared on normal diets , we observed no cost or benefit associated with the Wolbachia infection . But , if we reared flies on diets containing either very low or high amounts of iron , Wolbachia-infected flies produced more eggs than uninfected flies . As wild-caught flies contain low amounts of iron , our results suggest that flies in the wild should benefit from their Wolbachia symbiont . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
] | [
"ecology/environmental",
"microbiology",
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] | 2009 | Evidence for Metabolic Provisioning by a Common Invertebrate Endosymbiont, Wolbachia pipientis, during Periods of Nutritional Stress |
We carried out genome-wide association ( GWA ) studies in inbred mouse strains characterized for their lung tumor susceptibility phenotypes ( spontaneous or urethane-induced ) with panels of 12 , 959 ( 13K ) or 138 , 793 ( 140K ) single-nucleotide polymorphisms ( SNPs ) . Above the statistical thresholds , we detected only SNP rs3681853 on Chromosome 5 , two SNPs in the pulmonary adenoma susceptibility 1 ( Pas1 ) locus , and SNP rs4174648 on Chromosome 16 for spontaneous tumor incidence , urethane-induced tumor incidence , and urethane-induced tumor multiplicity , respectively , with the 13K SNP panel , but only the Pas1 locus with the 140K SNP panel . Haplotype analysis carried out in the latter panel detected four additional loci . Loci reported in previous GWA studies failed to replicate . Genome-wide genetic linkage analysis in urethane-treated ( BALB/c×C3H/He ) F2 , ( BALB/c×SWR/J ) F2 , and ( A/J×C3H/He ) F2 mice showed that Pas1 , but none of the other loci detected previously or herein by GWA , had a significant effect . The Lasc1 gene , identified by GWA as a functional element ( Nat . Genet . , 38:888–95 , 2006 ) , showed no genetic effects in the two independent intercross mouse populations containing both alleles , nor was it expressed in mouse normal lung or lung tumors . Our results indicate that GWA studies in mouse inbred strains can suffer a high rate of false-positive results and that such an approach should be used in conjunction with classical linkage mapping in genetic crosses .
Association studies are based on linkage disequilibrium ( LD ) between genetic markers and a disease locus affecting a particular phenotype [1] , [2] . Such studies may allow the fine mapping of loci affecting monogenic diseases , as well as loci affecting complex diseases if the relevant alleles are common in the general population under investigation . This approach can be quite powerful when disease chromosomes are descended from a single founder mutation and the markers analyzed are tightly linked to the disease locus . In these cases , the LD approach has proven successful not only in humans for the fine mapping of rare diseases in isolated populations , but also in experimental animals . Indeed , our LD analysis in mouse inbred strains served in refining the mapping region of Pas1 , the major locus affecting susceptibility to mouse lung tumorigenesis [3] . Subsequently , we found that the Pas1 locus consists of a conserved haplotype spanning ∼400 kb and including 6 genes , whose polymorphisms define a susceptibility allele that is frequent in laboratory mouse inbred strains and apparently derived from an ancestral progenitor [4] . More recently , we used LD analysis to show that the Pas1 locus affects not only carcinogen-induced lung tumor multiplicity , but also spontaneous lung tumor incidence [5] . The availability of single nucleotide polymorphism ( SNP ) data for many inbred strains has led to the proposal of in silico genome-wide mapping of mouse quantitative trait loci ( QTLs ) [6] . In three genome-wide association ( GWA ) studies aimed at detecting lung tumor modifier loci , 5 loci ( named SLT loci ) affecting incidence of spontaneous lung tumors [7] , 4 loci ( named Clas loci ) affecting incidence of urethane-induced lung tumors [8] , and 5 loci affecting incidence of N-nitroso-N-ethylurea-induced lung adenomas or adenocarcinomas [9] were identified . Each of these three reports detected a unique , non-overlapping set of loci , despite analysis of similar populations of mouse strains and highly correlated tumor phenotypes , i . e . , spontaneous , urethane- , or N-nitroso-N-ethylurea-induced lung tumor incidences [5] . The identification of authentic lung tumor modifier loci in the population of mouse inbred strains is fundamental to revealing the genetic elements that control tumor susceptibility in this mammalian model . To assess the reproducibility and functional relevance of GWA results , we carried out this analysis using a larger number of mouse strains and more phenotypes describing the lung tumor susceptibility trait than in previous studies and compared the results with those obtained from standard genetic linkage analyses in intercross populations .
Strain susceptibility to lung tumorigenesis can be described using different phenotypes , such as tumor incidence , tumor multiplicity , and tumor volume . Mouse inbred strains show a wide range of lung tumor phenotypes , with mean spontaneous tumor incidence ranging from 0 to 82% , mean urethane-induced lung tumor incidence from 0 to 100% , and mean urethane-induced lung tumor multiplicity ranging from 0 to 28 . 3 tumors/mouse ( Table 1 ) . A highly significant correlation was found between the spontaneous tumor incidence and urethane-induced tumor multiplicity phenotypes ( r = 0 . 94 , −log P = 8 . 9 ) , whereas correlation between the spontaneous tumor incidence and urethane-induced tumor incidence phenotypes was weaker ( r = 0 . 76 , −log P = 3 . 8 ) ( Figure 1 ) . GWA using the WTCHG 13K SNP panel was carried out in 20 to 27 strains for which phenotype-genotype data were available ( Table 1 ) . For any of the lung tumor phenotypes , the Bonferroni's statistical threshold ( α = 0 . 1 significance level ) accounting for the number of statistical tests ( i . e . , 12 , 959 ) would result in a −log P value = 5 . 1 . GWA analyses revealed that only rs3681853 on Chromosome 5 reached this statistical threshold for spontaneous tumor incidence , whereas no SNPs were associated with urethane-induced tumor incidence and only one SNP ( rs4174648 ) on Chromosome 16 was associated with urethane-induced tumor multiplicity . Surprisingly , the known Pas1 locus was not detected . Using the suggestive thresholds obtained by permutation tests ( α = 0 . 10 , Table 2 ) , which also account for the correlation structure of the data , we detected only SNP rs3681853 on Chromosome 5 , ( −log P = 5 . 3 , spontaneous incidence , SLT6 ) , SNPs rs13459098 and rs13479086 in the Pas1 region ( both SNPs at −log P = 4 . 7 , urethane-induced tumor incidence , Clas1 ) and SNP rs4174648 on Chromosome 16 ( −log P = 6 . 5 , urethane-induced tumor multiplicity , Clas5 ) ( Table 3 ) . When Mus spretus was excluded from the analysis , rs3681853 did not reach any statistical thresholds and the other results remained the same . Due to the low sensitivity of detection of putative loci based on statistical thresholds , as demonstrated by the significant association of the known Pas1 locus with only one of three lung tumor phenotypes , we also examined putative loci whose associations were below the statistical thresholds . For example , at −log P≥4 , 10 and 11 SNPs showed significant association with spontaneous incidence ( SLT loci ) and urethane-induced multiplicity ( Clas loci ) , respectively , of lung tumors . No other SNPs above −log P = 4 , except the two Pas1-associated SNPs at −log P = 4 . 7 , were detected for the phenotype “incidence of urethane-induced lung tumors . ” By attributing a locus definition to chromosomal regions spanning less than 1 Mb in length and containing one or more SNPs associated with lung tumor phenotypes , these associations identified 8 new SLT loci that included Pas1 , the previous Clas1 ( Pas1 ) , and 5 new Clas loci ( not shown ) . We then replicated the GWA using the BROAD SNP panel , which provided a higher SNP density ( 140K ) but a lower number of strains ( i . e . , 20 to 23 ) ( Table 1 ) . To reduce the risk of false-positives due to the inclusion of genetically distant strains , we excluded the Mus spretus strain ( SPRET/EiJ ) . Above the suggestive thresholds ( α = 0 . 10 , Table 2 ) , we detected only SNPs rs30118733 and rs30752783 ( −log P = 7 . 5 and 6 . 9 , respectively , urethane-induced incidence ) , both of which were located in the Pas1 region . SLT1 to SLT6 and Clas2 to Clas5 were not confirmed by analysis using the BROAD SNP panel , although SLT6 and Clas5 were detected by the WTCHG SNP panel ( Table 3 ) . Finally , our haplotype-based GWA analysis using the BROAD dataset and a three-SNP sliding window revealed haplotype-associated lung tumor ( Halt ) loci ( Table 4 , Figure 2 ) . For spontaneous lung tumor incidence , no haplotype reached statistical threshold ( α = 0 . 10 , Table 2 ) , whereas for urethane-induced lung tumor incidence , two associated haplotypes were detected: Halt1 in the Pas1 region and Halt2 on Chromosome 14 ( Table 4 ) . Associated to the urethane-induced tumor multiplicity phenotype , we detected five statistically significant haplotypes ( Halt3- Halt7 ) , two of which mapped in the Pas1 locus ( Halt5 ) or in its flanking region ( Halt6 ) ( Table 4 ) . A previous GWA study in 13 inbred strains detected 5 loci , named SLT1 to SLT5 , associated with spontaneous incidence of lung tumors [7] . Our analysis in 27 strains contained 12 , 4 , 10 , 11 , and 19 SNPs in the SLT1 to SLT5 regions , respectively . However , none of the SLT loci were confirmed in our GWA analysis ( Table 3 ) . To rule out the possibility that the non-replication of previous results [7] was due to the lack of inclusion of relevant SNPs in the SNP database used , we identified and selected SNPs in the same SLT regions showing exactly the same 13 strain distribution pattern reported [7] and genotyped the selected SNPs in the 27 strains of our study ( Table 1 ) plus the O20/A strain [5] . However , none of the SNPs located in SLT1 ( rs13478866 ) , SLT2 ( rs13479117 ) , SLT3 ( Galnt2 JC10664_20 , Agt JC10667_5 , Agt JC10669_3 ) , SLT4 ( rs13483600 ) , and SLT5 ( rs3667513 ) confirmed an association with spontaneous lung tumor incidence ( not shown ) and none of the SLT loci showed an association with urethane-induced lung tumor incidence or multiplicity ( Table 3 ) . We also tested whether previous GWA results on lung tumor multiplicity [8] might be confirmed in our study . Unlike the spontaneous incidence data , the sizes of the two datasets on lung tumor multiplicity are almost identical ( n = 21–22 ) [8] , with very similar strain composition ( Table 1 and [8] ) . Differences included two BALB/c substrains and the O20/A strain analyzed in [8] , whereas we analyzed only one BALB/c substrain and did not include the O20/A strain but did include the C58/J and the NZB/BlGd strains not analyzed in [8] . Overall , we expected to observe essentially overlapping results . Using the WTCHG 13K SNP panel , we confirmed the association at the Pas1 locus , with 2 SNPs showing P values above the statistical threshold for the tumor incidence phenotype ( Table 3 ) . At the Clas2 locus on Chromosome 4 , ( CEL-4_30653207 , alias rs27801920 ) , [8] , we found a −log P = 2 . 8 . Genotyping at this locus in the strains of the genome-wide scan plus the NGP/N and O20/A strains for the functional Lasc1 SNP D102E ( rs32396036 ) revealed no significant associations with lung tumor phenotypes ( −log P = 1 . 8 to 2 . 4 ) . Furthermore , we detected no significant associations at the Clas3 and Clas4 regions ( Table 3 ) , despite the inclusion of 6 and 9 SNPs in the Clas3 and Clas4 regions , respectively . Even the higher SNP density offered by the BROAD SNP panel failed to reveal any Clas loci except Pas1 ( Clas1 ) . Another recent GWA study conducted in 20 inbred strains treated with N-nitroso-N-ethylurea and scanned with the WTCHG SNP database detected several putative lung tumor susceptibility loci on Chromosomes 3 , 6 , 9 , and 15 [9]; these loci did not correspond to Clas loci or to regions detected in the present study . Authors of [9] did not detect the Pas1 locus , which has been implicated in lung tumorigenesis independently of the type of chemical carcinogen [5] . In contrast with the GWA results , the Pas1 locus but none of the GWA loci was detected in a genetic linkage study that used the same carcinogen as in the GWA study , i . e . , N-nitroso-N-ethylurea [10] . We observed no association with any lung tumor phenotype at any of the loci reported in [9] , using either the WTCHG or the BROAD SNP panel . Genetic linkage analysis of mouse crosses represents a formal approach to demonstrating functional activity of genetic loci on given phenotypes . In a single cross , not all loci affecting a specific complex phenotype in a given species are expected to be detected , since a locus can exert allele-specific effects only in crosses originating from two strains carrying different alleles and cannot be detected if the functional element ( s ) is non-polymorphic in the two parental strains . Accordingly , the Pas1 locus is easily detected in crosses between strains carrying either of the two Pas1 alleles ( or haplotypes ) but is not detectable in crosses between strains carrying the same haplotype [11] , [12] . To test whether loci detected by genome-wide strain survey may be involved in modulating lung tumorigenesis , we carried out genome-wide genetic linkage analyses of three intercross populations previously analyzed for urethane-induced lung tumor multiplicity [11]–[13] . In our population of ( BALB/c×C3H/He ) F2 mice , genotyping by SNP array detected a total of 383 non-redundant informative SNPs widely dispersed over the whole mouse genome . There was complete coverage of all chromosomes , with a range of 12–43 non-redundant SNPs genotyped for each chromosome , except for Chromosome 9 which contained only 4 SNPs . Above the R/qtl threshold , only the effect of the Pas1 locus was observed , with LOD scores of 18 . 4 ( Figure 3A , red line ) . By conditioning on the Pas1 genotype , no additional QTLs were detected ( Figure 3A , black line ) . In ( A/J×C3H/He ) F2 mice , 192 markers ensured genome-wide coverage , and composite interval mapping scan detected the known Pas1 locus and no other locus ( Figure 3B , red line ) , even by a separate conditioning for the Pas1 genotype ( Figure 3B , black line ) . Analysis of the ( BALB/c×SWR/J ) F2 population by composite interval mapping scan confirmed the reported Chromosomes 4 ( Papg1 ) , 6 ( Par4 ) , and 18 ( Par2 ) loci and detected an additional locus ( LOD score = 5 . 3 ) on Chromosome 1 between D1Mit18 and D1Mit22 markers ( not shown ) . Thus , none of the loci except Pas1 identified by previous or the present ( Table 3 ) genome-wide strain survey were confirmed by genetic linkage analysis ( Figure 3 ) , although either the same or flanking SNPs detected by GWA at SLT1 to SLT6 loci and at Clas1 ( Pas1 ) to Clas5 loci were , in fact , polymorphic in at least one of our three intercross populations . Note that the Clas2 locus showed no effect in the ( BALB/c×C3H/He ) F2 cross , as indicated by the absence of any significant linkage of the whole Chromosome 4 ( covered by 19 SNPs ) to any lung tumor phenotype ( Figure 3 and not shown ) . Moreover , since the claimed functional D102E polymorphism of the Lasc1 gene defining the Clas2 locus [8] was present in the ( BALB/c×C3H/He ) F2 cross , we genotyped that polymorphism; no significant linkage with lung tumor multiplicity was found at α = 0 . 10 significance level . Further testing of the D102E polymorphism by genotyping in the ( A/J×C3H/He ) F2 cross ( total of 163 mice ) [13] , which also carries the polymorphism , again revealed no significant associations with either lung tumor multiplicity or volume ( Figure 3B ) . With regard to the loci detected by haplotype analysis , none but those linked with the Pas1 locus were confirmed by genetic linkage analysis . Except for the Halt2 locus on Chromosome 14 , which could not be detected since all four parental strains of our genetic crosses carried the same haplotype , all other Halt loci could be detected in at least one of the intercrosses that displayed informative haplotypes ( Table 4 ) . In the ( BALB/c×SWR/J ) F2 intercross , a significant linkage was found in the pulmonary adenoma resistance 2 ( Par2 ) locus [12] , with peak LOD score = 15 . 5 at D18Mit33 , located at about 10 Mb distal from the Halt7 locus; no linkage near the Halt7 locus was found in the two other informative crosses ( Table 4 ) . Notwithstanding the lack of significant linkage of the Lasc1 gene in two independent crosses , we examined Lasc1 mRNA expression in mouse normal lung and lung tumors . RT-PCR analysis of normal lung tissue from A/J and C57BL/6J mice , which carry different alleles of this gene , and of normal lung and tumor tissue from ( A/J×C57BL/6J ) F1 mice revealed no Lasc1-specific transcript fragments in either normal or tumor lung tissue , whereas genomic DNA was clearly amplified ( Figure 4 , top ) and the Itpr2 or Gapdh positive controls were readily detected in cDNA samples ( Figure 4 , bottom and data not shown ) . In light of the reported widespread expression of Lasc1 [8] , we examined Lasc1 mRNA expression in brain , liver , kidney , spleen , and testis of adult mice; only testis from which the AK076999 clone was originally derived revealed detectable Lasc1 mRNA ( not shown ) .
We found a highly significant correlation ( r = 0 . 94 ) between the phenotypes of spontaneous incidence and carcinogen-induced multiplicity of mouse lung tumors ( Figure 1 ) . Although we cannot exclude the possibility that part of this correlation rests in population structure , the result suggests that the genetic control of both phenotypes resides mainly in the same genetic loci . The high correlation between the two phenotypes may be explained by the Pas1 locus and its strong effects on both phenotypes , i . e . , odds ratios of ∼12 and ∼15 with spontaneous and chemically induced lung tumorigenesis , respectively [5] . At present , it is not known whether additional lung tumor modifier loci can control both phenotypes . Proof that the same genetic elements control both spontaneous and chemically induced lung tumorigenesis in the mouse model could have important implications for other species , including humans , and therefore warrants further study . The design of the present GWA study was similar to that of two previous studies carried out for either spontaneous [7] or urethane-induced incidence of lung tumors [8] , although we had the opportunity to analyze a larger number of mouse strains . Indeed , in the spontaneous tumorigenesis association , we analyzed 27 strains with 13K SNPs ( WTCHG ) and 23 strains with 140K SNPs ( BROAD ) versus 13 strains with ∼135 , 900 SNPs [7] . In the analysis of urethane-induced lung tumorigenesis , we analyzed 20–22 ( WTCHG and BROAD ) strains for tumor multiplicity and incidence ( Table 1 ) , respectively , versus an effective number of 19 strains with ∼123 , 000 SNPs in [8] , where the two BALB/c substrains should count as a single strain because of their overlapping phenotypes and genotypes , and where lack of available genotype data from the Broad Institute for the C57BL/10J strain allowed analysis only with the 13K WTCHG panel of SNPs . The power to detect genotype-phenotype associations depends on the genomic length over which LD between functional and marker polymorphisms extends . Since LD decays with distance , a high-density map provides better resolution power than a low-density map . However , the haplotype structure of mouse inbred strains shows a mosaic pattern [14] , and haplotype segments ranging from 12 to 608 kb in length have been reported [15] . Those findings suggest that even a medium-density map is sufficient to detect QTLs , especially considering the limited pool of founder genomes of the mouse laboratory strains and their consequent relatedness [16] . Indeed , our use of the BROAD high-density SNP panel ( ∼20 kb per SNP ) confirmed the Pas1 detection but not the SLT6 and Clas5 loci detected by the medium-density WTCHG SNP panel ( average density of ∼160 kb per SNP ) . On the other hand , the power of QTL detection decreases as strain number decreases , and it has been proposed that there is little rationale supporting analysis of complex traits using less than 30 strains [17] . For comparisons , population-based association studies in humans require several hundreds or thousands of individuals , and confirmation of the results is also required [18] . We confirmed none of the 5 SLT loci detected in previous GWA studies . The previous association study on the urethane-induced lung tumor incidence phenotype detected 4 loci ( Clas1 to Clas4 ) [8] , none of which overlap with any of the SLT loci identified by the same group in another study [7] . Using the same phenotype , we confirmed the Pas1 locus ( also called Clas1 in [8] ) , with 2 SNPs in both the WTCHG ( rs13459098 in the Casc1 gene and rs13479086 in the genomic region between Kras and Ifltd1 genes ) and the BROAD panel ( rs30118733 at 5′-end of the Pas1 haplotype and rs30752783 near the Ifltd1 gene ) showing statistical associations . However , the Clas2 to Clas4 loci were not confirmed; genotyping of the functional Clas2 element ( D102E , rs32396036 ) in all strains for which phenotype data were available revealed a −log P = 2 . 4 for urethane-induced lung tumor incidence and a lower statistical association for the other tumor phenotypes ( Table 3 ) . The discrepancy regarding Clas2 to Clas4 detection in our study and that of [8] may rest in small differences in strain composition . The reported functional D102E polymorphism at the Lasc1 gene ( Clas2 locus ) may represent either a locus with a very weak effect or a false-positive finding , since no significant linkage was detectable in either ( BALB/c×C3H/He ) F2 or ( A/J×C3H/He ) F2 intercrosses carrying that polymorphism , and no Lasc1 transcript was detectable in normal lung tissue , in lung tumors , or in several mouse organs , except testis , despite its reported widespread expression [8] . Thus , the reported allele-specific effects by in vitro-transfected expression vectors containing either the 102D or 102E Lasc1 allele ( whose cDNA is contained in a single exon; Vega gene OTTMUSG00000004898 , http://vega . sanger . ac . uk/index . html ) cannot constitute evidence of a locus effect in the absence of such evidence by genetic linkage studies . Haplotype analysis did not increase the reliability of GWA in comparison with single-point analysis , since the Halt loci detected by haplotype analysis , with the exception of those linked to Pas1 , were not confirmed by genetic linkage studies despite the haplotype differences between the parental strains originating the crosses . The Halt7 locus might also represent a false-positive association , since significant genetic linkage near the Halt7 locus position was detected in only one of three informative intercrosses , and since the mapping position of Halt7 is ∼10 Mb apart from the LOD score peak defining the Par2 locus ( 69 . 83 Mb ) [12] and its candidate gene Poli ( 70 . 67 Mb ) [19] . Overall , our comparison of the results of GWA studies in inbred strains with the genetic linkage analysis results confirmed none of the putative loci identified by strain survey , except the Pas1 locus ( Figure 3 ) , notwithstanding the polymorphism of several loci in the genetic cross examined . Comparison of our GWA results with those of previous studies indicates a high variability of the statistical thresholds obtained by permutation . This is expected , since the thresholds are influenced by the number of SNPs and of strains , by the correlation structure of the data , and by the distribution of the phenotypes under study . Thus , inclusion or exclusion of even a single strain in a 20-strain study would strongly affect the statistical thresholds and the loci detected . Our study raises concern about the ability of GWA studies to detect authentic QTL loci and provides a note of caution to the mouse genetics field , where GWAs are seeing wide application across many phenotypes . In agreement with previous studies [17] , [20] , our results support the notion that association mapping in the population of inbred mouse strains is characterized by a high false-positive rate and that such a method must be carried out with a large number of strains ( i . e . , 40 to 150 ) . Accordingly , extensive computer simulation analyses have shown that the power of GWAs studies is low for phenotypes controlled by polygenic traits and that spurious associations are expected [21] . Thus , GWA studies should be carried out in conjunction with genetic linkage analysis to detect relevant loci .
Table 1 lists the data for 32 mouse inbred strains on spontaneous lung tumor incidence ( n = 28 ) , urethane-induced lung tumor multiplicity , i . e . , number of tumors/mouse ( n = 24 ) , and urethane-induced lung tumor incidence ( n = 21 ) derived from [5] , [7] , [22] , [23] . Genomic DNAs from the same inbred strains were obtained from The Jackson Laboratory Mouse DNA Resource ( Bar Harbor , ME , USA ) . Intercross populations consisted of ( BALB/cJ×C3H/HeJ ) F2 mice ( n = 182 males ) [11] , ( A/J×C3H/He ) F2 ( n = 87 males and 87 females ) [13] , and ( BALB/c×SWR/J ) F2 mice ( n = 106 males and 112 females ) [12]; all three populations had been treated with a single dose of urethane , observed without any further treatment , and evaluated quantitatively for lung tumor multiplicity phenotype . RNA was extracted from normal lung of adult male A/J , C57BL/6J , and ( A/J×C57BL/6J ) F1 mice , from urethane-induced lung tumors of ( A/J×C57BL/6J ) F1 mice [4] , and from brain , liver , kidney , spleen , and testis of a male SM/J adult mouse , using the NucleoSpin RNA II kit ( Macherey-Nagel , Bethlehem , PA , USA ) . Genotypes of 12 , 959 and 138 , 793 SNPs publicly available at Wellcome Trust Centre for Human Genetics ( WTCHG ) ( http://www . well . ox . ac . uk/mouse/INBREDS/ ) and at Broad Institute ( http://www . broad . mit . edu/ ) , respectively , were extracted . Table 1 lists the strains for which WTCHG or BROAD genotypes are available . Genomic DNAs of ( BALB/cJ×C3H/HeJ ) F2 mice were genotyped using Illumina SNP genotyping technology which allows the simultaneous analysis of 1536 SNPs [24] . Genomic DNAs of ( A/J×C3H/He ) F2 mice were genotyped using MassARRAY ( Sequenom , Inc . , San Diego , CA ) with a multiplex PCR assays ( iPLEX ) designed by Sequenom SpectroDESIGNER software . The extension products were spotted onto a 384-well spectroCHIP before analysis by MALDI-TOF mass spectrometry . Selected SNPs were genotyped in mouse inbred strains by pyrosequencing on a PSQ96MA system ( Biotage AB , Uppsala , Sweden ) . A short fragment containing the SNP was PCR-amplified using a biotinylated primer as one of the two PCR primers and pyrosequenced according to the manufacturer's instructions . Lasc1 mRNA was searched by RT-PCR using primers: 5′-tactcactggtggtcctaagatcg-3′ and 5′-aggaaaaatggcccttccg-3′; which flank the reported D102E polymorphism of the AK076999 cDNA sequence and , according to the Lasc1 gene structure ( Vega gene OTTMUSG00000004898 , http://vega . sanger . ac . uk/index . html ) , are located in the same exon . The Itpr2 ( 5′-tgatggacaccaagctgaag-3′ and 5′-cgaacattgtttctgcctga-3′ ) and Gapdh ( 5′-tgttcctacccccaatgtgt-3′ and 5′-gtggaagagtgggagttgct-3′ ) genes served as positive controls . Normal lung tissue from 3 A/J , 3 C57BL/6J , and 3 ( A/J×C57BL/6J ) F1 mice and lung tumors from 3 urethane-treated ( A/J×C57BL/6J ) F1 mice were used , as well as brain , liver , kidney , spleen , and testis of a male SM/J mouse . The association between spontaneous and urethane-induced lung tumor phenotypes ( mean percentages or mean multiplicities ) was expressed as a correlation coefficient . The association between each SNP and lung tumor phenotypes ( expressed as log+1 of phenotype value ) was tested by t-test . Haplotype analysis was carried out according to [25] , using a sliding window approach ( window size of 3 SNPs ) and the BROAD dataset . The association between each haplotype and lung tumor phenotypes ( expressed as log+1 of phenotype value ) was tested by F-test . To control the genome-wide false-positive fraction ( 12 , 959 and 138 , 793 t-tests for databases WTCHG and BROAD ) , statistical thresholds were computed both in accordance with the Bonferroni principle and with a permutation test ( 20 , 000 permutations ) . In particular , the distribution of the 20 , 000 smallest p-values among the 12 , 959 ( or 138 , 793 ) p-values under the null hypothesis was obtained . This approach implicitly uses the correlation structure of the data [26] . The 5th and 10th centile of the reference distribution of the smallest p-values were used to guarantee a 0 . 05 or 0 . 10 overall false-positive fraction . Genome-wide genetic linkage was carried out by interval mapping using R/qtl [27] . Marker order and position on chromosomes were established by multipoint analysis of the data using the MAPMAKER/EXP program [28] . Genetic distances were computed using Haldane's mapping function . Single-locus genome scans were carried out using the ‘scanone’ function of R/qtl ( http://www . biostat . jhsph . edu/̃kbroman/qtl/ ) using the Haley-Knott regression analysis . To increase the power to detect weak QTLs and to condition on the presence of the Pas1 genotype , a composite interval mapping was carried out using the three most significant markers identified by a stepwise regression as covariates [29] . In addition , the Kras2 genotype was used as a covariate in a single-locus genome scan of ( A/J×C3H/He ) F2 and ( BALB/c×C3H/He ) F2 intercross populations . Genome-wide significance thresholds ( α = 0 . 05 ) were generated through permutation tests ( 10 , 000 permutations ) as described [30] . | Genome-wide mapping is now popular in both humans and experimental animals , but results of these studies are not validated by independent approaches . We conducted a genome-wide mapping analysis of lung cancer phenotypes in mouse strains and compared our results with those in three previous studies . We found that most of the loci identified in the earlier studies do not replicate . When we combined genome-wide association study with genetic linkage analysis , representing the gold standard of causal inference for allelic effects , the Pas1 locus detected in only one of the three previous genome-wide studies did replicate in genetic crosses , whereas the reportedly functional Lasc1 D102E polymorphism lacked allelic effects in two independent crosses . Our study supports the notion that association mapping in the population of inbred mouse strains is characterized by a high false-positive rate and that such a method must be carried out in conjunction with linkage analysis to detect relevant loci . These results point to the need for independent confirmations in population-based studies . | [
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"and"... | 2009 | Mouse Genome-Wide Association Mapping Needs Linkage Analysis to Avoid False-Positive Loci |
The release of signaling molecules from neurons must be regulated , to accommodate their highly polarized structure . In the developing Drosophila visual system , photoreceptor neurons secrete the epidermal growth factor receptor ligand Spitz ( Spi ) from their cell bodies , as well as from their axonal termini . Here we show that subcellular localization of Rhomboid proteases , which process Spi , determines the site of Spi release from neurons . Endoplasmic reticulum ( ER ) localization of Rhomboid 3 is essential for its ability to promote Spi secretion from axons , but not from cell bodies . We demonstrate that the ER extends throughout photoreceptor axons , and show that this feature facilitates the trafficking of the Spi precursor , the ligand chaperone Star , and Rhomboid 3 to axonal termini . Following this trafficking step , secretion from the axons is regulated in a manner similar to secretion from cell bodies . These findings uncover a role for the ER in trafficking proteins from the neuronal cell body to axon terminus .
Communication between cells and their environment entails the release and reception of signaling molecules . In polarized cells , such as epithelia or neurons , the unique cellular architecture imposes constraints on the precise sites where signal release and reception occur . For example , the distribution of axonal guidance receptors is restricted to specific proximal or distal axon segments [1] . Similarly , secretion of molecules from neurons must be highly polarized for the ligand to propagate in the appropriate receptive field . In some cases , ligand is secreted along the axon , where it interacts with ensheathing glia [2] , [3] , whereas in other cases ligand is secreted locally from cell bodies or growth cones [4]–[6] . Thus , polarized secretion is an essential aspect of ligand processing in neurons . An example of ligand secretion from both cell bodies and axonal termini is that of the Drosophila epidermal growth factor receptor ( EGFR ) ligand Spitz ( Spi ) . In the Drosophila eye imaginal disc , photoreceptors differentiate in the wake of a progressive morphogenetic furrow , which sweeps from the posterior of the disc to its anterior [7] , [8] . Secretion of Hedgehog ( Hh ) from nascent photoreceptor cell bodies promotes the continued movement of the furrow [9] , [10] . Photoreceptor neurons subsequently secrete the EGFR ligand Spi from their cell bodies , triggering neurogenesis in closely neighboring cells [11] , [12] . Once specified as neurons , R1–R6 photoreceptor axons grow across the basal surface of the eye disc , funnel through the optic stalk , and reach the lamina , where they locally induce the differentiation of lamina cartridge neurons [13] , [14] . Secretion of Hh from photoreceptor axon termini triggers an initial phase of neurogenesis in the lamina precursor cells , marked by the expression of Dachshund ( Dac ) and the EGFR itself [5] . The subsequent phase of lamina neurogenesis requires Spi , which is also locally delivered by the incoming retinal axons . EGFR activation by Spi in the lamina leads to the differentiation of five neurons in each cartridge , which express the pan neuronal marker ElaV [6] . Thus , local secretion of Spi at the two distinct poles of photoreceptor neurons controls neurogenesis in both the eye disc and the lamina . While the mechanisms that regulate Hh delivery to axons have been explored [4] , how Spi is secreted from both cell bodies and axonal termini remains unknown . Spi is the cardinal EGFR ligand throughout Drosophila development . It is broadly expressed as an inactive precursor [15] . Spi secretion is dependent on processing by the intramembrane protease Rhomboid-1 ( Rho-1 ) [16] . The inactive Spi precursor is retained in the endoplasmic reticulum ( ER ) by a COPI-dependent mechanism [17] . Trafficking of Spi from the ER to the Rho-1 compartment requires the type II transmembrane protein Star ( S ) [18] , [19] . Upon arrival at this late secretory compartment , Spi is cleaved by the Rho-1 protease and subsequently released to the extracellular milieu . Rho-1 also cleaves the chaperone S , thereby rendering it incompetent to traffic additional Spi molecules [20] . We have previously shown that two additional Rhomboid family members , Rho-2 ( also called Stet ) and Rho-3 ( also called Roughoid [Ru] ) , which are dedicated to oogenesis and eye development , respectively [21] , [22] , localize to the ER , as well as to the late secretory compartment [23] . Although Rho-2 and Rho-3 , like Rho-1 , promote Spi release from the late compartment , their ER presence attenuates EGFR signaling , primarily because of premature cleavage of S [23] . Thus , in photoreceptor neurons , Spi secretion from cell bodies is promoted by both Rho-1 and Rho-3 acting in the late compartment , with the ER activity of the latter also attenuating the overall levels of secreted ligand . The presence of ER markers has been observed in axons and dendrites from various neurons [24] , [25] , and the ER has been suggested to be continuous in Purkinje cell axons [26] . However , the traditional role assigned to axonal ER is in localized translation of transported mRNA , rather than translocation of secreted proteins . Recently , a role for the ER in promoting trafficking of NMDA glutamate receptor to dendrites in cultured rat hippocampal neurons has been described [27] . Here we examined the mechanisms that regulate Spi release from axonal termini . We find that , unlike secretion from cell bodies , axonal secretion of Spi relies exclusively on Rho-3 . Furthermore , the ability of Rho-3 to promote axonal secretion of Spi stems from its combined ER and late compartment localization . Supplementing an ER presence to Rho-1 or eliminating the ER localization of Rho-3 alternates their potencies vis-à-vis axonal Spi secretion . Our data indicate that the importance of the ER stems from its ability to promote axonal trafficking of Rhomboids , a feature that we suggest is linked to the extension of the ER throughout the axon . Finally , we characterize the apical compartment in which Spi is processed in cell bodies , and suggest that it is also present at axonal termini , where Spi is processed following trafficking along the axon . Our results show that subcellular localization of the EGFR-ligand-processing machinery in photoreceptors dictates the polarity of ligand secretion , and highlight the role of the ER in facilitating protein trafficking from the neuronal cell body to the axon terminus .
To investigate the requirement for Rho-mediated cleavage in promoting Spi release from photoreceptor axons , we assessed the effect of rho-1 or rho-3 mutations on lamina neurogenesis . In late third-instar larvae , EGFR activation by Spi delivered from photoreceptor axons leads to the expression of the pan-neuronal marker ElaV at the posterior part of the lamina ( Figure 1A and 1B ) . Visual systems rendered homozygous for a null rho-1 allele , using the Eyeless Gal4 UAS Flip ( EGUF ) system [28] , occasionally show some morphological defects , but ElaV expression in the lamina is not perturbed ( Figure 1C ) . Thus , rho-1 is dispensable for Spi release from photoreceptor axons . We next examined ElaV expression in rho-3 EGUF clones ( Figure 1D ) or in homozygous mutant animals ( Figure 1H ) . While ElaV is properly expressed in the eye disc and brain lobula , we could not detect any ElaV expression in the lamina , indicating that rho-3 is essential for EGFR activation in this tissue . Thus , whereas Rho-1 and Rho-3 can redundantly promote Spi release from cell bodies in the eye disc , only Rho-3 mediates EGFR activation in the lamina . Since Rho-3 is also involved in photoreceptor neurogenesis , the lack of EGFR activation in the lamina of rho-3 mutants may be a secondary effect of defective neuronal development or axonal mistargeting . However , rho-3 mutant photoreceptors properly express the pan-neuronal marker ElaV , as well as markers of specific photoreceptor subtypes ( Figure 1D′ and unpublished data; [23] ) , demonstrating that the general program of photoreceptor differentiation is not perturbed . The only defect we observed at the larval stage is an extra number of neurons , at the expense of non-neuronal cells [23] . Importantly , no overt axonal targeting defects were detected in the mutant , as seen with anti–horseradish peroxidase ( HRP ) staining ( Figure 1D ) . Furthermore , the normal expression of the Hh target genes dac ( Figure 1D′′ ) and EGFR ( Figure 1E ) in the brain reveals that there is no general secretion defect in rho-3 mutants . It thus appears that the rho-3 mutant phenotype reflects a specific defect in processing and secretion of Spi from axon termini . To critically test the functionality of rho-3 mutant photoreceptors , we performed electroretinogram ( ERG ) recordings on adult flies ( Figure S1 ) . Photoreceptor neurons from wild-type or rho-3 eyes properly depolarize in response to light . However , “on/off transients , ” which represent the activity of the post-synaptic lamina neurons [29] , are absent in rho-3 ERG recordings , thus reflecting the defects in lamina neurogenesis . Conversely , “on/off transients” are detected in rho-1 EGUF clones . Hence , in the absence of Rho-3 , Rho-1 facilitates all aspects of photoreceptor development , but not the induction of EGFR activation in the lamina . Rhomboids promote EGFR signaling by processing the ligand Spi in the signal-sending cell prior to its secretion [30] , [31] . This suggests that the lack of EGFR activation in rho-3 mutant laminae stems from a failure in cleavage and secretion of Spi from photoreceptors . To follow Spi processing and secretion , we monitored the localization of Spi–green fluorescent protein ( GFP ) , a biologically active variant of the ligand , tagged by GFP at the extracellular domain [19] . The construct was expressed under the control of GMR–Gal4 [32] , to restrict expression exclusively to the eye disc . Inspection of EGFR distribution in the laminae of wild-type flies reveals many endocytic puncta , which are associated with the ElaV-expressing cartridge neurons ( Figures 1E and S1D ) . We found that Spi–GFP secreted from the eye co-localized in the lamina with EGFR in these puncta , reflecting the release of the ligand from photoreceptor axons and endocytosis of ligand–receptor complexes by lamina cells ( Figure 1F and 1J ) . This co-localization is dependent on cleavage by Rhomboid proteases , since a similarly expressed Spi–GFP construct in which the Rhomboid cleavage site was mutated [33] failed to co-localize with the receptor ( Figure 1G and 1K ) . We next examined the distribution of EGFR in rho-3 mutant laminae , and found that it is uniform compared to wild-type , and lacks the bright endocytic puncta ( Figures 1H and S1E ) . In rho-1 mutant visual systems , the distribution and intensity of laminar EGFR staining were comparable to wild-type ( Figure S1F ) . Furthermore , following expression of Spi–GFP in rho-3 mutant eye discs , GFP-positive puncta could not be detected in the laminae ( Figure 1I and 1K ) . These results indicate that Rho-3 cleaves Spi within the transmembrane domain in photoreceptor neurons , to promote ligand release from their axons to the lamina . In summary , our results show that , whereas both Rho-1 and Rho-3 are capable of mediating Spi secretion from cell bodies in the eye disc , only Rho-3 promotes the secretion of Spi from photoreceptor axons to the lamina . Each of the approximately 750 ommatidia in the Drosophila eye contains eight photoreceptor neurons of distinct identities . R1–R6 neurons project their axons to the lamina , whereas R7 and R8 project their axons to the medulla . To ask which of these neurons provides Spi for patterning the lamina , we used a repertoire of Gal4 lines to drive Rho-3 expression in different subsets of photoreceptors , and monitored their ability to rescue the rho-3 mutant phenotype . All Gal4 drivers used are normally expressed in rho-3 mutant eye discs ( unpublished data ) . As a complementary assay , we expressed Spi–GFP with the same lines , and monitored its co-localization with the internalized EGFR in the signal-receiving lamina neurons . Our findings are summarized in Table 1 , showing that Rho-3 acts to promote Spi secretion from the axons of R2 and R5 . We note that these axons also play a pivotal role in axonal pathfinding , as their mistargeting can lead to defective guidance of the entire ommatidial fascicle [34] . The concordance between the assays of ElaV induction and Spi internalization in the lamina suggests that the difference between the photoreceptors that do or do not provide the signal lies in their ability to process or secrete Spi , rather than in the capacity of the lamina cells to respond only to Spi that is secreted from distinct photoreceptors . A mechanism that may account for the importance of Rho-3 in promoting Spi secretion from axons is RNA transport and localized translation . However , we have found no rho-3 RNA in axons , even after Rho-3 overexpression , which rescues the rho-3 phenotype ( Figure S2 ) . We have previously shown that Rho-1 and Rho-3 differ in their subcellular localization within photoreceptor cell bodies . When ectopically expressed with the Gal4–UAS system , Rho-1 localized to apical punctate structures , whereas Rho-3 was localized to the ER , as well as to the apical puncta [23] . We set out to test the hypothesis that the distinct intracellular localizations of Rho-1 and Rho-3 account for the difference in their capacity to trigger Spi processing and secretion in photoreceptor axons . First , we examined the endogenous localization of the two proteases , without resorting to overexpression . Since antibodies that recognized the endogenous proteins could not be raised , we used recombineering [35] to generate ∼45-kb genomic fragments encompassing the rho-1 or rho-3 locus that express C-terminally tagged Rho-1–yellow fluorescent protein ( YFP ) and Rho-3–GFP in patterns and levels identical to the endogenous proteins . Transgenic lines were generated , in which the recombineered genes were inserted at the same chromosomal location . In the eye disc , genomic Rho-1 ( gRho-1 ) –YFP localized exclusively to the apical compartment , whereas gRho-3–GFP was enriched in the ER , with staining also at the apical compartment ( Figure 2A and 2B ) . These distributions demonstrate that despite the caveats associated with overexpression , the localizations obtained previously by the UAS–Gal4 system faithfully reflected the endogenous localization of these proteins . To identify the sequences mediating the subcellular localization of Rhomboids , we swapped different fragments between Rho-1 and Rho-3 . The resulting chimeras were GFP tagged , and transgenic animals were generated . In all cases the constructs were inserted at the same genomic location , to avoid a difference in expression levels . We find that the subcellular localization of Rhomboids depends on their cytoplasmic N terminus and the first intraluminal loop . Replacing these fragments of Rho-1 with the corresponding fragments from Rho-3 , to yield GFP–R3L1-R1 , relocalized Rho-1 to a Rho-3-like distribution , encompassing the ER and apical compartment ( Figure 2C and 2F ) . Conversely , Rho-3 in which the N terminus and first loop were replaced by those of Rho-1 ( GFP–R1L1-R3 ) retained localization to the apical compartment , but was absent from the ER ( Figure 2D and 2E ) . Importantly , since the active site of the proteases is formed by residues embedded within the fourth and sixth transmembrane helices [36]–[38] , the chimeras uncouple the subcellular localization signal from the catalytic activity . Therefore , the GFP–R1L1-R3 and GFP–R3L1-R1 constructs allow us to specifically define the role of subcellular localization in promoting Spi secretion from axonal termini . Although both Rho-1 and Rho-3 promote Spi secretion from photoreceptor cell bodies , only Rho-3 facilitates Spi secretion from axons . To investigate whether this is due to its ER localization , we assayed the ability of GFP–Rho-1 or GFP–Rho-3 to rescue the rho-3 lamina phenotype . In addition , we tested a modified Rho-1 targeted to the ER and late compartment ( GFP–R3L1-R1 ) and an ER-excluded Rho-3 ( GFP–R1L1-R3 ) using the same assay . All constructs were shown to be efficient in cleaving Spi in cell culture assays and in vivo ( unpublished data ) . Furthermore , since Rho-1 and Rho-3 are normally expressed at low levels in the eye disc , we inserted all the transgenes into attP18 , a genomic landing site that was reported to yield low expression levels [39] , and expression was driven in R2 , R5 , and R8 by MT14–Gal4 . As expected from their in vivo activities , GFP–Rho-3 rescued the rho-3 mutant lamina phenotype , whereas GFP–Rho-1 did not ( Figure 3 ) . Importantly , while GFP–Rho-1 failed to promote Spi secretion from the axons , supplementing it with an ER localization yielded a construct ( GFP–R3L1-R1 ) capable of rescuing the rho-3 phenotype ( Figure 1E and 1F ) . Conversely , whereas GFP–Rho-3 rescued the rho-3 phenotype , a Rho-3 version which is not ER localized ( GFP–R1L1-R3 ) failed to do so ( Figure 1D and 1F ) . These experiments show that ER localization is a critical feature that enables Rhomboid proteases to promote Spi secretion from the axons . We next asked whether intact endogenous Rho-1 , which cannot substitute for Rho-3 in Spi processing for axonal release , can facilitate Spi secretion when enriched in the ER . Passage through the ER is an essential step in Rho-1 maturation , as a protein bearing transmembrane domains . We thus attempted to compromise Rho-1 exit from the ER , by removing one copy of the syntaxin sed5 , which is required for the fusion of ER-derived vesicles with the Golgi [40] , [41] . When HA-tagged Rho-1 was expressed in sed5 homozygous mutant clones , its subcellular distribution shifted almost completely to the peri-nuclear ER ( Figure 3G and 3H ) . In rho-3 mutants in which sed5 gene dosage was halved , we found that some ElaV expression was restored to the lamina ( Figure 3I and 3J ) . Therefore , when endogenous Rho-1 trafficking out of the ER is compromised , it can substitute for Rho-3 and promote Spi release from axons . We note here that under strong overexpression conditions , Rho-1 also rescues the rho-3 phenotype . This may reflect the perdurance of some Rho-1 in the ER when its export machinery is heavily burdened . Indeed , a low endogenous level of ER activity by Rho-1 en route to the apical compartment has been suggested previously [17] . Accordingly , the ER levels of Rho-1–HA in sed5 heterozygotes were too low to be detected by anti-HA staining , yet restored some laminar ElaV expression to rho-3 mutants . In summary , our results indicate that the difference in subcellular localization is the cause of the distinct ability of Rho-3 , but not Rho-1 , to promote Spi processing and secretion from photoreceptor axons . The combined ER and secretory compartment localization of Rho-3 is critical for its ability to promote Spi secretion from axons . We next asked whether the ER component of this localization is sufficient for Rho-3 function in lamina induction . We uncoupled the two localizations by tagging Rho-3 with a KDEL sequence at its luminal C-terminus , thereby retaining it in the ER . This construct , as well as a KDEL-tagged Rho-1 , were fused at their N-termini to GFP , and inserted into the same genomic landing site as the constructs previously described . Although GFP–Rho-3–KDEL and GFP–Rho-1–KDEL localize to the ER , and efficiently cleave Spi in cell culture assays and in vivo ( unpublished data ) , they could not rescue the rho-3 lamina phenotype upon expression in the eye by MT14–Gal4 ( Figure 4A–4D ) . This indicates that the ER localization of Rho-3 is not sufficient to promote EGFR signaling in the lamina , and suggests that the active Spi molecules secreted from the axons are not processed in the ER . Since Spi that is secreted by photoreceptor axons is not cleaved in the ER , we monitored the capacity to traffic the Spi precursor to axonal termini . GMR–Gal4-driven expression in a wild-type eye disc of the Spi precursor marked with GFP at the N terminus , gave rise to translocation of the GFP tag across the entire length of the axon bundle ( Figure 4E and 4H ) . However , it is not possible to determine by this assay whether the ligand that reaches the axon termini represents the precursor form or the cleaved ligand . Two lines of evidence suggest that the ligand precursor can be trafficked from the cell body to the axon terminus . First , a non-cleavable form of Spi also reached the axonal growth cones , when expressed in the eye disc ( Figure 4F and 4H ) . Second , expression of mSpi–GFP in a rho-3 mutant background , in which the precursor does not undergo cleavage in the ER , gave rise to a ligand distribution in axons that was similar to wild-type ( Figure 4G and 4H ) . Taken together , these experiments demonstrate that the Spi precursor can be trafficked along the axon , and suggest that it is cleaved outside of the ER prior to its secretion . To support this conclusion , we assayed the ability of a cleaved form of the ligand ( cSpi ) , which is localized to the ER [17] , to rescue the rho-3 phenotype upon expression by MT14–Gal4 in R2 , R5 , and R8 . Biologically active cSpi , tagged with HA or HRP , failed to induce ElaV expression in rho-3 laminae ( Figure S3 ) . This is consistent with the notion that cleavage of Spi in the ER is not the mode by which Rho-3 promotes secretion , and suggests that the importance of the ER to Rho-3 function stems from a different mechanism . The above experiments demonstrate that while ER localization is crucial for the ability of Rho-3 to promote axonal secretion of Spi , the functional ligand is not cleaved in the ER . We therefore examined whether the ER could promote Rho-3-dependent signaling by facilitating the trafficking of the ligand-processing machinery to axons . Examination of the endogenous ER markers protein disulfide isomerase ( PDI ) and BiP reveals that the ER extends throughout the axons of developing photoreceptor neurons ( Figure 5A and unpublished data ) , as does the detection of KDEL-tagged ER luminal proteins ( Figure 5B ) . ER markers were also observed in axons of adult flies ( unpublished data ) , consistent with previous reports indicating that the ER is continuous in the axons of various neurons [26] , [42] . We also detected the presence of endogenous ER exit sites ( marked by dSec16 [43] ) along the axons and at their termini in the lamina ( Figure 5C ) , suggesting that proteins are released from the ER in these locations . Consistently , Golgi outposts ( marked by Mannosidase II ( ManII ) –GFP [44] ) were also evident along the entire axon length ( Figure 5F ) . These observations suggest that in photoreceptor axons , the ER can be used by secreted proteins to reach a given exit site , prior to progressing along the secretory pathway . To further test this idea , we expressed an ER-localized GFP ( GFP–KDEL ) [45] in the eye disc . GFP immunofluorescence was observed throughout the axons , while GFP mRNA was confined to the cell bodies ( Figure 5D and 5E ) . Thus , proteins localized to the ER in the cell body can also reach the axon , by utilizing the extension of the ER to the axon . Since Rho-3 is ER localized in the cell body , it could use this compartment in a manner similar to GFP–KDEL to move distally . Indeed , whereas rho-3 mRNA is not detected in the axons ( Figure 5G ) , gRho-3–GFP is found in a continuous distribution in axons ( Figure 5H ) . Conversely , gRho-1–YFP , which is not localized to the peri-nuclear ER , fails to reach the optic stalk ( Figure 5I ) . To examine the possibility that ER localization would promote the axonal delivery of a Rhomboid protease , we generated another gRho-1–YFP construct , with a C-terminal KDEL tag . In contrast to gRho-1–YFP , gRho-1–YFP–KDEL was robustly distributed along the entire length of the axons ( Figure 5J ) . Taken together , these results imply that the importance of the ER for Spi signaling in this physiological context stems from its ability to promote trafficking to the axons , where Spi processing subsequently occurs . Besides Rho-3 , Spi and S are also localized to the ER in the eye disc . Therefore , the three components could associate in this compartment for joint trafficking to the axons . To test this hypothesis , we examined the co-localization of biologically active , HA-tagged versions of Spi or S with Rho-3–GFP . S–HA co-localizes with Spi–GFP in the axons at the optic stalk ( Figure 6B ) . In photoreceptor cell bodies S was shown to stabilize Spi [46] . We observed that S stabilizes Spi in axons , and promotes its trafficking through the axons , as more Spi–GFP molecules arrive at the lamina when co-expressed with S–HA ( Figure 6D–6F ) . S–HA also co-localizes with Rho-3–GFP in the axons . Both the ligand and chaperone thus appear to co-localize with Rho-3–GFP in axons traveling through the optic stalk ( Figure 6A and 6C ) . We have previously shown that S is a substrate for ER-localized Rhomboid proteases [23] , and that cleaved S cannot traffic Spi [20] . ER-based cleavage of S has a functional significance , as it limits the trafficking of the Spi precursor by the S chaperone out of the ER . This results in an increased sensitivity of EGFR signaling to S levels . Indeed , S heterozygous flies exhibit reduced EGFR signaling during oogenesis and eye development , where the ER-active Rho-2 and Rho-3 mediate Spi processing , respectively [23] . Thus , a sensitivity to S levels is indicative of exposure to Rhomboid-based cleavage in the ER . We find that S heterozygous flies show a severe reduction in ElaV expression in the lamina ( Figure 6G and 6H ) . Importantly , the defect in EGFR signaling in the laminae of these flies is significantly more severe than the compromised induction of photoreceptors in the eye disc . This may reflect a longer exposure of S to ER cleavage by Rho-3 during trafficking to the axon termini . Thus , the hypersensitivity of the lamina to S gene dosage supports the notion that S and Rho-3 are jointly trafficked through the ER in photoreceptor axons . Following its trafficking to the axonal termini , Spi seems to be secreted locally at a precise location [6] . In the eye disc , Spi is also secreted locally , from a late secretory compartment where Rho-1 and Rho-3 reside [23] . To gain insight into the mechanism of Spi release , we set out to identify the “late compartment” in the eye disc . A variety of compartment markers were tested for co-localization with Rho-1–HA expressed in the eye disc ( see also [23] ) , including a collection of YFP-tagged Rab proteins [47] . The only significant co-localization was observed with YFP–Rab4 and YFP–Rab14 ( Figure 7A and 7B ) . This co-localization was also verified in cell culture , where a significant proportion of Rho-1- , Rab4- , and Rab14-positive puncta overlap ( Figure S4A ) . YFP–Rab4 and YFP–Rab14 also co-localize with apical , but not peri-nuclear , Rho-3–HA staining in the eye disc ( Figure S5 ) . Interruption of Rab4 and Rab14 function in photoreceptors by RNA interference ( RNAi ) or dominant negative ( DN ) approaches did not result in any discernible phenotypes . However , both Rab proteins interact with effectors of Rab11 [48] , [49] , suggesting a role for this major conserved regulator of endosomal trafficking in Spi exocytosis . Indeed , expression of a DN form of Rab11 in Drosophila cell culture disrupted the morphology of Rab4/14 endosomes , marked by Rho-1–red fluorescent protein ( RFP ) or Spi–HA , when the latter was co-expressed with S ( Figure S4 ) . Furthermore , in the eye imaginal disc , Rho-1–GFP , which is normally localized to discrete puncta , is misocalized upon co-expression of Rab11DN by GMR–Gal4 ( Figure 7C and 7D ) . Thus , although Rab11 does not co-localize to the Rho-1-containing endosomes , its function is essential for their correct formation . We then asked whether EGFR signaling is affected by impairment of the Rab4/14 compartment . Indeed , expression of Rab11DN by GMR–Gal4 led to a reduction in the number of ElaV-expressing cells in the eye disc ( unpublished data ) , as did expression of a Rab11 RNAi construct ( Figure 7E ) . Importantly , there was no alteration of photoreceptor R8 differentiation , which is not dependent upon EGFR signaling . Since this phenotype may reflect a requirement for Rab11 in the signal-receiving cells , downstream to EGFR , we expressed the Rab11DN construct specifically in R8 , which is the only photoreceptor that acts exclusively as a signal-emitting cell . Again , EGFR phenotypes such as missing photoreceptors and mis-rotated ommatidia were readily apparent ( Figures 7F , S6A , and S6B ) . This indicates that Rab11 acts non-autonomously in R8 , where it is required for EGFR ligand secretion . When larvae expressing UAS–Rab11DN by GMR–Gal4 in the eye disc were allowed to develop , the resulting adults had very small and rough eyes , as previously reported ( Figure 7G; see also [47] ) . Although Rab11 has pleiotropic functions , this phenotype is at least partly due to a specific failure in EGFR ligand secretion , since co-expression of Rho-1 with Rab11DN considerably ameliorated the phenotype ( Figure 7H ) . We conclude that in the eye disc , Spi is cleaved and secreted from Rab4/14 endosomes , and that the normal function of these endosomes is required for EGFR ligand trafficking and processing . The requirement for Spi cleavage to take place after ligand is trafficked out of the ER in both the cell bodies and axons , raised the possibility that subsequent trafficking steps also share common features . We therefore sought to determine whether Spi secretion from the axons similarly involves Rab4/14 endosomes , and is dependent upon Rab11 function . Indeed , we found that Rab4 or Rab14 , expressed in the eye disc by GMR–Gal4 , reached axonal growth cones , as did Rab11 . Note that GMR–Gal4 does not drive expression in the lamina ( [6] and Figure 4E–4G ) . As in the eye disc , co-localization between Rho-3–HA and YFP–Rab4 or YFP–Rab14 was observed in axonal termini ( Figure S5 ) , but not along the length of the axons in the optic stalk ( unpublished data ) . Expression of Rab11DN in the eye disc by GMR–Gal4 led to a significant reduction in the number of ElaV-positive cells in the lamina , while Dac expression was normal ( Figure 7I ) . Importantly , expression of Rab11DN in R8 , which does not secrete Spi to the lamina , severely impairs EGFR signaling in the eye disc but not in the lamina ( Figure S6 ) . To further separate the axonal function of Rab11 from its requirement in photoreceptor differentiation , we expressed Rab11DN by GMR–Gal4 together with RasV12 , which induces massive photoreceptor recruitment ( [50] and Figure S7A ) . In the eye disc RasV12 was epistatic to Rab11DN , where all cells were converted to ElaV-expressing neurons , supporting the notion that Rab11 acts upstream to Ras ( Figure S7 ) . Expression of RasV12 in the eye induces an enlarged lamina with extra lamina neurons . Co-expression of Rab11DN attenuated the effects of RasV12 on lamina development in seven of 12 specimens , leading to wild-type or even reduced ElaV expression ( Figure S7 ) . In other words , we have uncoupled the requirement for Rab11 for secretion of the ligand in the eye disc and in the lamina by using RasV12 to bypass the requirement for the ligand in the eye disc . Therefore , this effect specifically represents the requirement for Rab11 to allow secretion of the ligand at the axon termini . This is consistent with the notion that after trafficking of mSpi , S , and Rho-3 to the axonal termini , secretion occurs in a similar manner to the eye disc , utilizing a Rab11-dependent mechanism .
Polarized secretion of ligands from a signal-emitting cell to the appropriate receptive field is crucial for correct intercellular communication . Control over EGFR ligand secretion , and consequently EGFR activation , in Drosophila is achieved through trafficking and compartmentalization of the ligand-processing machinery . This work identifies a link between the subcellular localization of the Spi-processing machinery and the polarized release of Spi from axons . Subcellular localization of Rhomboid proteases , which process the inactive Spi precursor , impinges on ligand secretion [23] . Both Rho-1 and Rho-3 are localized to apical Rab4/14 endosomes , where they are redundant in promoting Spi release from cell bodies . In contrast , only the Rho-3 protease mediates axonal secretion of Spi . This is evident from the rho-3 mutant phenotype , which shows a complete loss of EGFR activation in the lamina . Since the two proteases are expressed in the neurons which secrete Spi , and share the same substrate specificity , these features cannot account for the specific requirement for rho-3 . RNA transport and localized translation of Rho-3 are also inconsistent with the following observations: ( a ) no rho-3 RNA was detected in axons , ( b ) gRho-3–GFP , reflecting endogenous expression , is localized throughout the axon , rather than concentrated at a point of localized translation , and ( c ) Rho-3 cDNA , devoid of 3′ or 5′ UTRs , rescued the mutant phenotype . The RNA of the rescuing construct was also not localized to axons . Our results indicate that the exclusive requirement for Rho-3 is due to its ER localization . Re-localization of some of the Rho-1 pool to the ER , or removal of Rho-3 from the ER , achieved by swapping specific sequences , alternated their potencies to promote axonal secretion of Spi . Furthermore , when the ER export of endogenous Rho-1 was compromised , EGFR activation was partially restored to the lamina of rho-3 mutants . Thus , the ER localization of Rho-3 in photoreceptor neurons serves a dual function: it negatively regulates Spi secretion from cell bodies , via premature cleavage of S [23] , and positively promotes Spi secretion from the axons to the lamina , by facilitating trafficking of the ligand-processing machinery to axon temini ( schematized in Figure 7J ) . How does the ER localization of Rho-3 contribute to Spi secretion from axons ? The inability of GFP–Rho-3–KDEL or cSpi–HA to rescue the rho-3 phenotype demonstrates that the axonally secreted Spi is not cleaved in the ER , and prompted investigation into the role of the ER in promoting axonal trafficking . We have shown that in Drosophila photoreceptor neurons , the ER extends throughout the axons . ER exit sites and Golgi outpost markers were also detected in axons . The continuity of the ER was previously demonstrated in Purkinje neurons [26] and in other cell types , including Drosophila oocytes [42] , [51] . This implies that ER-localized proteins could use this compartment to move distally in the axon . Indeed , GFP–KDEL expressed in the eye disc reaches the axonal termini . Furthermore , the ER-localized Rho-3 is enriched in axons , as opposed to Rho-1 , which is restricted to endosomes . Importantly , restricting the gRho-1 construct to the ER with a KDEL sequence gave rise to a robust translocation of the protease throughout axons , reaching their growth cones in the lamina . ER-facilitated trafficking of Rho-3 could occur through diffusion in the ER membrane , with exit and retrieval of ER-derived vesicles being biased distally . Alternatively , and perhaps more likely , the ER presence of Rho-3 could lead it to an exit site localized at the axon base , from which trafficking would be directed towards the growth cones . This would explain the ability of Rho-1 to rescue the rho-3 phenotype under strong overexpression conditions . Distinction between these possibilities would require co-localization of Rho-3 or Spi immunoreactivity with known compartment markers in axons . So far , and despite a large number of markers examined , we could not detect such co-localization ( unpublished data ) . Since the extension of the ER is correlated with the growth of the axons [52] , [53] , ER-facilitated trafficking also provides a means of ensuring that ligand is released only once the axons have reached their target layer , and ER exit sites and Golgi membranes are set in place . Spi , S , and Rho-3 are all localized to the peri-nuclear ER in the eye disc . Since all three proteins can interact with one another [19] , [46] , this implies that the processing machinery could assemble in the ER for joint trafficking . Indeed , we found that Spi , S , and Rho-3 also co-localize in photoreceptor axons . Further evidence for the joint trafficking of S and Rho-3 is the marked sensitivity of EGFR signaling in the lamina to S levels . We have previously shown that S cleavage in the ER leads to compromised EGFR activation phenotypes upon halving S gene dosage [23] . The observation that EGFR signaling in the lamina is even more sensitive to S gene dosage than in the eye suggests that Rho-3 and S spend a significant time in the ER , where the chaperone is exposed to inactivation by cleavage . How targeting of Spi–S–Rho-3 complexes to the basally located axons or the apical Rab4/14 endosomes is achieved is unclear . In the case of Hh , the presence or absence of the C-terminal cleavage fragment in the Hh-containing vesicle determines its destination [4] . The Spi C-terminus is not required for axonal targeting , since a Spi–GFP construct lacking most of the C-terminus showed the same distribution as intact Spi–GFP upon expression in the eye ( unpublished data ) . Alternatively , another factor , which would be ER localized , could promote the trafficking of the processing machinery to axons . This factor is also expected to be expressed mainly in R2 , R5 , and R8 , accounting for their importance in Spi secretion to the lamina . In the Drosophila oocyte , the polarized ER exit of another EGFR ligand , Gurken , is regulated by Cornichon . Somatic functions for Cornichon and its homolog Cornichon related have also been identified but not thoroughly explored yet [54] . While the presence of ER markers in axons or dendrites has been previously reported [27] , the biological significance of such observations , commonly derived from protein localization data in cultured neurons , could only be speculated upon , since no functional readout was examined . The unique properties of photoreceptor axons in Drosophila , which not only conduct electrical signals but are also involved in transmitting developmental cues at an earlier phase , have allowed us to functionally demonstrate the essential role of the ER in trafficking the complete EGFR ligand-processing apparatus to axon termini . This mechanism is clearly distinct from the established roles of the axonal ER in allowing local translation of secreted or transmembrane proteins whose mRNAs are enriched at axon termini . Spi is released to the extracellular milieu following cleavage by Rho-1 . Different experimental systems have yielded conflicting reports as to the compartment in which the protease resides [18]–[20] , [55] . We now find that in both photoreceptor neurons and Schneider cells , Rho-1 is localized to an endosomal population marked by Rab4 and Rab14 . Rab4 localizes to fast recycling endosomes , which mediate the retrieval of endocytosed cargo to the plasma membrane [56] , [57] . Rab14 mediates trafficking between the Golgi and endosomes [58] , [59] . Both Rab4 and Rab14 share binding proteins with Rab11 [48] , [49] , a major regulator of vesicle transport . The role of endosomal dynamics in Spi secretion is manifested by the EGFR phenotypes obtained following expression of Rab11 RNAi or DN constructs . While Rab11 has pleiotropic functions and is not dedicated to EGFR signaling , perturbing Rab11 directly impinges on Spi secretion . This was evident from the mislocalization of Rho-1–GFP in Rab11DN-expressing photoreceptors , and from similar effects in cell culture . This mislocalization is likely the cause of the phenotype , since co-expression of Rho-1 or Rho-3 with Rab11DN abrogated the small eye phenotype associated with Rab11DN expression . Although interfering with endosomal dynamics may also perturb signaling downstream of the receptor , we did not observe a mislocalization of EGFR itself ( unpublished data ) . Furthermore , the expression of Rab11DN in R8 impaired the differentiation of nearby cells into photoreceptor neurons , demonstrating that Rab11 acts non-autonomously upstream of the receptor , consistent with a role in ligand secretion . Rho-1 and some of the Rho-3 pool are localized to Rab4/14 endosomes . The intracellular route by which they reach these compartments remains to be explored . From the ER accumulation of Rho-1–HA in sed5 mutant clones , we infer that the proteases do not undertake a Golgi-independent route to the Rab4/14 endosomes [41] . Furthermore , Rab14 mediates trafficking between the Golgi and endosomes [59] , and Rab11 endosomes can be reached without passing through the plasma membrane ( see for example [60]–[62] ) . Therefore , there is no indication that Rhomboids must pass through the plasma membrane to reach the endosomal compartment . Nevertheless , if Spi is secreted by fusion of Rhomboid-containing endosomes with the membrane , then retrieval by endocytosis should play a role in shaping the steady-state distribution of Rhomboids . Accordingly , we have found that upon expression of a DN form of the Dynamin Shibire , Rho-1–HA immunofluorescence is detected on the plasma membrane ( unpublished data ) . Trafficking of Spi to endosomes also provides an efficient means of disposing of the ligand in cells that do not express a Rhomboid protease , to prevent nonspecific cleavage on the plasma membrane . In this case , the membrane-bound precursor could be sorted to a membrane domain that segregates to multi-vesicular bodies , and then degraded in the lysosome . Accordingly , distinct membrane domains have been described for Rab4 and Rab11 endosomes [63] . Finally , we detected a co-localization between Rab4/14 and Rho-3 at axonal termini , but not in the optic stalk , and found that disrupting Rab11 function in the eye disc compromised EGFR signaling in the lamina . This effect was not due to defects in eye development , as Rab11DN expressed in R8 also impaired eye development but had no effect on the lamina . This finding raises the possibility that the final steps of secretion from axonal termini and cell bodies are regulated in a similar manner , although Rab11 seems to play a more prominent role in secretion from cell bodies . A precedent supporting such a hypothesis is the requirement for Sec15 , which interacts with Rab11 , for the localization of several molecules at both photoreceptor cell bodies and axonal termini [64] . In summary , our results describe a mechanism of ER-facilitated trafficking of secreted molecules in axons , prior to processing and secretion at the axon tip . This mechanism could also be utilized for other proteins that are secreted in a polarized manner in neurons .
For the generation of gRho-1–YFP and gRho-3–GFP , 40–45 kb from the rho-1 or rho-3 loci , encompassing the ORFs and flanking region , were cloned into P[acman–attB , AmpR] by recombineering-mediated gap repair [35] . The domains extend between 3L:1437674 and 1475379 and 3L:1355719 and 1397235 ( release 5 . 23 ) for rho-1 and rho-3 , respectively . A YFP tag or a YFP–KDEL was inserted at the rho-1 C-terminus by GalK positive/negative selection [65] . rho-3 was GFP tagged at the C-terminus using the PL452 C-EGFP tag template vector [66] . Both constructs were injected into VK00005 landing site . For GFP–Rho-1 , GFP–Rho-3 , GFP–R1L1-R3 , and GFP–R3L1-R1 , eGFP was cloned into pUAST–attB at the BglII–EcoRI sites . cDNAs were then cloned using EcoRI and XhoI . All constructs were sequenced , and injected into attP18 lines [39] . cSpiHA contains a triple HA tag from pTWH , inserted after the Spi cleavage site . mSpi–HA was generated by a site-directed mutagenesis insertion of an XhoI site after T58 of Spi , into which a triple HA tag was subsequently inserted . mSpi–GFPmut was obtained from S . Urban [33] , and cloned into pTWM . Cleavage assays in S2 cells verified that this construct cannot undergo Rhomboid-dependent cleavage ( unpublished data ) . S–HA is the S cDNA cloned into pTHW . mSpi–GFP and cSpi–HRP were previously described [17] , [19] . The cleavage activity of all Rhomboid constructs has been tested in cell culture , and the biological activity of all UAS-based constructs was assayed by expression in wing or eye imaginal discs . Climbing late third-instar larvae were dissected and fixed in PBS containing 4% PFA . All subsequent washes and antibody incubations were done in PBS with 0 . 1% Triton X-100 . Primary antibodies used were anti-FasIII ( mouse , 1∶50 ) , anti-EGFR ( rat , 1∶1 , 000 ) , anti-Senseless ( guinea pig , 1∶2 , 000; from H . Bellen ) , anti-dSec16 ( rabbit , 1∶1 , 000; from C . Rabouille ) , anti-Myc ( mouse , 1∶100; Santa Cruz Biotechnology ) , anti-GFP ( chick , 1∶2 , 000; Abcam ) , anti-HA ( mouse , 1∶1 , 000; Roche ) , and anti–Troponin H to detect BiP ( rat , 1∶100; Babraham Bioscience Technologies ) . Anti-ElaV ( rat , 1∶2 , 000 , or mouse , 1∶500 ) and anti-Dac ( mouse , 1∶500 ) were obtained from the Developmental Studies Hybridoma Bank , University of Iowa . Cy-5-conjugated goat anti-HRP , as well as Cy-2- , Cy-3- , and Cy-5-conjugated secondary antibodies ( 1∶200 ) were obtained from Jackson ImmunoResearch . In situ hybridizations using rho-3 or GFP probes were done using standard techniques . The following lines were used: GMR–Gal4 , Sca–Gal4 , m™–Gal4 ( from M . Mlodzik ) , Lz–Gal4 , K25–Gal4 , MT14–Gal4 ( [34] , from I . Salecker ) , UAS–GFP–KDEL [45] , MS1096–Gal4 , PDI–GFP [67] , sed5AR113 ( From C . Rabouille ) , SIIN23 , a collection of YFP-tagged , native or DN UAS–Rab transgenes [47] , UAS–Rab11DN ( from M . Gonzalez-Gaitan ) , UAS–ManII–GFP ( from Y . Jan ) , and UAS–Rab11–RNAi ( VDRC22198 ) . Null alleles of rho-1 ( rho-1Δp38 ) and rho-3 ( ruPLLb ) were recombined with FRT2A , and crossed to ey–Gal4 , UAS–FLP/Cyo;FRT2a , GMR–hid , l ( 3 ) CL–L1/TM6B to generate entirely mutant eyes [28] . To generate sed5 AR113 MARCM clones expressing Rho-1–HA , C155–Gal4 , UAS–CD8GFP , hsFLP;Gal80 , FRT40A females were crossed to sed5 AR113 , FRT40A/+;UAS–Rho-1HA/+ males . Wild-type clones were generated with a chromosome bearing only FRT40A . Clones expressing ManII–GFP were induced in animals of the following genotype: C155–Gal4 , hsFLP/+;UAS–ManII–GFP/+;FRT82B . UAS–mSpi–GFPmut , UAS–cSpi–HA , UAS–mSpi–HA , UAS–GFP–Rho-1 , UAS–GFP–Rho-3 , UAS–GFP–R1L1-R3 , and UAS–GFP–R3L1-R1 were generated by standard P-element or phi31 germline transformation procedures . ERG recordings were performed as described in [29] . | Cells secrete signaling molecules that trigger a variety of responses in neighboring cells by activating their respective cell-surface receptors . Because many cells in an organism are polarized , regulating the precise location of ligand secretion is important for controlling the position and nature of the response . During the development of the compound eye of the fruit fly Drosophila , for example , a ligand of the epidermal growth factor family called Spitz ( Spi ) is secreted from both the apical and basal ( axonal ) poles of photoreceptor cells but with different outcomes . Photoreceptor cells are recruited to the developing eye following apical secretion of Spi . Conversely , basal secretion of this same ligand , at a significant distance from the cell body , triggers differentiation of cells in the outer layer of the brain . Although secretion of Spi is known to occur at both poles of the cell , one important question is how Spi and its processing machinery are trafficked throughout the length of the photoreceptor axon to achieve basal secretion . In this study we show that the key to axonal trafficking is the regulated localization of Spi and its processing machinery , including the intramembrane protease Rhomboid , to sites within the endoplasmic reticulum ( ER ) , which extends along the length of the axon . Two different Rhomboid proteins are expressed in photoreceptor cells , but only one of them is localized to the ER . We show that this ER-localized Rhomboid is indeed necessary and sufficient for Spi processing at axon termini . Our work therefore demonstrates how variations in intracellular localization of conserved signaling components can alter signaling outcomes dramatically . It also highlights the importance of the ER in trafficking proteins along the axon . | [
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... | 2010 | Polarized Secretion of Drosophila EGFR Ligand from Photoreceptor Neurons Is Controlled by ER Localization of the Ligand-Processing Machinery |
Keratins are cytoskeletal intermediate filament proteins that are increasingly being recognised for their diverse cellular functions . Here we report the consequences of germ line inactivation of Keratin 76 ( Krt76 ) in mice . Homozygous disruption of this epidermally expressed gene causes neonatal skin flaking , hyperpigmentation , inflammation , impaired wound healing , and death prior to 12 weeks of age . We show that this phenotype is associated with functionally defective tight junctions that are characterised by mislocalization of the integral protein CLDN1 . We further demonstrate that KRT76 interacts with CLDN1 and propose that this interaction is necessary to correctly position CLDN1 in tight junctions . The mislocalization of CLDN1 has been associated in various dermopathies , including the inflammatory disease , psoriasis . These observations establish a previously unknown connection between the intermediate filament cytoskeleton network and tight junctions and showcase Krt76 null mice as a possible model to study aberrant tight junction driven skin diseases .
The epidermis provides a stable and selectively permeable barrier essential to terrestrial life . Together with microfilaments and microtubules , intermediate filaments ( IFs ) make up the major components of the epidermal cytoskeleton . Keratins are the largest subgroup of the IF proteins and comprise the major structural proteins in epithelial cells [1] . Keratins are composed of a central , filament forming , alpha-helical rod domain of ∼310 amino acids that is flanked by non-helical head and tail domains [1] , [2] , [3] , [4] , [5] . They act as a flexible scaffold enabling cells to resist physical stress . Consequently , defects in IFs can lead to cell fragility and are linked to a wide array of genodermatoses and cancers [5] , [6] . The classical view that keratins simply provide a structural scaffold has been challenged by recent studies demonstrating their increasingly specialised and diverse functions [7] . These include protection from apoptosis [8] , [9] and injury [10] , regulation of epithelial polarity [11] , [12] and influence on cell size and protein translation[10] , [13] , [14] , [15] . The functional integration of cytoskeletal elements and cellular junctions is critical for the establishment and maintenance of the epidermal barrier . Tight junctions ( TJ ) form a seal between cells which make up the layers of the epidermis [16] . This barrier is selectively permeable , allowing passage of small molecules , but restricting water loss , and allowing for antigen sampling by immune cells [16] , [17] , [18] . TJs are composed of scaffolding and adhesion molecules including claudins , junctional adhesion molecules and occludins . Defective tight junction organization has been linked to compromised barrier function [17] and the development of various dermopathies including psoriasis [19] , [20] . The TJs are thought to interact with the IF network by binding of a number of integral or associated TJ proteins that complex to to F-actin [21] but their associations , if any , with the keratin IF network are unclear . In this report we have studied the effects of Krt76 disruption in mice and demonstrate that the KRT76 protein is essential for postnatal survival beyond ∼3 months of age . Loss of KRT76 leads to the acquisition and infection of skin wounds which fail to properly resolve over time . This phenotype correlates with observations showing that the gene is up-regulated during normal wound healing and is required for this process . At a mechanistic level we show that loss of KRT76 is associated with defective tight junction function through the mislocalization of Claudin1 ( CLDN1 ) , an integral TJ component which we show binds to KRT76 . These findings identify a critical new relationship between the IF network and TJs which we propose is essential for epidermal homeostasis .
As part of the Wellcome Trust Sanger Institute ( WTSI ) Mouse Genetics Programme [22] , we screened the skin of the mutant mouse strains generated . This skin screen is discussed in an accompanying article in this issue of PLoS Genetics [23] . From this screen we identified significant cutaneous defects in mice homozygous for the gene trap “knockout first” [24] allele of Keratin 76 ( Krt76tm1a ( KOMP ) Wtsi hereafter Krt76tm1a ) ( Figure 1A ) [23] . These animals have a splice acceptor-LacZ reporter integrated upstream of floxed exon 2 that allows gene expression to be traced whilst disrupting gene function . Quality control of this mutant allele and correct genome positioning has been confirmed by long range PCR ( http://www . sanger . ac . uk/mouseportal/search ? query=krt76 ) . Krt76 expression has previously been reported in the palatal and gingival epithelium [25] . By utilising the integrated LacZ reporter in our Krt76tm1a/+ model we confirmed expression at these locations but also detected previously unreported expression in the vagina and the eyelid ( Figure 1B ) . Krt76tm1a mice were then further back-crossed onto a C57BL/6 genetic background and bred back to homozygosity to determine the full consequences of Krt76 disruption . Krt76tm1a/tm1a neonates exhibit flaky skin ( Figure 1C , arrow-insert ) , although these defects diminish somewhat with the emergence of hair follicles . After weaning , mutant mice are distinguished by their unkempt , dull coats and smaller body size ( Figure 1D , E ) and by scaling of skin of the tail ( Figure 1F ) [26] . Krt76tm1a/tm1a mice also show abnormal paw pad hyperpigmentation ( Figure 1G ) which corresponds with Krt76 expression as reported by LacZ , which is observed throughout the stratified epidermal layers and in the exocrine glands ( Figure 1H , H′ , I ) . Haematoxylin and Eosin ( H&E ) staining of wild type ( WT ) and Krt76tm1a/tm1a paw pads revealed an overall epidermal thickening , with reduced granular layer compaction and an increased cornified layer ( Figure 1J , J′ ) . Pigment was also observed in the dermis ( Figure 1J′ see arrowhead ) . To further explore KRT76 expression , we performed immuno-staining on paw pad skin using antibodies raised against human KRT76 epitopes that are predicted to be disrupted in tm1a animals . Specific KRT76 expression was highest in the granular cell layer where it overlapped with Filaggrin ( FLG ) in serial sections ( Figure 1K ) . This site of expression correlates with β-galactosidase activity detected via the integrated reporter gene ( Figure 1I ) . Importantly granular layer staining was absent from Krt76tm1a/tm1a animals ( Figure 1L ) and Western blots from epidermal extracts of the mid-dorsum and face epidermis confirmed that the tm1a allele results in complete loss of KRT76 protein ( Figure 1M ) . We did detect low levels of immuno-staining in basal keratinocytes in Krt76tm1a/tm1a skin which was slightly reduced compared to wild type mice when imaging was performed using the same confocal settings . Given the unequivocal Western results , one interpretation is that this basal signal is a combination of non-specific cross reactivity and low levels of bona fide expression at this location . However , we cannot exclude the possibility that this change instead relates to alterations in expression of the cross-reacting species that might occur as a consequence of loss of KRT76 . Further studies , perhaps using different antibodies , would be required to confirm this . To examine a developmental role for the gene we profiled protein expression during embryonic and postnatal skin development , showing increasing levels of protein associated with the differentiation of the skin during late embryonic development , followed by a subsequent reduction in expression levels after birth ( Figure 1N ) . Importantly though , low levels of KRT76 were still detectable in the spinous and granular cell layers in intact adult dorsal skin ( Figure 1N ) . As expected , KRT76 protein was absent in Krt76 tm1a/tm1a animals by both immunofluorescence ( compare Figure 1N with Figure 1N′ ) and qRT-PCR ( Figure 1O ) , further validating this allele as a bona fide knockout model . This profile also suggests KRT76 may have a role in the later steps of keratinocyte differentiation , an observation which correlates with the flaky skin phenotypes observed in Krt76tm1a/tm1a neonates ( Figure 1C ) . As they age , Krt76tm1a/tm1a mice develop spontaneous wounds that fail to heal , especially on the dorsal skin around sites of active grooming ( Figure 2A , see arrow head ) . Histological examination showed no obvious phenotypic change in young Krt76tm1a/tm1a mice prior to significant wound acquisition ( which we refer to as the “early” phenotype ) , but large scabs , immune dermal infiltrates , extreme IFE thickening ( Figure 2B ) and hyperpigmentation in the dermis and epidermis develop over time ( arrrowheads , Figure 2B ) . Phospho-histone H3 staining demonstrated a hyperproliferative response in these mice ( Figure 2C , D ) . The morbidity associated with loss of KRT76 is such that animals rarely survive beyond 12 weeks of age . To assess whether cutaneous bacterial infection of these spontaneous wounds may exacerbate morbidity , we treated Krt76tm1a/tm1a mice with a broad spectrum antibiotic ( Baytril ) and observed a considerable improvement in lifespan ( median survival = 70 days versus 32 ( p<0 . 04 ) ) ( Figure 2E ) . The wounding phenotypes associated with Krt76tm1a/tm1a mice led us to examine whether KRT76 was directly involved in the healing of induced wounds . As a first step in addressing this question we sampled dorsal skin from Krt76tm1a/+ and WT mice 1 , 3 , 5 , 7 , and 10 days after wounding by punch biopsy to examine gene expression . Immunofluorescence staining for KRT76 showed an up-regulation of KRT76 protein in the healing wound 5 days after injury ( Figure 2F ) . This was confirmed by LacZ staining in Krt76tm1a/+ wound sections ( Figure 2G ) . Expression profiling by qRT-PCR in WT mice confirmed Krt76 mRNA upregulation in response to wounding , with a profile slightly delayed in comparison to the “classical” wounding keratins Krt6b and Krt16 ( Figure 2H ) . Similar punch biopsy experiments in the dorsal epidermis in Krt76tm1a/tm1a mice resulted in a significant impairment in wound closure at day 3 and day 5 , correlating with the peak of Krt76 expression in the wound ( Figure 2I ) . These observations indicate that that KRT76 is normally upregulated in response to skin damage and is required to facilitate wound healing during the latter phases of this process . We next examined whether the skin of Krt76tm1a/tm1a mice underwent a normal program of differentiation . The basal keratin marker , Keratin 14 ( KRT14 ) and the hair follicle expression of Keratin 6 ( KRT6 ) [27] were normal in “early” phenotype Krt76tm1a/tm1a dorsal skin but both expanded in the interfollicular epidermis ( IFE ) of “late” phenotype Krt76tm1a/tm1a indicative of a wounding response ( Figure 3A ) . Likewise , the psoriasis and wounding associated factor , Fatty acid binding protein 5 ( FABP5 ) [28] , [29] , showed normal weak suprabasal IFE expression in WT and “early” phenotype Krt76tm1a/tm1a mice which increased dramatically when wounds developed in “late” phenotype Krt76tm1a/tm1a mice ( Figure 3B ) . Keratin 10 ( KRT10 ) , a marker of the stratum spinosum , and Filaggrin ( FLG ) , a marker of the stratum granulosum , were again normal in early phenotype Krt76tm1a/tm1a dorsal skin but expanded upon wounding in late phenotype mice ( Figure 3C , D ) . We also surveyed lipid profiles of the cornified envelope with Nile Red , demonstrating that the deposition of extracellular lipid lamellae were unaffected in mutant animals ( Figure 3E ) . The terminal products of epidermal differentiation , the corneocytes , also appeared to form normally , albeit with a small but significant reduction in surface area which we propose derives from hypercellularity in the epidermis ( Figure 3F ) . While the overall differentiation of keratinocytes in late phenotype Krt76tm1a/tm1a dorsal skin was mostly normal , the hyperplasia , immune infiltrate and IFE expression of KRT6 and FABP5 , were reminiscent of the hyperproliferative skin disorder , psoriasis [30] , [31] , [32] . We also observed enlargement of sebaceous glands shown histologically in Figure 2B and further indicated by sebocyte markers , FABP5 and FASN [33] , [34] ( Figure 3B , G ) . Hyperpigmentation was also analysed using an MELAN-A ( MEL-A ) antibody which revealed dermal melanocytes were abnormally increased in density in the dermis and pigment increased in late phenotype Krt76tm1a/tm1a epidermis ( Figure 3H ) . Their location concurred with the increased incidence of pigment detected in H&E sections [35] ( Fig . 2B , upper arrowhead ) . The progressive deterioration of the skin in these mice led us to examine whether the barrier function and integrity of the skin was compromised as a result of loss of KRT76 function . Dorsal skin from 3 week old mice ( without overt wounding ) were subjected to toluidine blue dye exclusion tests and no dye penetrance was observed indicating an intact outside to inside barrier ( Figure 3I ) . Unlike other models of intermediate filament dysfunction , we observed no evidence of cell fragility and intraepidermal cell breakages by histology . This was confirmed using tape stripping assays , which showed no increased susceptibility to dye uptake ( Figure S1A ) and similar yields of corneocytes in tape stripping assays ( Figure S1B ) . To further confirm that the phenotypes we observed were representative of a null allele , and to confirm the phenotype we observed was driven by gene deletion in the epidermis and not in another organ , we generated a conditional KRT76 allele by crossing these mice with a flippase expressing line to remove the LacZ and NeoR cassettes; thereby generating a Krt76tm1c allele ( Figure 4A ) . Mice homozygous for Krt76tm1c were functionally and phenotypically wild type . This allele was then manipulated to achieve gene deletion by crossing to Cre-driver strains ( Figure 4A and Protocol S1 ) . Global gene inactivation using CMV-Cre recapitulated the gene trap phenotype , resulting in early postnatal lethality . Temporally controlled Krt76 deletion specifically in the epidermis was achieved by topical application of 4-hydroxytamoxifen ( 4OHT ) to the dorsal skin of 8 weeks old Krt76tm1c/tm1c mice carrying a K14-CreER transgene . These animals ( Krt76tm1d/tm1d ) showed regions of IFE hyperplasia and wounding after 3 weeks of treatment ( Figure 4B ) which was consistent with KRT76 deletion in these areas ( Figure 4C , see granular layer absence indicated by arrowhead ) . As with Krt76tm1a/tm1a mice , hyperproliferation was increased in conditional knockouts ( Figure 4D ) as well as up-regulation of KRT14 , KRT6 and FABP5 IFE expression ( Figure 4E , F ) . Both KRT10 and FLG cell layers appeared to differentiate in normal sequence and showed wound related expansion ( Figure 4G , H ) . The sebaceous glands were again enlarged , as shown by both FABP5 and FASN staining ( Figure 4F , I ) and like genetrap Krt76tm1a/tm1a mice , an increase in Melanin-A reactivity was seen ( Figure 4J ) . Taken together these experiments confirm that the phenotypes we observe in these mice are due to epidermal specific knockout of KRT76 . Hyper-proliferation , induction of wounding keratins , unresolved wounds , and follicular dysmorphology are phenotypes associated with a loss of barrier function . Neonatal barrier function in dorsal skin was examined using a transepidermal water loss ( TEWL ) assay and identified a significant defect in the cutaneous barrier in Krt76tm1a/tm1a pups compared to their control littermates ( Figure 5A ) . Importantly , this dorsal skin defect ( at P3 ) was apparent before obvious skin wound lesions develop . As our previous phenotypic characterisation indicated this barrier function breakdown was unlikely to be linked to overt defects in cell stability , epidermal stratification , lipid deposition or terminal differentiation we examined tight junctions ( TJ ) . Loss of TJ functionality can result in a compromised epidermal barrier independent of defects in lipid deposition or keratinocyte differentiation [16] , [17] . Furthermore , alterations in TJ proteins are an early event in psoriasis [20] , a disease with phenotypes that parallel some of those evident in Krt76tm1a/tm1a and Krt76tm1d/tm1d mice . To investigate TJ integrity we subcutaneously injected P3 mouse paw pads with membrane impermeable Sulfo-NHS-Biotin and tracked its diffusion using streptavidin immunohistochemistry . In WT epidermis , the diffusion of this high molecular weight compound was restricted before the interface of the granular and cornified layers , defined by FLG expression ( Figure 5B ) , but in Krt76tm1a/tm1a littermates the tracer was detected within the cornified layer ( Figure 5B , see arrowhead ) . Co-staining with a cell surface marker ( CLDN1 ) showed regions of distal dye exclusion in wild type animals ( Figure 5C , see region defined by arrowheads ) , which were absent in mutant mice , further indicating that TJ function in these animals was disrupted ( Figure 5C ) . The ultrastructure of TJ's in P3 paw pad was grossly normal ( e . g . kissing points ) and their number and position were comparable to their WT and heterozygote littermates ( Figure S1C ) . Desmosomes also appeared normal ( Figure S1C ) . In assessing the diffusion of the biotin tracer in paw pad skin we noted that CLDN1 exhibited broader margins at the cell periphery and acquired a partial ( albeit weak ) nuclear localization ( Figure 5C ) . This altered distribution was also observed and quantified in samples stained with CLDN1 , DAPI and E-cadherin ( Figure 5D , E , F ) , which confirmed the inward shift and partial nuclear localisation . While CLDN1 is typically a cytoplasmic protein , nuclear redistribution of CLDN1 has been previously reported [36] . Dorsal skin from young animals taken prior to the development of wounding phenotypes also exhibits mislocalisation of membranous CLDN1 ( Figure 5G ) and this was further exacerbated when wounds formed ( Figure 5H ) , although CLDN1 in the nucleus was not evident at this anatomical site ( Figure 5I ) . Mislocalisation was also confirmed in Krt76tm1d/tm1d samples ( Figure 5J , K ) . No difference in total CLDN1 protein levels were observed in mutant skin relative to ECAD ( Figure 5I ) nor was there a difference in Cldn1 mRNA expression ( Figure S1D ) . This data collectively suggests KRT76 is required to correctly position CLDN1 . Analysis of other TJ components ZO-1 and OCLN confirmed that the mislocalisation was specific to CLDN1 ( Figure S2 and S3 ) . In conclusion , our observations using several different experimental approaches indicate that KRT76 is required for normal TJ composition and in particular , the correct membrane localization of CLDN1 . Given that KRT76 is required for normal CLDN1 localization we next assessed a possible physical association between the proteins . Although KRT76 antibodies proved unsuitable for co-immunoprecipitation experiments , we were able to express the tail domain of the protein and conjugate this to nickel magnetic beads . Paw pad lysates were then applied to the beads and interacting proteins eluted . Using this approach we were able to identify a specific interaction between the tail domain of KRT76 and endogenous CLDN1 ( 23 kDa ) and a second higher molecular weight species ( ∼50 kDa ) which may represent previously reported CLDN1 dimers [37] , [38] . No such interactions were observed with the HIS-tag control protein ( Figure 6A ) . These bands were absent from samples containing bound HIS-tail domain protein not incubated with paw skin extracts . This assay thereby shows that KRT76 can physically complex with CLDN1 although we cannot determine if this interaction is direct or indirect . The available reagents meant that performing the reverse reaction ( pull-down on CLDN1 ) was impossible in the mouse , so we instead employed the human A549 adenocarcinomic alveolar basal epithelial cell line which we determined to endogenously express both proteins ( Figure 6B ) . Using these cells we were able to co-immunoprecipitate KRT76 with CLDN1 . Furthermore , ZO-1 ( another TJ component ) did not form part of this interaction complex , indicating the interaction between KRT76 and CLDN1 is specific amongst TJ components ( Figure 6B ) . CLDN1 and KRT76 were also observed to co-localise in punctate structures within the cytoplasm of A549 cells ( Figure 6C , see arrowheads ) . These interaction and co-localization assays confirm a physical complex between CLDN1 and KRT76 which we propose is important for mediating the barrier dysfunction and wound healing phenotypes of the KRT76 knockout mouse .
The keratins are classically regarded as structural proteins whose role is to form the fabric of the cytoskeleton and to stabilise epithelial cells . However , this somewhat simplistic view has increasingly been challenged by the description of their specialised and dynamic functions in a number of cellular and developmental contexts . The keratins are the most diverse class of intermediate filament proteins and in many cases their functions are poorly defined . In this study we describe the characterisation of KRT76 , one of the least understood of the protein family , delineating its essential role in the maintenance of the integrity of the skin . Under resting conditions , Krt76 is expressed at its highest levels in the paw , oral epithelium and vagina , localising to the granular layer . It is also expressed in the dorsal epithelium , particularly during the late stages of embryonic development . Wounding induces Krt76 expression , although the profile of this induction is distinct from other wounding keratins like Krt6 and Krt16 . To examine the functional relevance of this expression and its role in epidermal homeostasis we inactivated the gene in mice globally and in a skin specific manner . Loss of Krt76 results in the rapid appearance of extensive non-healing wounds ( especially at sites of active grooming ) , and the subsequent infection of these lesions contributes significantly to morbidity and mortality in the mice . Unlike other knockout models of structural keratins we failed to observe cytolysis and/or blistering in the skin . Instead we observed a relatively unperturbed program of keratinocyte differentiation although this gives way to a phenotype of hyperproliferation as the phenotype of the animals worsens . What triggers this change remains to be determined , however the frequency of wounds around active grooming sites suggests that KRT76 deletion may impair the ability of the skin to recover from physical insults normally experienced in the life of the mouse . This theory is supported by the demonstration that induced wounds in the skin of these mice , administered prior to the accumulation of significant cutaneous damage , failed to heal normally . As well as the progressive wounding phenotype observed in these mice , we also noted cellular changes which were consistent with defects in the barrier function of the epidermis . This was confirmed using trans-epidermal water loss assays in neonatal animals . We were unable to establish a role for defective keratinocyte stability or termination in driving this defect , nor was lipid transport affected in the mice to any appreciable level . Instead , we observed the specific mislocalisation of the TJ component CLDN1 , even in newborn mice and in animals without overt or severe cutaneous defects . Indeed previous reports have shown that even significant hyperproliferation induced by two step carcinogenesis treatments is unable to elicit similar changes [39] . Although TJs appeared normal at an ultra-structural level , their reduced capacity to limit the movement of molecules between differentiating keratinocytes in our mice suggests that they were functioning abnormally . Importantly mislocalisation was not observed for other structural elements of the TJ . It is therefore notable that the phenotype of the Krt76 KO mice is strikingly similar to animals carrying homozygous mutations in Cldn1 [17] . In both cases , barrier function defects are detectable by biotin tracer and TEWL assays ( but not by dye exclusion ) , and both have apparently normal formation of TJ structures as assessed by EM . Overall , the phenotypes of Krt76 null mice are somewhat milder than their CLDN1 counterparts , suggesting that despite loss of KRT76 , some CLDN1 can still contribute to partial TJ function . By studying both skin extracts and cell lines endogenously expressing both CLDN1 and KRT76 we were able to demonstrate a physical association between these proteins , mediated by the tail domain of the latter . At present we do not know whether this interaction is direct , or whether the proteins exist in a larger complex . In either case , the loss of KRT76 is clearly required for normal tight junction function and for CLDN1 localisation . Although links between the tight junction and the cytoskeleton have been described for actin , this is the first report detailing an interaction with the keratin intermediate filaments . In summary , we believe that the KRT76 protein represents a new and essential protein required for maintaining epidermal integrity . Its expression during fetal development and during wound healing suggests it is required to establish and/or stabilise the development of TJs in differentiating keratinocytes , specifically through mediating the correct localisation off CLDN1 to these structures . Deletion of the protein leads to defects in TJ function that are at least in part associated with the development of progressively worsening wounds . Whether this severe later phenotype , which ultimately leads to the death of the animals , reflects a separate , non-TJ , role for the protein in wound repair is unclear . Mislocalization of CLDN1 is a feature of a number of cutaneous diseases such as psoriasis [40] , and in a number of cancers [36] , [41] , [42] . KRT76 depletion has also been linked with human oral carcinomas and premaligant epidermal changes [43] . It will therefore be interesting to determine the extent to which this new cytoskeletal-TJ interface between CLDN1 and KRT76 interaction plays a role in the development or progression of these diseases .
Animal models were maintained under the auspices of ethics applications to Monash University and subject to the conditions of the Australian Bureau of Animal Welfare . Krt76tm1a/tm1a mice were generated in the Mouse Genetics Programme at the Wellcome Trust Sanger Institute [24] . Animals were bred and maintained on a mixed background of C57BL/6JTyrcBrd; C57BL/6N . The Krt76tm1a/tm1a characterisation data presented is available at www . mousephenotype . org . Targeting vector information is available at http://www . mousephenotype . org/martsearch_ikmc_project/martsearch/ikmc_project/38047 . Flip recombinase ( Flipper ) mice [44] , K14-CreER mice [45] and CMV-Cre mice [46] have been described previously . 1 . 5 mg of 4-hydrotamoxifen ( H6278 , Sigma-Aldrich ) was applied to a shaved region of lower back skin in 100 µl of acetone every second day for 21 days before mice were harvested for analysis . The PCR conditions were set for amplification of small PCR fragments only . Details of primer sequences , reaction composition and cycling profile are provided in Protocol S1 . Staining for LacZ expression was performed as previously described [47] on frozen sections and counterstained with Nile Red . Immunofluorescence experiments were performed after citrate based antigen retrieval . Primary antibodies were ZO-1 ( Invitrogen cat# 339100 ) , Occludin ( BD Transduction cat# 611090 ) , Claudin-1 ( ABCAM cat# ab15098 ) , cytokeratin14 - LLOO2 ( ABCAM cat # ab7800 ) , keratin10 ( Covance PRB-159P ) , keratin6 ( Covance cat # PRB-169P ) , Ecadherin ( Life Technologies , 13-1900 ) , phospho-histone H3 ( Cell Signalling , #9708 ) , PCNA ( Santa Cruz Biotechnology sc-9857 ) , CLDN1 ( Santa Cruz Biotechnology , sc-81796 ) , Keratin 76 ( Sigma-Aldrich HPA019696 ) and Keratin 76 ( Sigma-Aldrich HPA019656 ) , Filaggrin ( FLG- Covance PRB-417P ) , FASN ( Santa Cruz Biotechnology , sc-48357 ) , and Melan-A ( MEL-A , Santa Cruz Biotechnology , sc-20032 ) . All secondary antibodies were AlexaFluor conjugated ( Invitrogen ) . Sections were imaged using Lecia SP5 5 Channel , Olympus FV500 confocal microscopes or Aperio slide scanners . Bright field images of wound healing experiments were taken with Olympus dotslide brightfield microscope . Images for CLE assays were acquired with Olympus CKX41 and exported to FIJI software [48] for cell analysis . Mice ( age-matched males; 6 weeks ) were isofluorane-anaesthetized and 2 full-thickness excisional wounds were made with a 5 mm biopsy punch ( Livingstone International ) . Wound tissue was harvested with an 8 mm biopsy punch . One µg of DNase ( Ambion ) treated RNA was used for cDNA synthesis ( SuperScriptVILO ) . Multiplex quantitative PCR was performed using Taqman probes for Gapdh ( VIC-primer limited labelled , cat# 4448484 ) and Krt76 ( FAM labelled , Cat#4351372 ) with TaqMan Fast Advanced Master Mix Protocol ( PN 4444605B ) . Gene specific primers were designed and used in conjunction with SYBR Green PCR Master Mix ( Applied Biosystems ) for the detection and quantification of Claudin1 ( 5′-ATTTCAGGTCTGGCGACATT-3′ fwd , 5′-ACACCTCCCAGAAGGCAGAG-3- rev ) , Krt6b ( 5′-CAGACCCCAGATACCCTGGC-3′ fwd , 5′-GAGCAGAGATGGCATCATGTGAGCAACAGG-3′ rev ) , Krt16 ( 5′- AACAGCCTAGAAGAGACCAAAGGC-3′ fwd , 5′-GGTAGGGGAGACAGATGGGGAATGCGC-3′ rev ) mRNA as compared to Gapdh ( 5′- CTGCACCACCAACTGCTTAG-3′ fwd , 5′- GTCTTCTGGGTGGCAGTGAT-3′ rev ) . All fractionation experiments were performed on dorsal epidermis of P3 animals . Pups were euthanized ( Pentobarbital ) and skin was removed as previously described [49] . Skins were floated on 2 . 3 U/mL Dispase ( Life Technologies ) in PBS overnight at 4°C . The epidermis was separated from the dermis and protein fractionated using a Qproteome Cell Compartment Kit ( Qiagen ) . Western blots for E-cadherin ( Life Technologies ) and total Histone H3 ( Cell Signaling ) were performed on the nuclear and membrane fractions . Image Quant software was used to calculate densitometry and quantify protein levels . Claudin-1 levels in the membrane fraction were normalized to E-cadherin for each sample . TJ permeability assays were undertaken as previously described [17] , [50] . Briefly , a solution of 10 mg/ml EZ-Link Sulfo-NHS-LC-Biotin ( Pierce ) in PBS containing 1 mM CaCl2 was injected into the paw pads of P3 pups . Paw pads were incubated at room temperature for 30 minutes prior to frozen sectioning and IHC with conjugated Streptavidin Alexafluor 594 ( Life Technologies , S-11227 ) . Tissue was fixed in Karnovsky's fixative ( 2% paraformaldehyde , 2 . 5% glutaraldehyde in 0 . 1 M Cacodylate buffer ) for 2 hours . Then washed in 3×10 min changes of 0 . 1 M Cacodylate buffer . Post-fixation was with 2% osmium tetroxide in 0 . 1 M Cacodylate buffer followed by dehydration through a graded series of alcohols , two acetone rinses and embedding in Spurrs resin . 80 nm sections were cut with a diamond knife ( Diatome , Switzerland ) on an Ultracut-S ultramicrotome ( Leica , Mannheim , Germany ) and contrasted with uranyl acetate and lead citrate . Images were captured with a Megaview II cooled CCD camera ( Soft Imaging Solutions , Olympus , Australia ) in a JEOL 1011 transmission electron microscope . Recombinant HIS-tagged proteins were produced by IPTG induction ( 0 . 4 mM ) of T7 Express lysY/Iq Competent E . coli ( New England Biolabs C3013I ) transformed with HIS-tag expressing control vector , pET-30a+ ( Novagen ) or HIS-tagged KRT76 domains in pDEST17 gateway backbone ( Life Technologies ) , grown for 6–8 hours at 37°C in low salt LB , supplemented with 100 µg/ml ampicillin or 50 µg/mL kanamycin ( as required ) . Recombinant protein was purified using 0 . 1 ml per 1 ml of culture of PopCulture lysis reagent ( Novagen ) , 1 µl per mL of culture of 40 U/ml of Lysonase bioprocessing reagent ( Novagen ) , protease inhibitors ( Sigma P8849 ) , and His-Mag beads ( Novagen ) according to manufacturer's protocols . Bound recombinant HIS and HIS-KRT76 protein were washed and stored at 4°C as a 1∶2 resin slurry in Tris-saline pH 7 . 4 containing protease inhibitors . Paw pad skin of adult was collected in RIPA lysis buffer and incubated with HIS or HIS-KRT76 overnight at 4°C . HisMag bead-bound HIS and HIS-KRT76 + lysates were then washed four times in Tris-saline pH 7 . 4 including 1% Triton X-100 and immunoblotted for mCLDN1 ( Santa Cruz Biotechnology , SC-81796 ) and the HIS tag ( Sigma-Aldrich , clone HIS-1 ) . A549 cells ( ATCC CCL-185 ) were cultured in low-glucose DMEM including 10% FCS , Penicillin Streptomycin and L-glutamine . For CO-IP , cells at confluence were scraped and lysed in 1% Triton X-100 in 1xTBS with Roche complete protease inhibitor tablet , extracted for 2 hrs at 4°C then supernatant collected . The supernatant was applied to binding columns prepared using the Pierce Crosslink IP Kit and CO-IP performed as per manufacturers protocol . Bound fractions were washed 3 times in lysis buffer before elution and standard WB analysis . For Immunofluorescence , 2×105 cells were seeded on Collagen type 1 coated glass coverslips in 6 well plate format and processed as previously described [51] . E18 . 5 embryos or 3 week old dorsal skin were collected and transferred through a Methanol gradient with emersion for 1 minute each: 25% methanol in water , 50% methanol in water , 75% methanol in water , 100% methanol , 75% methanol in water , 50% methanol in water , 25%methanol in water and equilibrated in PBS . All reagents were chilled . Tissue was then exposed to 0 . 1% Toluidine Blue solution in water for 2 minutes and destained in 1xPBS pH7 . 4 . For tape stripping , clipped dorsal skins were first tape stripped twelve times with adhesive tape before tissue collection . Analysis of the size of corneocytes in the cornified lipid envelope ( CLE ) assay was performed as previously published [52] . Statistical analysis was performed using unpaired students test , values of p<0 . 05 were deemed significant . A minimum of 3 mice were analysed per condition unless otherwise stated . In graphs , error bars represent Standard Error of the Mean ( S . E . M ) . | The generation of knockout mice is a central approach to studying gene function . We have examined the consequences of the germ line inactivation of Keratin 76 in mice and in doing so we reveal a previously undescribed mechanism by which keratin intermediate filaments regulate cellular interactions and tissue homeostasis . Our study supports an emerging body of evidence which challenges the classical view of the keratin intermediate filaments as simple structural proteins , highlighting Krt76 as a gene whose function is indispensable for barrier function and skin wound repair as a result of its novel interaction with tight junction complexes . This study identifies a previously unknown and critical link between intermediate filaments and tight junctions where intermediate filament dysfunction influences skin disease . | [
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"biologic... | 2014 | Keratin 76 Is Required for Tight Junction Function and Maintenance of the Skin Barrier |
The only oral drug available for the treatment of leishmaniasis is miltefosine , described and approved for visceral leishmaniasis in India . Miltefosine is under evaluation for the treatment of cutaneous leishmaniasis in the Americas although its efficacy for the treatment of human visceral leishmaniasis caused by Leishmania infantum chagasi has not been described . Drug efficacy for visceral leishmaniasis is ideally tested in hamsters , an experimental model that mimics human disease . Luciferase has been validated as a quantitative tool for the determination of parasite burden in experimental leishmaniasis . However , there are no reports of luciferase detection in the model of progressive visceral leishmaniasis in hamsters . Therefore , the aims of this study were to generate recombinant Leishmania infantum chagasi expressing the luciferase gene ( Lc-LUC ) , characterize the biological properties of this transgenic line as compared with the wild-type parasites and evaluate miltefosine effectiveness in Lc-LUC infected hamsters . A transgenic line containing a luciferase encoding gene integrated into the ribosomal DNA locus was obtained and shown to produce bioluminescence which correlated with the number of parasites . Lc-LUC growth curves and susceptibility to pentavalent antimony and miltefosine in vitro were indistinguishable from the wild-type parasites . The effectiveness of pentavalent antimony was evaluated in Lc-LUC infected hamsters through bioimaging and determination of Leishman Donovan Units . Both methods showed concordant results . Miltefosine was effective in the treatment of Lc-LUC-infected hamsters , as demonstrated by the reduction in parasite burden in a dose-dependent manner and by prolongation of animal survival . Luciferase expressing parasites are a reliable alternative for parasite burden quantification in hamsters with advantages such as the possibility of estimating parasite load before drug treatment and therefore allowing distribution of animals in groups with equivalent mean parasite burden . Miltefosine was effective in vivo in an L . infantum chagasi experimental model of infection .
Visceral leishmaniasis ( VL ) is a neglected vector borne disease that manifests with fever , fatigue , weight loss , anemia and hepatosplenomegaly in humans . Untreated , VL is almost 100% fatal [1] . VL is transmitted by phlebotomine sand flies and is caused by Leishmania infantum and Leishmania donovani . L . infantum chagasi [2] is the etiological agent of VL in Latin America . L . infantum is found in the Mediterranean Basin , while L . donovani is prevalent in the Indian subcontinent , South Asia and East Africa [1] . Leishmaniasis chemotherapy is currently a major issue in disease management and there is pressing need for new drugs and/or new treatment regimes . Pentavalent antimony ( SbV ) is the first line drug for VL treatment in Brazil and in many countries , in spite of its high toxicity . Due to parasite resistance , the use of antimonials was interrupted in some regions of India and replaced by miltefosine , the first and only oral agent available for leishmaniasis treatment [3] . Phase 4 studies demonstrated high success rates in the treatment of VL with miltefosine in that country [4] . Nonetheless , susceptibility to miltefosine is variable amongst Leishmania species and its efficacy in New World Leishmania infections is still a matter of investigation [5–8] . Miltefosine was employed successfully in L . infantum-infected hamsters in an early curative model [9] but , to the best of our knowledge , it has not been evaluated in a chronic VL model of L . infantum chagasi infection . BALB/c mice and hamsters are the most commonly used VL animal models for drug and vaccine testing . L . donovani infection in mice results in early parasite replication followed by immunological control and subclinical infection , but it does not reflect the progressive disease observed in human VL . Infections of the Syrian golden hamster ( Mesocricetus auratus ) , on the other hand , lead to hepato and splenomegaly , relentless increase in visceral parasite burden , progressive cachexia and , ultimately , death . These clinical and pathological findings are similar to the picture found in human and canine VL . However , research performed on hamsters is still limited due to the lack of reagents such as antibodies against cell markers and cytokines [10 , 11] . Quantification of parasite burden in hamsters experimentally infected with VL causative species is generally obtained by classical methods , such as limiting dilution and/or microscopic counting of amastigotes in imprinting of infected organs , through the determination of Leishman Donovan units ( LDU ) . These methods are laborious and time consuming . Attempts to overcome these difficulties include the determination of parasite burden based on reverse transcription and real-time PCR , for example [12] , but this alternative is not devoid of difficulties . Therefore , the development of a technique allowing easy quantification of parasites in various tissues would be very useful . Luciferase has been validated as a quantitative tool for the determination of parasite burden in experimental cutaneous leishmaniasis in vivo and ex vivo in tissue samples from Leishmania amazonensis-infected mice [13] . Luciferase transfected parasites were also used to quantify L . infantum in BALB/c infected mice [14] . However , there are no reports on quantitative determination of parasite burden through luciferase-detection in the model of progressive VL in hamsters . Therefore , in this work we aimed at developing a transgenic line of L . infantum chagasi expressing luciferase , testing its application as a tool to evaluate drug efficacy in VL and evaluating miltefosine’s efficacy in L . infantum chagasi infections .
Animal experiments were approved by the Ethics Committee for Animal Experimentation ( Protocol CPE-IMT 2012/145 ) of the Instituto de Medicina Tropical of the University of São Paulo . The research adhered to the Brazilian Guidelines for Care and Utilization of Animals from the Conselho Nacional de Controle e Experimentacão Animal ( CONCEA ) . Wild-type Leishmania ( Leishmania ) infantum chagasi ( Lc-WT ) ( MHOM/BR/1972/LD ) promastigotes were grown in 25 cm2 tissue culture flasks containing M199 medium ( Sigma-Aldrich , St . Louis , MO , USA ) supplemented with 10% heat-inactivated fetal calf serum ( FCS; Gibco Invitrogen Corporation , NY , USA ) , 0 . 25% hemin ( Sigma-Aldrich ) and 2% sterile male human urine at 25°C . Parasites were maintained in male golden hamsters ( Mesocricetus auratus ) and infections were performed with amastigotes obtained from the spleen of infected animals . Briefly , hamsters infected with L . infantum chagasi were euthanized no later than 60–70 days post-infection . The spleens were removed and triturated in PBS using a glass tissue homogenizer . Spleen homogenates were used to infect young male hamsters and this procedure was performed once a month to continuously maintain the strain in animals . Spleen smears were prepared on microscopic slides and parasite burden was quantified by optical microscopy as described in the section “Quantification of spleen parasite burden by optical microscopy” . The modified Photinus pyralis luciferase open reading frame ( ORF ) from plasmid pLUC2 [15] was amplified with primers Luc2For ( 5’-GCGGGATCCATGGAAGATGCCAAAAACATTAAG-3’ ) and Luc2Rev ( 5’-CACGCGCATACATTCACGGCGTTACACGGCGATCTTGCCGC-3’ ) . A fragment of the Leishmania enrietti α-tubulin 3’ untranslated region ( 3’ UTR ) was amplified from plasmid pSPαHYGα [16] with primers αTubForLuc2 ( 5’-GCGGCAAGATCGCCGTGTAACGCCGTGAATGTATGCGCGTG-3’ ) and αTubRevBamHI ( 5’-GCGGGATCCGGGGAGAGGGATGAGGGGT-3’ ) . To allow for constitutive expression of luciferase in Leishmania , the 3’ α-tubulin UTR fragment was linked downstream to the luciferase ORF by overlap PCR and cloned into the Bam HI restriction site of the vector pSSUint [17] . This vector contains the sequence encoding the hygromycin phosphotransferase gene as the resistance marker and fragments of the Leishmania small subunit ( SSU ) ribosomal DNA ( rDNA ) at the cassette extremities in order to promote homologous recombination . The resulting plasmid ( pSSUint-Luc2 ) was sequenced to confirm the integrity of the insert . The linear cassette containing the sequences of interest was purified from agarose gels after digesting pSSUint-Luc2 with Pac I and Pme I . L . infantum chagasi promastigotes were transfected with 5 μg of the linear digested DNA construct as described [18] . Briefly , 4×107 promastigotes from log-phase cultures were transfected by electroporation at 2 , 250 V/cm and 500 μF . Cells were then transferred to 10 mL of M199 medium and incubated for 24 h at 25°C . After 24 hours , 32 μg/mL hygromycin was added for selection of mutants . After four passages in liquid medium with hygromycin , mutants were plated on semi-solid M199 medium supplemented with 1 . 2 μg/mL biopterin , 1% agar , 2% urine and 32 μg/mL hygromycin for clone selection [18] . Integration into the SSU rDNA was confirmed through PCR amplification with primers complementary to sequences inside and outside the transfected cassette . Primers S1 ( 5’-GATCTGGTTGATTCTGCCAG-3’ ) and S4 ( 5’-GATCCAGCTGCAGGTTCACC-3’ ) [19] anneal to the SSU rDNA sequence flanking the insertion sites and primers Luc2For , Luc2Rev and Luc465–484 ( 5’-GACCGACTACCAGGGCTTCC-3’ ) are complementary to the cassette SSU:Luc2αTub . The vectors pSSU-int and pSPαHYGα were kindly provided by Dr . Tony Aebischer ( Robert Koch Institute , Berlin , Germany ) and Dr . Marc Oullette ( Universite Laval , Quebec , Canada ) . Promastigotes of Lc-LUC were harvested at the late log-phase of growth , washed twice and resuspended in phosphate-buffered saline ( PBS ) ( pH 7 . 2 ) . Parasites were serially diluted and the luciferase assay was performed with One Glo Luciferase Assay System ( Promega Corporation ) according to the manufacturer’s instructions . Briefly , one volume of reagent was added to 5 volumes of parasite suspension and luminescence was registered in a microplate reader ( POLARstar Omega , BMG Labtech , Ortenberg , Germany ) . Each point was tested in triplicate in at least two independent experiments . Resident macrophages were collected from the peritoneal cavity of BALB/c mice by washing with RPMI-1640 medium ( Gibco , Invitrogen Corporation ) supplemented with 10% FCS and added to 24-wells plates in round glass cover slips at 4×105/well . Plates were incubated in 5% CO2 for 24 h at 37°C . L . Infantum chagasi amastigotes extracted from the spleen of infected golden hamsters were added to macrophages at a ratio of 10:1 ( amastigotes:macrophage ) . After 24 h , extracellular parasites were removed by washing . Increasing concentrations of pentavalent antimony ( N-Methylglucamine antimonate—Glucantime , Sanofi-Aventis , Brazil ) and miltefosine ( Sigma-Aldrich ) were added to infected macrophages and treatment was performed for 120 or 72 hours , respectively . Stock solutions of miltefosine ( 20 mM ) were prepared in sterile distilled water . Glucantime was kindly donated by the Brazilian Ministry of Health . Both drugs were freshly diluted to the final concentration in RPMI-1640 medium immediately before the experiment . Infected macrophages incubated without drugs were used as controls . After the end of drug treatment , cells were fixed in methanol and stained with the Romanovsky type Instant Prov kit ( Newprov , Pinhais , PR , Brazil ) . The percentage of infected cells was determined by counting 200 macrophages in each of the replicates . EC50 values were determined from sigmoidal regression of the concentration-response curves using GraphPad Prism 5 software . Each point was tested in duplicate and experiments were performed three times . Male hamsters ( 3 to 5 weeks-old ) were obtained from the Instituto de Medicina Tropical de São Paulo of the University of São Paulo , kept in cages with absorbent material and received unlimited food and water . To establish the infection in hamsters with transgenic parasites , animals were infected with 1 . 5×109 promastigotes from stationary phase cultures via the intraperitoneal route . In the subsequent experiments , animals were intraperitoneally infected with 107 Lc-LUC amastigotes obtained from the spleen of infected hamsters . Thirty-five days post-infection , parasite burden was quantified in live animals through luciferase detection . Animals were distributed in experimental groups according to the parasite load . At day 40 post-infection , treatment was initiated . Animals received SbV or miltefosine for 15 or 10 consecutive days , respectively . Parasite burden was quantified at day 56 through luciferase detection and/or microscopic counting of spleen smears . In the first experiment , Lc-LUC infected hamsters were assigned to groups ( n = 6 ) that were either left untreated or received 50 mg/kg/day SbV ( which corresponds to 85 . 18 mg/kg/day of Glucantime ) intraperitoneally in 200 μL final volume of PBS . The second experiment was performed with groups of Lc-LUC infected hamsters ( n = 5 ) that were left untreated or received 50 mg/kg/day SbV as described above or 20 mg/kg/day miltefosine by oral gavage in 200 μL final volume . The third experiment was performed with 4 groups ( n = 4 or 5 ) that received 0 , 5 , 10 or 20 mg/kg/day miltefosine by oral gavage as described above . In this experiment , the animals were followed up for 5 months for survival analysis . Lc-LUC light emission in live animals was recorded by bioimaging ( IVIS Spectrum , Caliper Life Sciences ) . Previous to the imaging , each animal received 6 mg VivoGlo Luciferin ( Promega Corporation ) intraperitoneally followed by anesthesia in a 3% isoflurane atmosphere ( Cristália ) . Animals were then transferred to the imaging chamber and kept in a 2 . 5% isoflurane atmosphere . Total photon emission from a defined region of interest ( ROI ) was collected using the high resolution ( medium binning ) mode . The same ROI was applied to all animals . The images were acquired 5 min after luciferin injection . Total photon emission was quantified with Living Image software version 4 . 3 . 1 ( Caliper Life Sciences ) and results were expressed as the number of photons/second/square centimeter/steradian . The photon signal from the abdominal region was presented as a pseudocolor image representing light intensity ( red = most intense and blue = least intense ) and superimposed on the gray scale reference image [13] . The average background signal was estimated in an uninfected animal and was used to correct the bioluminescent emission by subtraction . For in situ imaging , Lc-LUC infected hamsters received 6 mg VivoGlo Luciferin ( Promega Corporation ) intraperitoneally and were anesthetized in a 3% isoflurane atmosphere ( Cristália ) . Animals were euthanized through cervical dislocation , the peritoneal cavity was accessed through the linea alba and the viscera were exposed . Animals were then transferred to the imaging chamber and emitted photons were recorded immediately . Bioluminescent organs were collected and placed in a 24-well plate containing luciferin in PBS and images were acquired immediately . Spleen smears were prepared on microscopic slides , stained with the Instant Prov kit ( Newprov , Pinhais , PR , Brazil ) and examined under optical microscopy to identify Leishmania amastigote forms . Results were expressed as Leishman Donovan Units ( LDU ) corresponding to the number of amastigotes per 1000 nucleated cells multiplied by the organ weight in grams . Data on parasite burden was analyzed for statistical significance by One Way ANOVA , followed by the Tukey post-test . Statistical analyses were performed using GraphPad Prism 5 software . A result was considered significant at p<0 . 05 .
L . infantum chagasi line expressing luciferase ( Lc-LUC ) was obtained by transfection of a cassette containing the luc2P gene flanked by ribosomal DNA sequences which directed homologous recombination into the L . infantum chagasi ribosomal DNA locus ( Fig 1A ) . After clone selection , integration was confirmed by PCR amplification of the flanking regions using primers complementary to sequences outside ( primers S1 and S4 ) and inside the transfected cassette ( primers Luc2For and Luc2Rev ) ( Fig 1A-B ) . Amplification reactions performed on the transgenic line with primers S1/Luc2Rev and Luc465–484/S4 generated 2 . 4 kb and 5 . 5 kb fragments , respectively . The positive amplification of the luc2P gene was demonstrated in the transgenic line by the detection of a 1 . 6 kb amplified fragment . Amplifications with these sets of primers occurred only in the transgenic line and showed the expected size , confirming integration into the SSU rDNA ( Fig 1B ) . Primers S1 and S4 [19] amplified the SSU rDNA in both the wild-type and transgenic line resulting in a 2 . 2 kb fragment . The amplification of a fragment of 6 . 8 kb in size resulting from the rDNA cistron where the integration occurred was not observed , most likely because of the relative abundance of the normal cistron , highly repeated in the genome . Luciferase expression in the transgenic lines was confirmed by light production upon addition of luciferin . A linear correlation between the number of promastigotes and emitted light was found , as shown in Fig 1C , indicating that luciferase activity can be used to assess parasite numbers with confidence . Transgenic ( Lc-LUC ) and wild-type ( Lc-WT ) promastigotes exhibited indistinguishable growth curves , achieving the stationary phase on the fourth day of culture ( S1 Fig ) . In vitro susceptibility of the transgenic line to SbV and miltefosine was evaluated . The calculated EC50 values for Lc-LUC intracellular amastigotes were 111 . 0 μg/mL ( confidence interval 95% = 94 . 6 to 130 . 3 ) and 4 . 4 μM ( confidence interval 95% = 3 . 3 to 5 . 9 ) , for SbV and miltefosine , respectively . The EC50 values for Lc-WT intracellular amastigotes were 110 . 0 μg/mL ( confidence interval 95% = 76 . 68 to 157 . 9 ) and 3 . 88 μM ( confidence interval 95% = 3 . 02 to 4 . 99 ) , for SbV and miltefosine , respectively . These values are in accordance with previously published data on L . infantum chagasi susceptibility to these drugs [7] . The first experimental infection in hamsters was obtained by intraperitoneal injections of Lc-LUC stationary phase promastigotes . Clinical signs of disease were observed 2–4 months post-infection , when amastigotes were recovered from spleen homogenates . This first set of infected animals served as a source of amastigotes which were pooled and used to infect hamsters for the next experiment . Parasite load in the spleen was determined by LDU and the following infections were achieved by intraperitoneal inoculation of 107 amastigotes per animal . After establishing the infection in vivo , we performed a pilot experiment to determine the best conditions for hamster imaging . Different doses of luciferin and various capture times were tested . We found that luminescence could be efficiently visualized after 5–10 minutes of administering 6 mg luciferin per animal . One month after infection , animals showed widespread light emission in the abdominal and/or pelvic region disclosing different patterns of parasite dissemination ( S2 Fig ) . Bioluminescence in situ , in an euthanized animal with the peritoneal region exposed is also shown ( S2 Fig ) . Subsequent dissection allowed observation of bioluminescent parasites in different tissues and organs , such as the epididymis and adipose tissue ( S3 Fig ) . Based on this heterogeneous pattern , we defined that the region of interest ( ROI ) to be used in parasite burden determinations should comprise the whole abdominal and pelvic regions . Treatment of infected animals with SbV was used as a proof-of-concept study to validate this model as a tool to evaluate drug activity in hamsters with VL . Before treatment , parasite load was quantified by bioimaging ( Fig 2A ) . A large dispersion of parasite burden was noted between the animals . Emitted light measurements allowed the allocation of animals into experimental groups ( Fig 2B ) with similar mean radiance ( Fig 2C ) . Treatment was initiated 40 days post-infection and animals received 50 mg/kg/day of SbV for 15 consecutive days . At the end of treatment ( 56 days post-infection ) , parasite burden was quantified by bioimaging ( Fig 2D ) . Thereafter , animals were euthanized and each spleen was used to imprint the organ in glass slides for LDU determination ( Fig 2E ) . SbV treatment resulted in 98% suppression of bioluminescence when compared with untreated animals ( Fig 2D ) . No parasites were detected by microscopic examination in treated animals ( Fig 2E ) . This new experimental model was then used to evaluate the efficacy of miltefosine in Lc-LUC infected hamsters . In this experiment , treatment with miltefosine was compared with SbV . Similarly to the first experiment , parasite load was quantified by bioimaging before treatment initiation ( Fig 3A ) . Animals with parasite loads above or below 4 times the overall average of the whole group were considered outliers and excluded from the experiment . Parasite burden based upon light emission was used to distribute animals evenly between three treatment groups ( Fig 3B ) which therefore had comparable mean levels of infection ( Fig 3C ) . Treatment was initiated 40 days post-infection and animals received 50 mg/kg/day of SbV for 15 consecutive days or 20 mg/kg/day of miltefosine for 10 consecutive days . At the end of treatment ( 56 days post-infection ) , parasite load was quantified by bioimaging ( Fig 3D ) and LDU determination ( Fig 3E ) . According to the bioluminescence quantification ( Fig 3D ) , SbV resulted in 96% suppression of parasite burden when compared with untreated animals , while treatment with miltefosine resulted in 86% suppression . Parasite load determined through LDU indicated absence of detectable parasites in animals treated with both miltefosine and SbV ( Fig 3E ) . There were no significant differences in parasite burden between SbV and miltefosine-treated groups quantified by bioimaging . Having determined that miltefosine was as effective as SbV in the early stage after treatment , we were interested in determining the effective dose and long term effects of miltefosine in L . infantum chagasi infections . As in the previous experiments , groups were based on parasite burden determination before treatment ( S4 Fig ) . Miltefosine doses used were 5 , 10 and 20 mg/kg/day , which resulted in 47 , 63 and 85% reduction in parasite burden compared to the control untreated group , as determined by bioluminescence ( Fig 4A ) . Plotting normalized values on a non-linear regression curve resulted in an ED50 of 6 . 1 mg/kg/day . Bioluminescence imaging of animals from untreated and miltefosine-treated groups at the end of treatment is shown in S5 Fig To assess the long term drug-efficacy , animals were followed-up for 20 weeks ( 18 weeks after treatment interruption ) . The infection led to death in 100% of untreated animals 18 weeks post-infection . Miltefosine-treated animals survived longer as compared with untreated animals in a dose-dependent manner . In the group treated with the highest total dose of miltefosine ( 20 mg/kg/day ) , we observed 100% animal survival 20 weeks post-infection ( Fig 4B ) .
We report here the generation of recombinant luciferase-expressing L . infantum chagasi parasites and their use to quantify parasite load in vivo in infected hamsters . The hamster model is used to study VL because it reproduces the clinical course and pathology of the disease , as seen in humans and dogs [10] . Results presented here contribute to the study of new alternatives for VL treatment through the use of a chronic infection model . Studies to determine drug efficacy in experimental leishmaniasis involve multiple technical difficulties such as the need for a large number of animals which should be maintained for long periods . Quantification of parasite load is commonly done by limiting dilution protocols or by microscopic examination of slides prepared by imprinting of the infected organs . These techniques involve enormous variability , are laborious and time demanding . Furthermore , spread of parasites to an unexpected site of infection may be missed because the infected tissue is not harvested or analyzed . A more efficient method for quantifying parasite load in vivo would help to overcome these drawbacks . Recently , the use of firefly luciferase to detect transgenic Leishmania has provided many advantages over conventional methods . Bioluminescence allows the detection of live parasites and can be performed repeatedly . Quantification of bioluminescence is not only sensitive but also more rapid than culture-based techniques and can be used to monitor the efficacy of antileishmanial drugs in animal models [10 , 13] . Various recombinant parasites carrying a reporter gene as an episomal copy are currently available , as reviewed by [10] . However , for prolonged growth in the absence of drug selection , such as within animal models , quantitation of parasites is more reliable when the gene encoding luciferase is stably integrated into the parasite genome . In fact , when reporters are part of plasmids , the relative output of the reporter may depend on the copy number of the transfected plasmid , which varies from cell to cell , resulting in different levels of expression [20] . In order to circumvent this problem , in the present work , the luc2P gene was integrated in the L . infantum chagasi rDNA locus . As mentioned previously , luminescent parasites can be quantified repeatedly in the same animal . Genetic variability in hamsters is the cause of considerable discrepancy in VL progression , as is the case in humans and dogs . Individual parasite burdens in individual infected animals were used to distribute them between different treatment groups so that the mean parasite burdens were equivalent before treatment initiation . This procedure brings substantial progress in experimentation with hamsters , allowing drug efficacy to be evaluated with greater reliability . We also observed that infected animals showed a heterogeneous pattern of parasite dissemination . This demonstrates the inherent variability of experimentation with hamsters and the possibility of parasite spread to different sites . Based on this , we defined a ROI corresponding to the whole abdominal and pelvic region , including in the analysis an extensive area where parasite could be found . Peritoneum bioluminescence , distinct from spleen bioluminescence , was reported by [14] in luciferase-expressing L . infantum infected BALB/c . The presence of L . infantum in the epididymis of infected hamsters and dogs was also previously reported [21–23] . However , these unusual sites of infection are commonly ignored in drug efficacy assays . It has to be said that the parasite distribution observed herein may be the result of the intraperitoneal infection route used in this study . The choice of this infection route was justified by the difficulty of intravenous access in hamsters , which lack usable tails . While natural infection upon sand fly feeding results in parasite inoculation into dermal tissue and capillary blood the intraperitoneal route may artificially create extensive abdominal dissemination . It remains to be investigated whether intracardiac inoculation would result in less disseminated parasite colonization . We found that transgenic and parental lines presented similar growth rates ( S1 Fig ) and in vitro susceptibility to SbV and miltefosine , with EC50 values in the same range as previously published data [7] . Transfected parasites were not attenuated in vivo compared with the parental strain , leading to animal death 3–5 months post-infection ( Fig 4 ) . We found that SbV was able to reduce parasite load in Lc-LUC infected hamsters to very low levels , as reported previously in infections with the wild type parasites of the same strain [24 , 25] . So , in all aspects evaluated , the luciferase expressing line behaved similarly to the parental parasites . In order to validate the use of bioluminescence as a quantitative tool for parasite load determination , we performed the experimental treatment of Lc-LUC-infected hamsters with antileishmanial drugs and bioluminescence results were compared with the classical method of LDU determination . Interestingly , luciferase detection was more sensitive than LDU: while LDU indicated parasite clearance in hamsters treated with antimony and miltefosine , bioimaging revealed the persistence of parasites in small numbers . We have previously shown that bioluminescence data were in accordance with limiting dilution and clinical parameters evaluated after experimental treatment with amphotericin B when using luminescent L . amazonensis in infected mice [13] . Our present work confirms that bioluminescence is a reliable tool for parasite load determination . Miltefosine was as effective as SbV in the treatment of Lc-LUC infections in hamsters . Data on the efficacy of miltefosine in L . infantum-infected hamsters are limited . In one study [9] , the authors used an early curative model where treatment was initiated 21 days post-infection and parasite burden determined 35 days post-infection . In these settings , milfefosine treatment resulted in 61 and 99% reduction in parasite burden in the spleen when animals were treated with total doses of 100 and 200 mg/kg , respectively . Here , we used a late curative therapeutic approach , as we started treatment 40 days post-infection and parasite burden was estimated 56 days post-infection . We chose the doses of 10 and 20 mg/kg/day ( total dose of 100 and 200 mg/kg ) , as previously reported [9] , and included a lower dose of 5 mg/kg/day , in order to estimate miltefosine’s ED50 . In our model , doses of 5 , 10 and 20 mg/kg/day miltefosine resulted in 47 , 63 and 85% reduction in parasite burden . These differences in parasite reduction could be explained by the fact that we assessed parasite load in an extensive area , corresponding to the abdominal and pelvic region , while the data from literature refers to parasite load in specific organs , determined by LDU [9] . Furthermore , differences were expected due to the dissimilar protocols used in both experiments ( early versus late curative model ) . We showed that miltefosine is effective in the experimental treatment of L . infantum chagasi-infected hamsters , as demonstrated by the reduction in parasite burden in a dose-dependent manner and by prolongation of animal survival . However , we found that , even for the highest dose used , clinical response did not reflect sterile cure . The same is true for antimony-treated hamsters and it may still reflect clinical cure . On the other hand , it is possible that the remaining parasites may lead to disease recurrence . In a study performed with dogs naturally infected with L . infantum chagasi , miltefosine treatment resulted in improvement of clinical symptoms but did not result in parasitological clearance [26] . Additional studies are needed to ascertain if total doses higher than 200 mg/kg could result in parasite clearance in the hamster model . In any case , results shown here indicated that L . infantum chagasi infections are responsive to miltefosine treatment and , at least in the hamster model , resulted in 100% survival in treated animals until 20 weeks post-infection , as opposed to 100% mortality in the control group . Data presented here also indicates that the use of luminescent L . infantum chagasi is a reliable alternative for parasite burden quantification in hamsters . This tool has several advantages such as the possibility of assessing the progress of infection in the same animal and the benefit of estimating parasite load before and after drug treatment . The possibility of distributing animals in equivalent groups is an important advantage , especially when working with heterogenic animals , as is the case of hamsters . This model may be useful for the study of pathogenesis and healing processes in hamsters , allowing the dissection of parasite persistence . | Studies to determine drug efficacy in experimental models of leishmaniasis involve several difficulties . Parasite quantification in tissues is generally done by techniques that are laborious and time consuming , such as limiting dilution and microscopic counting of amastigotes in imprinting of infected organs . To overcome some of these problems , we described in this study an experimental model of visceral leishmaniasis ( VL ) using parasites expressing luciferase . Leishmania infantum chagasi , the etiologic agent of VL in Latin America , was genetically manipulated to express the luciferase gene in order to produce light upon addition of luciferin . We inoculated these parasites in hamsters and , about one month post-infection , we detected light in the abdominal and/or pelvic region , indicating widespread infection . We compared the data obtained by bioluminescence with microscopic counting of amastigotes in imprinting of infected spleens and both methods showed concordant results . Aiming to validate our model to be used in drug effectiveness experiments , we performed the experimental treatment with pentavalent antimony , the first choice drug for VL treatment in Latin America . We also treated infected hamsters with miltefosine , the only oral drug used for leishmaniasis treatment and for which effectiveness against L . infantum chagasi infected hamsters had not yet been reported . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Generation of Luciferase-Expressing Leishmania infantum chagasi and Assessment of Miltefosine Efficacy in Infected Hamsters through Bioimaging |
Dengue hemorrhagic fever ( DHF ) is a severe form of dengue , characterized by bleeding and plasma leakage . A number of DHF risk factors had been suggested . However , these risk factors may not be generalized to all populations and epidemics for screening and clinical management of patients at risk of developing DHF . This study explored demographic and comorbidity risk factors for DHF in adult dengue epidemics in Singapore in year 2006 ( predominantly serotype 1 ) and in year 2007–2008 ( predominantly serotype 2 ) . A retrospective case-control study was conducted with 149 DHF and 326 dengue fever ( DF ) patients from year 2006 , and 669 DHF and 1 , 141 DF patients from year 2007–2008 . Demographic and reported comorbidity data were collected from patients previously . We performed multivariate logistic regression to assess the association between DHF and demographic and co-morbidities for year 2006 and year 2007–2008 , respectively . Only Chinese ( adjusted odds ratio [AOR] = 1 . 90; 95% confidence interval [CI]: 1 . 01–3 . 56 ) was independently associated with DHF in year 2006 . In contrast , age groups of 30–39 years ( AOR = 1 . 41; 95% CI:1 . 09–1 . 81 ) , 40–49 years ( AOR = 1 . 34; 95% CI:1 . 09–1 . 81 ) , female ( AOR = 1 . 57; 95% CI:1 . 28–1 . 94 ) , Chinese ( AOR = 1 . 67; 95% CI:1 . 24–2 . 24 ) , diabetes ( AOR = 1 . 78; 95% CI:1 . 06–2 . 97 ) , and diabetes with hypertension ( AOR = 2 . 16; 95%CI:1 . 18–3 . 96 ) were independently associated with DHF in year 2007–2008 . Hypertension was proposed to have effect modification on the risk of DHF outcome in dengue patients with diabetes . Chinese who had diabetes with hypertension had 2 . 1 ( 95% CI:1 . 07–4 . 12 ) times higher risk of DHF compared with Chinese who had no diabetes and no hypertension . Adult dengue patients in Singapore who were 30–49 years , Chinese , female , had diabetes or diabetes with hypertension were at greater risk of developing DHF during epidemic of predominantly serotype 2 . These risk factors can be used to guide triaging of patients who require closer clinical monitoring and early hospitalization in Singapore , when confirmed in more studies .
Dengue is a major neglected tropical disease in the tropical and subtropical regions of the world [1] . It is predominantly found in urban and semi-urban areas , and results in a wide spectrum of clinical manifestations , from asymptomatic infection , undifferentiated fever , dengue fever ( DF ) to severe infection known as dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [2] . It is estimated about 50 million infections occur annually , with 500 , 000 DHF cases and 22 , 000 deaths [3] . In several Asian countries , dengue is one of the leading causes of hospitalization and death among children [1]–[3] . In Singapore , however , there is a decreasing trend of children ( aged <15 years ) and an increasing trend of adults ( aged ≥25 years ) being infected with dengue since 1982 [4] . Dengue hemorrhagic fever is characterized by bleeding and plasma leakage which may lead to life-threatening shock , if unrecognized and not treated in a timely manner . Molecular determinants of DHF such as virus variation , viral load , antibody-dependent enhancement ( ADE ) , ‘original antigenic sin’ , ‘cytokine storm’ and plasma factors were proposed in the pathophysiology of DHF [5]–[7] . However , predicting or preventing the occurrence of DHF remains a challenge . Identifying risk factors for DHF can facilitate early clinical , preventive and healthcare resource management . Epidemiological risk factors of DHF such as dengue-serotype 2 [8] , [9] , Asian genotype [10] , prior dengue infections [11] , [12] , children [13] , [14] , age >65 years [15] , white females [12] , [16] were identified . Integrative analysis of these risk factors , together with the molecular determinants of DHF , may facilitate better understanding of the pathophysiology of DHF . Co-morbidities were reported as risk factors for DHF in a number of studies from dengue endemic countries . These co-morbidities included sickle cell anemia [17] , asthma [17]–[19] , hypertension [15] , [16] , [18] , uremia [15] , allergies treated with corticosteroid [16] and diabetes mellitus [15]–[18] . However , these co-morbidities may not be generalized to all populations and epidemics of all dengue serotypes . Furthermore , most of these risk factors were identified from univariate analysis [15] , [17]–[19] instead of multivariate analysis to adjust for potential confounders [16] . In this study , we explored demographic and co-morbidity risk factors for DHF in Singapore in the year 2006 ( where dengue serotype 1 predominated ) and in the year 2007 and 2008 ( where dengue serotype 2 predominated ) [20] .
For descriptive analysis , Pearson's chi-square and Fisher's exact tests were used to compare categorical variables , and Mann-Whitney U test was used to compare continuous variables with non-normal distribution . Univariate and multivariate logistic regression were used to calculate crude and adjusted odds ratios ( COR , AOR ) , respectively , and their 95% confidence intervals ( CI ) were used to assess the association of the variables with DHF . Confounding effect was minimized by performing multivariate logistic regression adjusting for potential confounders identified in the descriptive analysis . These potential confounders are exposures that were found to be statistically different ( p<0 . 05 ) between DHF and DF patients in Table 1 and the model that fits best in the multivariate regression is tested using likelihood-ratio test . In Singapore , dengue infections were predominantly due to dengue serotype 1 ( detected in 75% to 100% of dengue samples collected each month ) during the epidemic in the year 2006 , and dengue serotype 2 ( detected in up to 91% of dengue samples ) during the epidemic in the year 2007 and 2008 [20] . Given that different dengue serotypes may cause different disease severity [24] , the data from year 2006 and the data from year 2007 to 2008 were analysed separately to minimize confounding effect due to different predominant dengue serotypes . Stratified analyses were performed to evaluate the presence of effect modification between diabetes mellitus and other co-morbidities on the risk of DHF outcome . All statistical analyses were performed using Stata 10 . 0 ( Stata Corp . , College Station , TX , 2005 ) . All tests were conducted at the 5% level of significance , with OR , P-value and corresponding 95% CI reported where applicable . This study was approved by Domain Specific Review Board , National Healthcare Group , Singapore ( DSRB-E/08/567 ) with waiver of informed consent as this was a retrospective study and the data were analyzed anonymously .
In year 2006 epidemic , there were 149 DHF and 326 DF patients . Among these patients , there were 131 ( 27 . 6% ) patients who were PCR positive and 344 ( 72 . 4% ) patients who were serology positive but PCR negative . The mean age was 37 . 3 ( ±12 . 8 ) years and 34 . 0 ( ±11 . 0 ) years for DHF patients and DF patients respectively . Among DHF patients , there were 67 . 8% male and 77 . 2% Chinese . Of the 326 DF patients , 71 . 5% were male and 62 . 9% were Chinese ( Table 1 ) . In year 2007 and 2008 epidemic , there were 669 DHF and 1 , 141 DF patients . Among these patients , there were 590 ( 32 . 6% ) patients who were PCR positive and 1220 ( 67 . 4% ) patients who were serology positive but PCR negative . The mean age was 38 . 4 ( ±13 . 4 ) years and 36 . 2 ( ±12 . 9 ) years for DHF patients and DF patients respectively . Among the DHF patients , there were 58 . 7% male and 77 . 1% Chinese . Of the 1 , 141 DF patients , 70 . 9% were male and 62 . 8% were Chinese ( Table 1 ) . Of the demographic variables , statistically significant differences ( P<0 . 05 ) were found between DHF and DF with respect to mean age ( P = 0 . 008 ) , age groups ( P = 0 . 017 ) and ethnicity ( P = 0 . 021 ) in year 2006 , and mean age ( P<0 . 001 ) , age groups ( P = 0 . 002 ) , gender ( P<0 . 001 ) and ethnicity ( P<0 . 001 ) in year 2007 and 2008 ( Table 1 ) . Using the number of fever days before hospital presentation as a surrogate index of health-seeking behavior between DHF and DF patients , no significant difference was observed in both year 2006 ( P = 0 . 941 ) as well as year 2007–2008 ( P = 0 . 308 ) ( Table 1 ) . Notably , statistically significant differences were found between DHF and DF with respect to hypertension ( P = 0 . 036 ) and diabetes mellitus ( P = 0 . 004 ) in year 2007 and 2008 but not year 2006 ( Table 1 ) . Chinese ethnicity was the only significant risk factor independently associated with DHF in year 2006 , after adjustment for statistically significant univariate risk factors ( Table 2 ) . Although marginally significant , the likelihood ( AOR ) of a Chinese patient developing DHF was 1 . 90 ( 95% CI:1 . 01–3 . 56 ) times higher than that of other ethnicity ( not Chinese , Malay or Indian ) . In year 2007 and 2008 , age groups , gender and ethnicity were observed to be independently associated with DHF , following adjustment for statistically significant univariate risk factors ( Table 2 ) . The likelihood ( AOR ) of an individual who were 30 to 39 years of age and 40 to 49 years of age developing DHF was 1 . 41 ( 95% CI:1 . 09–1 . 81 ) and 1 . 34 ( 95% CI:1 . 09–1 . 81 ) times higher than that of an individual below 30 years old respectively . Females had 1 . 57 ( 95% CI:1 . 28–1 . 94 ) times higher risk developing DHF than males . In addition , the likelihood ( AOR ) of a Chinese patient developing DHF was 3 . 15 ( 95% CI:2 . 34–4 . 23 ) and 1 . 67 ( 95% CI:1 . 24–2 . 24 ) times higher than that of Indian and other ethnicity respectively ( Table 2 ) . For co-morbidities , after adjustment for statistically significant univariate risk factors , only diabetes mellitus remained an independent risk factor for DHF outcome ( AOR = 1 . 78; 95% CI:1 . 06–2 . 97 ) in year 2007 and 2008 ( Table 3 ) . Diabetic patients tend to have other co-morbidities . We investigated the risk effect of DHF outcome on patients having diabetes mellitus with hypertension , hyperlipidemia or asthma . Diabetes mellitus with hypertension ( COR = 2 . 43; 95% CI:1 . 42–4 . 15 ) , diabetes mellitus with hyperlipidemia ( COR = 1 . 82; 95% CI:1 . 06–3 . 12 ) and diabetes mellitus with no asthma ( COR = 1 . 74; 95% CI:1 . 10–2 . 76 ) were observed to be significantly associated with DHF outcome ( Table 4 ) . However , only diabetes mellitus with hypertension ( AOR = 2 . 16; 95% CI:1 . 18–3 . 96 ) and diabetes with no asthma ( AOR = 1 . 68; 95% CI:1 . 02–2 . 76 ) were observed to be independently associated with DHF outcome after adjustment for statistically significant univariate risk factors ( Table 4 ) . Interestingly , the likelihood ( AOR ) of an individual having diabetes mellitus with asthma developing DHF was 4 . 38 ( 95% CI:0 . 80–23 . 85 ) times higher than that of an individual having no diabetes with no asthma . However , there is a lack of statistical significance and it is most likely due to the small sample size with only 7 subjects having diabetes mellitus with asthma ( Table 4 ) . In order to confirm this observed phenomenon , further studies with larger sample size are required . In addition , among patients with hypertension , the likelihood ( AOR ) of developing DHF due to diabetes mellitus was higher ( AOR = 2 . 39; 95% CI:1 . 21–4 . 71 ) compared to that of patients without hypertension ( AOR = 1 . 28; 95% CI:0 . 56–2 . 93; Table 5 ) . This provided preliminary evidence of effect modification between diabetes mellitus and hypertension on the risk of DHF outcome . Moreover , it was observed that the mean hospitalization days was longer for diabetic patients ( 4 . 99±3 . 34 days ) compared to non-diabetic patients ( 4 . 04±1 . 62 days; P = 0 . 001 ) . Significant difference was also observed in the mean hospitalization days between diabetic DHF patients and non-diabetic DHF patients ( diabetic DHF: 5 . 21±3 . 12 days; non-diabetic DHF: 4 . 33±1 . 75 days; P = 0 . 046 ) ( data not shown ) . Subgroup analyses of patients with dengue IgG data and of Chinese patients were carried out . In the subgroup analysis of 1 , 220 ( 67 . 4% ) patients hospitalized during the year 2007–2008 that had dengue IgG data , we further showed that diabetes ( AOR: 1 . 92; 95% CI: 1 . 02–3 . 61 ) as well as diabetes with hypertension ( AOR: 4 . 41; 95% CI: 1 . 16–16 . 82 ) remained as risk factor of DHF ( Table S1 ) . Furthermore , in a subgroup analysis of cases ( DHF ) and controls ( DF ) identified as Chinese in year 2007 and 2008 , diabetes mellitus ( AOR = 2 . 23; 95% CI:1 . 21–4 . 11 ) , diabetes mellitus with hypertension ( AOR = 2 . 1; 95% CI:1 . 07–4 . 12 ) , diabetes mellitus with no hyperlipidemia ( AOR = 3 . 75; 95% CI:1 . 27–11 . 02 ) and diabetes mellitus with no asthma ( AOR = 1 . 96; 95% CI:1 . 09–3 . 52 ) were independently associated with DHF outcome , after adjustment for age groups , gender , and hypertension ( data not shown ) .
The results of this study showed that female , Chinese , age group between 30 to 49 years , pre-existing diabetes mellitus or diabetes mellitus with hypertension were risk factors of developing DHF during the year 2007 and 2008 epidemic when dengue serotype 2 was predominant . In contrast , Chinese ethnicity was the only risk factor observed during the year 2006 epidemic when dengue serotype 1 was predominant . This might be due to the different predominant circulating dengue serotypes during the two epidemics [20] . Notably , dengue serotype 2 was known to be associated with more severe dengue disease than serotype 1 [8] , [9] , [24] . In a combined analysis of year 2006 , 2007 and 2008 epidemic , all the risk factors identified in the year 2007–2008 epidemic remained as independent risk factors except for diabetes mellitus ( Table S2 ) . This may suggest potential confounding effect of different serotypes . Furthermore , it was observed that age , gender and co-morbidities were not independently associated with DHF outcome in a previous study of 1 , 973 adult dengue patients in the year 2004 epidemic when dengue serotype 1 was also predominant [25] . However , it is not possible to conclusively demonstrate serotype difference during epidemics as the main factor that accounted for the differences in risk factors in this study . This was because individual serotype data was inaccessible for our analyses due to national regulations . Instead , the differences in risk factors may be due to the small sample size of patients admitted in year 2006 , and the significant differences in mean age , number of patients with co-morbidities and DHF outcome admitted during the two epidemics ( Table S3 ) . It is beyond the scope of this study to highlight other potential factors , such as climate change , viral genotype change as well as change in health-seeking behaviors that may have also resulted in the differences . It is not surprising that female and Chinese ethnicity were independent risk factors of DHF as gender [12] , [26] and ethnicity [12] , [16] were shown to be risk factors for DHF in Cuba and Brazil studies as well as in Vietnam for dengue shock syndrome ( DSS ) . Age groups between 30 and 39 and between 40 and 49 were independent risk factors of DHF in our adult dengue cohort . This observation is different from previous studies in Cuba [13] and in Singapore [14] where children <14 years of age had higher risk of developing DHF compared to young adults aged 15 years or greater . The rationale behind this difference could be due to lowered herd immunity and change in transmission pattern [14] , [27] . The elderly ( >65 years of age ) in Taiwan [15] had a higher risk of developing DHF . However , the age group ≥60 year was not an independent risk factor of DHF outcome ( Table 2 ) in our current study which is also consistent with our previous study on dengue in older adults [28] . It is still not well understood how these risk factors contribute to the pathophysiology of DHF , and understanding the underlying mechanism may facilitate clinical management . Co-morbidities such as hypertension , diabetes mellitus , hyperlipidemia and asthma are among the few leading causes of mortality and morbidity in Asia [29] and globally [30] , [31] . Co-morbidities were shown to be associated with severe clinical manifestations of several infectious disease such as SARS [32] , [33] , pandemic influenza H1N1 [34] , tuberculosis [35] , [36] , hepatitis C [37] and community-acquired infections [38] , [39] . Many studies found association between various co-morbidities and DHF outcome [15]–[19] but only one study was carried out with multivariate analysis to adjust for potential confounders [16] . Furthermore , none has evaluated the risk effect of two existing co-morbidities and the effect modification between two co-morbidities on DHF outcome . In this study , we showed that diabetes mellitus was associated with DHF outcome as observed by other studies [15]–[18] . In addition , we observed that individuals reported having diabetes mellitus with hypertension had higher risk of developing DHF compared with individuals with no diabetes mellitus and no hypertension . Our study may be the first that provide the preliminary evidence of synergy of risk effect between diabetes mellitus and hypertension on DHF outcome ( Tables 4 & 5 ) . Our study showed concomitant diabetes mellitus with hypertension as an independent risk factor for DHF in a large number of adult DHF cases in Singapore , and supported the initial evidence of association between hospitalization with a diagnosis of DHF and diabetes mellitus in Brazil [16] . However , the pathophysiology behind diabetes leading to DHF outcome is not well understood yet , even though numerous studies had suggested that diabetes mellitus can result in immune and endothelial dysfunction [40]–[44] . Identifying risk factors for DHF can guide clinicians to triage dengue patients for the right site of care for closer monitoring and early intervention with fluid resuscitation . In an epidemic where healthcare resources may be stretched , risk factors for DHF can be used to prioritize hospitalization of dengue patients . In our study , we observed that diabetic patients with DHF outcome required longer stay and , presumably , required more medical attention in the hospital compared to non-diabetic patients with DHF . Additionally , policy makers can prioritize population groups at high risk of developing DHF such as female patients , patients in age group 30–49 , and patients having diabetes or diabetes with hypertension for vaccination when dengue vaccines are available , particularly in resource-limited countries . Demographic and co-morbidity risk factors may help public health clinicians raise awareness among high-risk individuals to take preventive measures against dengue infections . As this is a retrospective study , the quality of the study was dependent on the quality of the data available and collected . Information bias was minimized by the use of the standardized dengue care path for consistent clinical documentation . Reporting bias was minimized by the fact that patients with comorbidities tend to know their existing condition and are likely to be on constant medication . However , it is challenging to exclude the fact that there are no undetected existing comorbidities among some of these patients as this study is performed retrospectively . In addition , there may be selection bias because the subjects were all hospitalized and hence were likely to have active health care-seeking behaviour , and the controls were hospitalized DF patients who may not truly represent the general population . In the general population , less active health care-seeking , asymptomatic or mild DF patients may not visit a doctor or hospital and may also have diabetes mellitus or other co-morbidities assessed in this study . However , it is technically challenging to identify these less active health care-seeking , asymptomatic or mild DF patients for inclusion in the study . We also did not have patient-specific dengue serotype data and could only extrapolate our observations from previous population study in Singapore [20] . Lastly , we understand the importance of accounting for prior infection as it is a main risk factor for DHF . The result of IgG test carried out within seven days of fever onset can be used to classify patients with or without prior infection [22] , [45] . However , we only have had IgG results of 67 . 4% of all patients during the year 2007–2008 . In the subgroup analysis , we showed that prior infection was not significantly associated with DHF in adult patients ( Table S1 ) . Furthermore , it has been shown that prior infection was strongly associated with DHF in children under the age of 15 years [24] , [46] . In other words , this may suggest that diabetes as well as diabetes with hypertension may be risk factors of DHF in adults , regardless of prior dengue infections . Further studies involving larger number of patients with acute secondary infections are required to confirm this hypothesis . In conclusion , we found age between 30 and 49 years , female gender , Chinese ethnicity , diabetes mellitus and diabetes mellitus with hypertension to be independent risk factors for DHF in an adult dengue epidemic with predominantly dengue serotype 2 . The two co-morbidities appeared to have effect modification on the risk of DHF outcome . More studies , particularly prospective studies are required to confirm these findings . Our finding raised the likely association between the pathophysiology of diabetes mellitus , hypertension and dengue severity . An ongoing genome-wide association study in Singapore may help elucidate genetic predisposition to severe dengue disease including the role of diabetes mellitus . | Dengue is a major vector borne disease in the tropical and subtropical regions . An estimated 50 million infections occur per annum in over 100 countries . A severe form of dengue , characterized by bleeding and plasma leakage , known as dengue hemorrhagic fever ( DHF ) is estimated to occur in 1–5% of hospitalized cases . It can be fatal if unrecognized and not treated in a timely manner . Previous studies had found a number of risk factors for DHF . However , screening and clinical management strategies based on these risk factors may not be applicable to all populations and epidemics of different serotypes . In this study , we found significant association between DHF and diabetes mellitus and diabetes mellitus with hypertension during the epidemic of predominantly serotype 2 ( year 2007 and 2008 ) , but not during the epidemic of predominantly serotype 1 ( year 2006 ) . Diabetes mellitus and hypertension are prevalent in Singapore and most parts of South-East Asia , where dengue is endemic . Therefore , it is important to address the risk effect of these co-morbidities on the development of DHF so as to reduce morbidity and mortality . Our findings may have impact on screening and clinical management of dengue patients , when confirmed in more studies . | [
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"neglected",... | 2012 | Diabetes with Hypertension as Risk Factors for Adult Dengue Hemorrhagic Fever in a Predominantly Dengue Serotype 2 Epidemic: A Case Control Study |
The idea that synaptic properties are defined by specific pre- and postsynaptic activity histories is one of the oldest and most influential tenets of contemporary neuroscience . Recent studies also indicate , however , that synaptic properties often change spontaneously , even in the absence of specific activity patterns or any activity whatsoever . What , then , are the relative contributions of activity history-dependent and activity history-independent processes to changes synapses undergo ? To compare the relative contributions of these processes , we imaged , in spontaneously active networks of cortical neurons , glutamatergic synapses formed between the same axons and neurons or dendrites under the assumption that their similar activity histories should result in similar size changes over timescales of days . The size covariance of such commonly innervated ( CI ) synapses was then compared to that of synapses formed by different axons ( non-CI synapses ) that differed in their activity histories . We found that the size covariance of CI synapses was greater than that of non-CI synapses; yet overall size covariance of CI synapses was rather modest . Moreover , momentary and time-averaged sizes of CI synapses correlated rather poorly , in perfect agreement with published electron microscopy-based measurements of mouse cortex synapses . A conservative estimate suggested that ~40% of the observed size remodeling was attributable to specific activity histories , whereas ~10% and ~50% were attributable to cell-wide and spontaneous , synapse-autonomous processes , respectively . These findings demonstrate that histories of naturally occurring activity patterns can direct glutamatergic synapse remodeling but also suggest that the contributions of spontaneous , possibly stochastic , processes are at least as great .
Activity-induced modification of synaptic connections ( synaptic plasticity ) is widely believed to represent a major mechanism for modifying the functional properties of neuronal networks . Indeed , overwhelming experimental evidence supports the idea that synaptic properties are affected by the history of their activation . What is less established and often ignored is the "flip side" of synaptic plasticity: that is , the implicit supposition that synapses , when not driven to change their characteristics , will retain these over time . This assumption would seem to be an essential complement of the synaptic plasticity concept; without it , spontaneous changes occurring independently of physiologically relevant input would cause spurious changes in network function or undo physiologically relevant ones . The validity of this assumption has been called into question by recent studies , in which sizes and contents of individual synapses—both excitatory and inhibitory—were observed to fluctuate considerably over timescales of hours and days ( e . g . , [1–17] ) ; notably , such fluctuations persisted even in the absence of specific activity patterns or any activity at all ( e . g . , [5 , 6 , 9 , 12 , 17] ) . Finally , it was shown that these fluctuations could be described remarkably well by statistical processes that are essentially stochastic [5 , 6 , 8 , 16 , 17] . Given the emerging view of the synapse as a complex assembly of dynamical components [1 , 2] , the presence of such fluctuations might not be very surprising . Nevertheless , they would seem to imply that synaptic tenacity , which we define as the capacity of individual synapses to maintain their properties over behaviorally relevant time scales [6 , 9 , 11 , 17] , is inherently limited , and that synapses exhibit a non-negligible degree of spontaneous size remodeling . Although these conclusions were derived mainly from studies in reduced systems ( cell and organotypic cultures ) , they are not limited to these settings [4 , 8 , 14 , 15] . Thus , for example , it has recently been shown that synapse size fluctuations in the cerebral cortex of adult mice are at least as large as those observed in culture ( [15]; see also [4] ) ; in fact , the degree of such size fluctuations is comparable to the magnitude of size changes induced by experimental stimulation paradigms that induce long-term potentiation ( e . g . , [18 , 19] ) . Thus , when considering changes in synaptic sizes , it remains to be asked what the relative contributions of specific activity histories to such changes are and how these compare to size changes driven by other , possibly stochastic , processes . In the rodent cerebral cortex , two neurons are often connected by more than one excitatory synapse ( reviewed in [20] ) . This situation provides an excellent opportunity to examine the relative contributions of specific activity histories to changes in synaptic sizes and then compare these to the contributions of other processes . This claim is based on the reasonable premise that , to a first approximation , all synapses connecting two specific neurons ( commonly innervated [CI] synapses ) will have similar activation histories when these are integrated over many days [21 , 22] . Assuming that changes in synaptic properties are driven primarily by activation histories , changes in the sizes of such CI synapses might be expected to co-vary significantly . In contrast , synapses formed on the same neuron or dendrite by two different upstream neurons ( non-commonly innervated [non-CI] synapses ) , would have somewhat different activation histories , and thus their sizes would not be expected to co-vary to the same degree . Moreover , the remodeling covariance would be expected to be even greater for nearby synapses formed between the same axonal and dendritic segments , as regional differences in axonal/dendritic properties would minimally affect activity histories and their biological consequences . Finally , this approach provides an opportunity to examine how synaptic sizes are affected by more natural activation histories , spanning hours and days , as compared to the brief and rather artificial stimulation paradigms typically used in experimental settings ( reviewed in [23] ) . In the current study we measured and compared the remodeling of CI and non-CI synapses in monolithic and modular networks of cortical neurons in primary culture by using genetically encoded fluorescent reporters combined with multielectrode array ( MEA ) recordings , automated confocal microscopy , and pharmacological manipulations . Although cortical networks in culture differ in many ways from their in vivo counterparts , in the current context , they are advantageous in the sense that they provide a generic , isolated , and well-controlled system for studying the net effects of activation histories , free from potential confounds inherent to in vivo settings such as behavioral states , stress , neuromodulatory input , and circadian rhythms . Moreover , as shown below , this system allows for excellent long-term and continuous monitoring of synaptic sizes , the presynaptic origins of individual synapses , and experimental differentiation of activation histories . Our findings are presented next .
The rationale of the experiments described below is depicted in Fig 1A . In this scheme , a single postsynaptic neuron is innervated by multiple axons belonging to different “upstream” excitatory neurons . A subset of synapses formed on this postsynaptic cell represents connections formed with a particular upstream axon , and these are hereafter referred to as CI synapses . Some CI synapses are located on the same dendrite , and these are hereafter referred to as Commonly Innervated Same Dendrite ( CISD ) synapses . For each CI synapse , a nearby synapse is selected , which represents a connection between the postsynaptic neuron and another axon . These are hereafter referred to as reference ( Ref ) synapses . As explained above , it might be expected that CI synapses will have very similar activation histories ( even more so , perhaps , for CISD synapses ) . If activation history is the major force that drives changes in synaptic size , then CI synapses should change in a similar manner , resulting in a strong covariance of their sizes over time ( as illustrated schematically in Fig 1B ) . Similarly , given that CI and Ref synapses are activated by different upstream neurons and assuming that the activity histories of these neurons differ significantly ( a matter we will return to later ) , sizes of CI and Ref synapses ( non-CI synapses ) would not be expected to co-vary to the same extent ( Fig 1C ) , with the residual covariance mainly representing the combined contributions of ( postsynaptic ) neuron- ( or dendrite- ) wide , non-synapse-specific processes . The overall goals were therefore to ( 1 ) quantify the covariance of CI synapses , ( 2 ) compare it to the covariance of non-CI synapses , and ( 3 ) use these data to estimate the specific contributions of particular activity histories to the remodeling of glutamatergic synapses . The experiments were carried out in a system based on networks of rat cortical neurons growing on thin glass MEA substrates , automated confocal microscopy , and genetically encoded fluorescent reporters . This system , which we have previously used to explore relationships between activity and remodeling of excitatory [6 , 16 , 24] and inhibitory synapses [17] , allows for chronic recordings of network activity from up to 59 electrodes while simultaneously imaging synapses by automated confocal microscopy for many days , even weeks . For these experiments , we used cortical networks maintained in culture for 18–21 d , as at this time , synaptogenesis is mostly complete and synapses are relatively mature . To estimate changes in synaptic sizes , we expressed fluorescently tagged variants of the postsynaptic density ( PSD ) protein PSD-95 ( /Dlg4/SAP90 ) and followed changes in its fluorescence at individual synapses . PSD-95 is a core postsynaptic scaffolding protein of glutamatergic synapses that is thought to control the number of glutamate receptors at the postsynaptic membrane through direct and indirect interactions ( reviewed in [25]; see also [26] ) . Importantly , a recent in vivo correlative light and electron microscopy study [15] demonstrated excellent correlations between tagged PSD-95 fluorescence and PSD area when these are measured for the same synapses , and thus fluorescently tagged PSD-95 can be used to record changes in PSD area and , by extension , in synaptic size . To locate CI synapses , we expressed spectrally separable fluorescently tagged variants of presynaptic molecules , namely SV2 ( a conserved , highly specific synaptic vesicle integral membrane protein; [27 , 28] ) or Synapsin I ( a synaptic vesicle-associated phosphoprotein [29]; experiments described later on ) . Expression of all fluorescent reporters was carried out using lentiviral vectors , resulting in minimal overexpression levels of exogenous proteins and very sparse labeling of individual neurons . In spite of the sparse labeling , postsynaptic sites ( labeled with fluorescently tagged PSD-95 ) juxtaposed against fluorescent presynaptic sites ( labeled with fluorescently tagged SV2 or Synapsin I ) were often observed . Careful examination then allowed us to locate pairs ( and sometimes triplets or more ) of CI synapses , that is , postsynaptic sites connected to the same axon . As axonal shafts were often barely discernable , the selection of CI synapses for subsequent analyses was limited to short axonal stretches for which a common axonal origin could be determined unambiguously ( see Materials and Methods for further details ) . Fluorescently tagged CI synapses were then followed over time to verify that presynaptic and postsynaptic compartments remained juxtaposed at all time points . A Ref synapse was then chosen near each synapse connected to the common axon , and tagged PSD-95 fluorescence was measured at all synapses—CI and Ref alike—at each time point for the duration of the experiments ( all measurements were made in maximum intensity projections of all sections ) . To minimize the potential effects of measurement noise , fluorescence measures of each synapse were first smoothed with a 2 . 5- to 3-h low-pass filter [16] . The fluorescence covariance of all CI and non-CI synapse pairs was then calculated using Pearson’s correlation ( a linear measure ) as well as Spearman’s rank correlation ( a measure that quantifies monotonic , but not necessarily linear , relationships between two variables ) . We first compared the covariance of CI and non-CI synapses in monolithic cortical networks . In these experiments , individual postsynaptic sites were visualized using PSD-95 tagged with enhanced green fluorescent protein ( EGFP ) ( PSD-95:EGFP; [6 , 24] ) , whereas presynaptic sites were visualized using SV2 tagged with Cerulean ( a cyan fluorescent protein variant; Cer:SV2; [30] ) . As shown in Fig 2A , dendrites , individual postsynaptic sites , and presynaptic boutons were readily discernable , allowing us to locate and follow CI and Ref synapses ( Fig 2B ) . To compare the size covariance of CI and non-CI synapses , the networks were mounted on the combined MEA recording/imaging system described above and provided with optimal environmental conditions ( a sterile atmospheric environment of 5% CO2 and 95% air , slow perfusion with fresh feeding medium , and a temperature of 37°C ) , allowing us to carry out experiments lasting one week or longer with no signs of deterioration or cell death ( Fig 2B ) . Stacks of images ( at 10 focal planes ) were collected automatically from 6–12 fields of view ( or sites ) . Images were collected at 30-min intervals for several days concomitantly with recordings of network activity ( action potentials ) from the 59 electrodes of the MEA dish . As we noted in preliminary experiments that Cerulean exhibited significant photobleaching , axons were imaged at longer intervals ( once every 7 . 5 h ) . Imaging was started only 2–3 d after mounting the preparations , as we noted here and elsewhere [6 , 17] that the first 24–36 h of such experiments are invariably associated with increases in spontaneous activity levels related to the introduction of slow perfusion . Imaging in spontaneously active networks was then carried out for at least two further days . Finally , the Na+ channel blocker tetrodotoxin ( TTX ) was added to the MEA and perfusion media to suppress spontaneous network activity , and imaging was continued for additional 1–2 d . In agreement with prior cell culture [3 , 6 , 11 , 13 , 24 , 31] and in vivo [4 , 15] studies , the fluorescence of individual PSD-95:EGFP puncta often changed considerably over timescales of many hours . This is exemplified for two CI and two non-CI synapses in Fig 2C . The synapse size covariance of CI and non-CI synapses was then compared by calculating the correlation between the changes in PSD-95:EGFP fluorescence for each CI and non-CI pair over periods of 48 h . This is illustrated for one CI synapse pair ( Fig 2C ) and respective non-CI pairs ( Fig 2D ) . In this example , the covariance of the CI pair is much greater than that of the non-CI pairs; this difference , however , was not nearly as obvious in all such comparisons ( 92 pairs from 24 neurons in 6 experiments ) . In fact , distributions of both Pearson’s correlation coefficients ( r ) and Spearman’s rank correlation coefficients ( ρ ) measured for both CI and non-CI pairs were quite broad ( Fig 3A and S1A Fig , respectively ) . Nevertheless , the average covariance measured for all 92 CI pairs was somewhat greater than that measured for all non-CI pairs: ( Fig 3B , S1B Fig; CI pairs: r = 0 . 17 ± 0 . 05 , ρ = 0 . 15 ± 0 . 05; non-CI pairs: r = 0 . 06 ± 0 . 02 , ρ = 0 . 05 ± 0 . 02; average ± SEM; p = 0 . 04 , p = 0 . 04 , Pearson’s and Spearman’s correlation respectively , two-tailed Mann-Whitney U test ) . This difference was also observed when data were pooled by experiment ( Fig 3D , S1D Fig; CI pairs: r = 0 . 19 ± 0 . 05 , ρ = 0 . 18 ± 0 . 05; non-CI pairs: r = 0 . 05 ± 0 . 03 , ρ = 0 . 04 ± 0 . 03; average ± SEM; p = 0 . 04 , p = 0 . 04 , Pearson’s and Spearman’s correlation , respectively , Mann-Whitney U test ) . If the greater covariance observed for CI synapses is due to the commonality of their activation histories , blocking network activity might be expected to reduce CI synapse covariance to levels observed for non-CI synapses . Somewhat surprisingly , however , suppressing spontaneous network activity as described above , resulted in substantial increases in covariance values for both CI and non-CI synapses ( Fig 3C and 3E , S1C and S1E Fig; CI pairs: r = 0 . 25 ± 0 . 06 , ρ = 0 . 25 ± 0 . 06; non-CI pairs: r = 0 . 17 ± 0 . 04 , ρ = 0 . 17 ± 0 . 03; average ± SEM ) . We attribute this general increase in remodeling covariance to the nonspecific growth of glutamatergic synapses associated with the suppression of network activity ( S2A Fig , see also [6 , 24] ) . A small difference between the covariance of CI and non-CI pairs was still apparent; this difference , however , was not statistically significant ( p = 0 . 33 , p = 0 . 41 , Pearson’s and Spearman’s correlation , respectively , Mann-Whitney U test ) . The suppression of network activity is known to evoke and affect numerous cellular processes ( collectively referred to as synaptic “homeostatic” processes [32] ) and parametrically affect the statistics of stochastic remodeling processes [6 , 16] . As the effects of “homeostatic” processes are not easily disentangled from activity-dependent remodeling processes in active networks , these apparently straightforward experiments were not as informative as might have been expected , although they hint that CI synapses might change in a slightly more correlated manner even when network activity is blocked ( see Discussion ) . Although the size covariance observed for CI synapses in active networks was somewhat greater than that observed for non-CI synapses , the difference was surprisingly modest . We explored several possible reasons for this modest difference . We first considered the possibility that the overall extent of remodeling exhibited by synapses in these preparations was small , and , thus , the measures of covariance used here might have reflected , for the most part , the ( in ) coherence of low amplitude noise in fluorescence measurements . To evaluate this possibility , we measured for each synapse its normalized range of change ( “range over mean” ) defined as RangeMean=100*Fmax−FminF¯ where Fmax , Fmin , and F¯ are the maximal , minimal , and average PSD-95:EGFP fluorescence intensities , respectively , measured for a given synapse over a period of 48 h . As shown in Fig 3F , distributions of range over mean values were rightward skewed and similar for CI and Ref synapses; about 35% of synapses changed by more than 40% over this period , whereas averages ( ±SEM ) of range over mean values were 37% ± 1 . 6% ( CI ) and 37% ± 1 . 6% ( Ref ) ( Fig 3G ) . Thus , synapses exhibited substantial changes over these periods , similar in magnitude to changes induced in organotypic slice cultures by paradigms that induce long-term potentiation ( 33% on average; [18] ) . This and the fact that all data were low-pass filtered before analysis is thus not in line with the possibility that our covariance measures mainly reflect low amplitude measurement noise ( see also [16] ) . Interestingly , the suppression of network activity reduced , but did not eliminate , synaptic remodeling ( Fig 3H; CI: 23% ± 1 . 2%; Ref: 24% ± 1 . 5%; average ± SEM ) . Here too , however , the contributions of “homoeostatic” and other processes to this remodeling are not readily disentangled . The expected differences in size covariance of CI and non-CI synapses are based on the assumption that activity histories of CI synapses are much more similar than activity histories of non-CI synapses . If , however , all synapses—regardless of their presynaptic origin—share similar activation histories , the size covariance of CI and non-CI synapses might not be expected to differ much . This possibility cannot be ignored , as activity in the preparations used here tends to occur as synchronized bursts that encompass a large fraction of neurons within the network ( Fig 4A; e . g . , [6 , 24 , 33–36] ) . To increase the “contrast” between the activity histories of synapses belonging to different neurons , we desynchronized network activity by exposing the neurons to Carbachol [24] , a non-hydrolysable cholinergic agonist . As shown in Fig 4B , Carbachol ( 20 μM ) greatly diversified the spontaneous activity characteristics , causing some neurons to fire continuously , others to fire more sporadically , and others to fire only occasionally . Furthermore , the tendency of the network to generate network-wide , synchronous bursts was suppressed . Somewhat unexpectedly , this manipulation eliminated the differences between CI and non-CI synapses while elevating their absolute size covariance values ( Fig 4C and 4D; CI pairs: r = 0 . 26 ± 0 . 09 , ρ = 0 . 21 ± 0 . 11; non-CI pairs: r = 0 . 25 ± 0 . 05 , ρ = 0 . 22 ± 0 . 06; average ± SEM ) . Here too , the increased covariance reflects the generalized synaptic growth that follows prolonged exposure to cholinergic agonists ( S2B Fig ) [24] . The experiments described so far indicated that synapses with similar activity histories changed in a somewhat more correlated manner in comparison to synapses with apparently different activity histories , but the difference between the two groups was rather modest . The possibility that this might have been due to the limited diversity of activity histories in these networks was not supported by pharmacological network desynchronization , but the interpretation of the latter experiments was complicated by the global effects of cholinergic agonists on synaptic properties . Moreover , due to the tendency of synchrony to reemerge after ~12 h in such experiments [24] , the duration of such experiments was inherently limited . We thus sought to diversify the activity histories of CI and non-CI synapses by different means . To that end , we turned to modular network architectures . As mentioned above , large groups of neurons in the networks used here tend to fire in synchronized bursts , indicating that the activity histories of neurons in such networks might be quite similar . Previous studies have shown , however , that when such networks are divided into modules separated by barriers partially restrictive to axonal extension , activities in the two modules become more disparate ( [37]; see [38] for a comprehensive analysis ) . We thus set out to compare the size covariance of synapse pairs innervated by axons originating in the same module with the size covariance of synapse pairs in which each synapse is innervated by axons originating in two different modules . To that end , we labeled neurons in one module with a postsynaptic reporter ( referred to here as the “postsynaptic” module ) and labeled cells in the other module with a presynaptic reporter ( the “presynaptic” module; see Fig 5A for a schematic illustration of this “presynaptic/postsynaptic” arrangement ) . We then searched for pairs of synapses on neurons in the postsynaptic module formed by axons that crossed over from the presynaptic module . The assumption here was that the activity history of these synapses will be similar yet substantially different from the histories of most other synapses in the postsynaptic module , the axons of which were much more likely to have local origins . In practice , networks were divided into two subnetworks by fabricating polydimethylsiloxane ( PDMS ) inserts with two compartments and sealing them onto special MEA dishes whose electrodes were arranged in a modular fashion ( four-quadrant [4Q] MEAs ) . The two modules were connected through 6–12 very narrow channels ( ~400 μm long , ~13 μm wide , and ~3 μm high ) , which allowed some axons to grow across the barrier and innervate neurons in the other module ( Fig 5B ) , yet were restrictive to the migration of entire cells . Neurons in the presynaptic module were labeled with GFP-tagged Synapsin-Ia ( EGFP:SynI ) instead of Cer:SV2 due to its much greater photostability , its high endogenous expression levels , and its very high fidelity as a presynaptic marker [39] . Neurons in the postsynaptic module were labeled with PSD-95 tagged with mTurquoise2 ( PSD-95:mTurq2 ) , a very bright and relatively photostable variant of cyan fluorescent protein ( [40]; see also [13] ) . The expression of each reporter was fully restricted to its respective module , ensuring that EGFP:SynI-labeled axons observed in the postsynaptic module originated in the presynaptic module . A limited number of EGFP:SynI-labeled axons crossed over to the postsynaptic module ( Fig 5B ) and formed synapses with neurons in that module ( Fig 5C ) ; based on comparisons of axon labeling density in the presynaptic and postsynaptic modules , axons from the presynaptic module represented a tiny fraction ( less than 1% ) of the total number of axons in the postsynaptic module . Thus , the vast majority of PSD-95:mTurq2 puncta in the postsynaptic module was innervated by axons originating within that module . The presence of extracellular electrodes in both modules allowed us to examine the disparity of activity in the two modules . As shown in Fig 5D , some network-wide bursts spread from one module to the other ( with some delay ) , but many network-wide bursts remained confined to one module and did not spread to the other module . To verify that axons traversing the barrier indeed carried the activity patterns of the presynaptic module into the postsynaptic module , we expressed the genetically encoded calcium indicator GCaMP6s [41] in presynaptic module neurons and used an electron-multiplying charged couple device ( EMCCD ) camera to measure Ca2+ transients in the presynaptic boutons of axons that crossed into the postsynaptic module ( S3 Fig ) . To that end , sequences of 600 frames were captured at rates of ~7 Hz , allowing us to compare the timing of Ca2+ transients with network activities of the presynaptic and postsynaptic modules . As illustrated in S3E Fig , Ca2+ transients measured in such axons corresponded extremely well with bursts of activity recorded from the electrodes in the presynaptic module , but not nearly as well with bursts recorded from the postsynaptic module . This analysis also confirmed that Ca2+ transients in boutons distributed along the labeled axonal segments correlated almost perfectly , as might be expected . ( S3C and S3D Fig; see also [42] ) . Collectively , these observations show that activity histories of CI synapses are very similar , insofar as action potentials are concerned , whereas those of non-CI synapses differ significantly in both patterning and timing . We then carried out long-term combined imaging and electrophysiological recordings of neurons expressing PSD-95:mTurq2 and of axons expressing EGFP:SynI as described above . PSD-95:mTurq2 was imaged at 1-h intervals ( and EGFP:SynI at 1–3-h intervals ) for 2 d . Here too , imaging was initiated only 2–3 d after mounting the preparations on the microscope . After the experiments CI , CISD , and Ref synapses were located , tracked , and their fluorescence values measured . The covariance of CI , CISD , and non-CI pairs ( i . e . , pairs in which one synapse was innervated by an axon from the presynaptic module and the other by a local axon ) was then calculated and compared . As in the experiments performed in monolithic networks ( Fig 3 ) , distributions of correlation values measured for CI , CISD , and non-CI pairs were quite broad ( Fig 6A ) . Yet , in agreement with the aforementioned experiments , the average covariance measured for all CI pairs was greater than that measured for all non-CI pairs ( Fig 6B and S4B Fig; CI pairs: r = 0 . 28 ± 0 . 03 , ρ = 0 . 28 ± 0 . 03; non-CI pairs: r = 0 . 11 ± 0 . 02 , ρ = 0 . 11 ± 0 . 02; average ± SEM; p = 1*10−6 , p = 4*10−7 , Pearson’s and Spearman’s correlation , respectively , Mann-Whitney U test; 271 CI pairs from 29 neurons from 8 experiments ) . This difference was also observed when data were pooled by experiment ( Fig 6C , S4C Fig; CI pairs: r = 0 . 22 ± 0 . 07 , ρ = 0 . 24 ± 0 . 07; non-CI pairs: r = 0 . 02 ± 0 . 06 , ρ = 0 . 02 ± 0 . 06 , average ± SEM; p = 0 . 05 , p = 0 . 04 , Pearson’s and Spearman’s correlation , respectively , two-tailed Mann-Whitney U test ) . A similar observation was made for CISD pairs , i . e . , nearby synapses innervated by the same axon and formed on the same dendrite ( Fig 6D and 6E , S4D and S4E Fig; CI pairs: r = 0 . 34 ± 0 . 05 , ρ = 0 . 34 ± 0 . 05; non-CI pairs: r = 0 . 18 ± 0 . 03 , ρ = 0 . 16 ± 0 . 03; average ± SEM; p = 0 . 0036 , p = 0 . 0008 , Pearson’s and Spearman’s correlation , respectively , two-tailed Mann-Whitney U test; 91 CISD pairs from 29 neurons from 8 experiments ) . This difference was also observed when data were pooled by experiment ( Fig 6F , S4F Fig; CI pairs: r = 0 . 35 ± 0 . 08 , ρ = 0 . 36 ± 0 . 08; non-CI pairs: r = 0 . 08 ± 0 . 07 , ρ = 0 . 06 ± 0 . 07 , average ± SEM; p = 0 . 04 , p = 0 . 04 , Pearson’s and Spearman’s correlation , respectively , two-tailed Mann-Whitney U test ) . Although the introduction of a barrier diversified the activity histories of synapses belonging to non-CI pairs , some network-wide bursts did spread from one module to the other , suggesting that activity histories of synapses belonging to non-CI pairs were not entirely dissimilar . The degree to which the two modules were coupled in terms of their bursting activity varied from one experiment to another , ranging from 0 . 20 to 0 . 91 ( 0 . 64 ± 0 . 26 average ± standard deviation; see Materials and Methods for further details on this measure ) . Comparing this coupling with non-CI synapse covariance on an experiment-by-experiment basis revealed a positive correlation ( r = 0 . 62 ) between these two measures , although this correlation was not statistically significant ( p = 0 . 09 ) . In contrast , and as might be expected , no correlation was observed for CI synapses ( r = 0 . 04 , p = 0 . 99 ) . It should be noted that the measure used here to quantify coupling only considered the fraction of bursts that propagated from one module to another , ignoring functionally important features such as propagation delays and burst durations ( see [38] for a comprehensive analysis ) . Nevertheless , these findings indicate that even in modular networks , the size covariance of non-CI synapses might be influenced somewhat by partial similarities in activity histories , although this influence is at most very small ( Fig 6B , 6C , 6E and 6F , S4B , S4C , S4E and S4F Fig; see Discussion ) . Although the remodeling covariance of CI and non-CI pairs differed in a statistically significant manner , the actual differences were rather modest . We wondered if this might be due to the inclusion of relatively small synapses , which are more prevalent than large synapses in these preparations [6 , 16] and in the intact brain [43] , as these would be most sensitive to minor fluctuations in background fluorescence or measurement noise . To examine this possibility , we increased the stringency of selection criteria of CI pairs , removing small synapses from the analyses . Even with these stringent selection conditions , however , differences between the covariance of CI and non-CI pairs remained quite modest ( S5 and S6 Figs; 103 CI pairs: r = 0 . 26 ± 0 . 05 , ρ = 0 . 26 ± 0 . 04 non-CI pairs: r = 0 . 08 ± 0 . 03 , ρ = 0 . 08 ± 0 . 03; p = 0 . 003 , p = 0 . 002 , Pearson’s and Spearman’s correlation , respectively; 40 CISD pairs: r = 0 . 33 ± 0 . 07 , ρ = 0 . 35 ± 0 . 06; non-CI pairs: r = 0 . 17 ± 0 . 04 , ρ = 0 . 14 ± 0 . 04; p = 0 . 04 , p = 0 . 009 , Pearson’s and Spearman’s correlation , respectively; Mann-Whitney U test ) . Not only were the differences in size covariance for CI and non-CI synapses rather modest; the absolute covariance values for CI synapses were surprisingly small , with the highest average values observed in any of the experiments described above being r = 0 . 35 and ρ = 0 . 36 ( CISD synapses in modular networks; Fig 6F , S4F Fig , respectively ) . This would seem to suggest that , in addition to joint remodeling , each synapse within a CI pair exhibits significant change that occurs independently of its counterpart . Assuming that CI synapses and , in particular , CISD synapses share common activity histories , the residual remodeling would seem to represent spontaneous , activity-independent synaptic remodeling . Yet it remained possible that at least some of the imperfect size covariance of CI synapses stems from measurement limitations , such as fluorescence measurement inaccuracies . We therefore set out to determine what would have been the average size covariance measured in our system had CI synapse sizes co-varied perfectly . To that end , we introduced artificial correlations between PSD-95:mTurq2 puncta synaptic fluorescence levels by modulating excitation light intensities from one time point to the next ( Fig 7A and 7B ) ; we then measured the fluorescence of PSD-95:mTurq2 puncta ( Fig 7C ) and calculated the correlations for all pairs of synapses in the fields of view ( Fig 7D ) . The depths and temporal profiles of excitation laser light intensity modulation were based on changes in fluorescence levels measured for particular synapses during the long-term experiments described above ( Fig 7A ) , selecting for this purpose synapses whose range/mean ratios were similar to average range/mean ratios measured during those experiments ( e . g . , Fig 3F and 3G ) . The experiments were carried out in exactly the same way all experiments described so far were performed , except that here , 48 images were collected in rapid succession to minimize the effects of true synaptic remodeling . As shown in Fig 7E , average correlation values measured here were all positive and rather high ( r = 0 . 78 , ρ = 0 . 76; 100 synapses from 4 neurons , 1 , 223 pairwise comparisons ) . These experiments thus suggest that the modest size covariance observed for CI synapses cannot be solely attributed to measurement inaccuracies . So far , the analyses presented concerned the degree to which sizes of synapses with common activity histories changed together over time . But how similar were the absolute sizes of such synapses ? It might be expected that , given their common activity history , their sizes should be similar [21 , 22] . To examine the degree to which sizes of synapses with identical activity histories were similar , we plotted for each synapse in a CISD synapse pair its PSD-95:mTurq2 fluorescence against the fluorescence of its counterpart in the same pair . For this analysis we used the most stringent data set in which the smallest synapses were omitted ( see S5 and S6 Figs ) , using the measures of PSD-95:mTurq2 fluorescence obtained from the first image stack of each time-lapse series . As shown in Fig 8A , the correlation between the sizes of synapses belonging to the same CISD pair was rather poor ( r = 0 . 23 ) . We then repeated the same analysis for the same synapses , but now using PSD-95:mTurq2 fluorescence values averaged for each synapse over a period of 24 h . Here too , however , the correlation was still quite poor ( Fig 8B; r = 0 . 25 ) . The degree to which synapses formed between the same axon onto the same dendrite have similar sizes was explored as part of a recent study in which a small volume of mouse neocortex was reconstructed in full by serial section electron microscopy [21] . All data obtained in that study were made publicly accessible , allowing us to compare our findings , obtained in living cells , in cell culture , by light microscopy , to data obtained in fixed tissue , in vivo , by means of state-of-the-art electron microscopy . To that end , we identified in the aforementioned data set groups of spine synapses made by a particular axon onto a particular dendrite ( CISD synapses ) and plotted , for each synapse in each CISD pair , its PSD size and spine volume against the PSD size and spine volume of its CISD counterpart ( 124 pairs; Fig 8C and 8D , respectively ) . As these figures show , the size similarity of in vivo CISD synapses was no greater than the similarity of CISD synapses in culture . In fact , the correlation ( r = 0 . 23 ) between PSD sizes for synapses belonging to the same CISD pair was identical to the correlation observed in our study for PSD-95:mTurq2 fluorescence at synapses belonging to the same CISD pair ( Fig 8A and 8B ) . The data presented here suggest that the covariance of size changes for synapses that share similar activity histories is greater than that of synapses formed on the same neurons or dendrites that differ in their activity histories . At the same time , the data suggest that the covariance of size changes for synapses that share similar activity histories is significantly smaller than what might have been expected if synaptic remodeling was solely determined by activity histories . Our data thus allow for a conservative estimation of the maximal relative contribution of activity history-dependent processes to glutamatergic synapse remodeling in our system ( Fig 8E ) . For this estimation , we used ( 1 ) the highest average covariance values obtained here for CISD synapses ( pooled data , r = 0 . 34 and ρ = 0 . 34; Fig 6E and S4E Fig , respectively ) , as these represent synapses whose activity histories are probably the most similar in our data sets; ( 2 ) the lowest average covariance values obtained here for non-CI synapses ( pooled data , r = 0 . 06 and ρ = 0 . 05; Fig 3B and S1B Fig , respectively ) , as these represent the lowest possible contributions of ( postsynaptic ) neuron/dendrite-wide , nonspecific processes ( and possibly of some residual shared activity ) ; and ( 3 ) the maximal average correlation measurable in our system ( r = 0 . 78 , ρ = 0 . 76; Fig 7E ) . Using Pearson’s and Spearman’s correlations , respectively , the relative contributions of specific activity histories might thus be estimated as follows: 0 . 34−0 . 060 . 78≈0 . 36 ( Pearson’s ) , and 0 . 34−0 . 050 . 76≈0 . 38 ( Spearman’s ) The contributions of spontaneous processes that occur autonomously at each synapse can then be estimated as follows: 0 . 78−0 . 340 . 78≈0 . 56 ( Pearson’s ) , and 0 . 76−0 . 340 . 76≈0 . 55 ( Spearman’s ) This analysis suggests that under our experimental conditions , the ratio of contributions by activity history-dependent and -independent processes to synaptic remodeling is , at most , about 2:3 . Put differently , the “signal-to-noise ratio” of activity history-dependent synapse remodeling is approximately 0 . 7 , i . e . , less than one .
The interpretation of the experiments described here is based on several key assumptions that warrant some discussion . The first assumption concerns the identification of CI synapses as such . This identification was based on the juxtaposition of pre- and postsynaptic synaptic proteins tagged with fluorescent groups and , thus , ultimately on light microscopy . Several studies ( e . g . , [21 , 44] ) have suggested , however , that the proximity of an axon to a dendrite or a spine observed by light microscopy might not reliably predict the presence of a synapse , not even in a statistical sense ( e . g . , [45] ) . Yet it should be noted that this conclusion pertained to proximities of axons and spines , visualized by volume-filling dyes or electron microscopy , whereas , here , the presence of a synapse was deduced from the juxtaposition of fluorescent foci , originating in proteins that cluster almost exclusively at pre- and postsynaptic sites; thus , the presence of a synapse was deduced here not only from physical proximity but also from the juxtaposition ( in three dimensions ) of pre- and postsynaptic specializations . Furthermore , unlike most proximity-based assignments , which are typically performed in fixed tissue , assignments here were based on multiple observations of the same clusters over at least 2 d , and thus small movements of axons and dendrites ( which are quite common in these preparations; see [46 , 47] ) allowed us to exclude juxtaposed pre- and postsynaptic protein clusters that did not move in unison . It is also worth noting that when we restricted the analysis to the most cleanly identifiable , bright CI synapses , the results were practically identical ( compare S5 and S6 Figs with Fig 6 , S4 Fig ) . Thus , although we cannot exclude the possibility that some CI synapses were not really innervated by the same axon , it is unlikely that our conclusions were significantly affected by erroneous assignments . The second assumption concerns the relationships between tagged PSD-95 fluorescence , synapse size , and synaptic strength . A good correspondence between spine volume and synaptic strength has been established in multiple studies ( e . g . , [48–51] ) . Similarly , good correspondences between spine volume , PSD size , as well as α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) -type glutamate receptor content have been shown repeatedly ( e . g . , [18 , 22 , 52–54]; reviewed in [23 , 55] ) . Finally , an excellent correspondence between tagged PSD-95 fluorescence , measured by light microscopy , and PSD area , measured for the same synapses by electron microscopy , was recently reported [15] . As PSD area is thought to correlate with the number of synaptic glutamate receptors , [56] tagged PSD-95 fluorescence might represent an acceptable surrogate of synaptic strength [55] . Yet , under some circumstances , for example after strong stimuli that drive spine enlargement , spine volume is temporarily decoupled from PSD size and postsynaptic scaffold molecule contents , which “catch up” on a slower timescale ( 2–3 h; [18 , 19] ) ; this uncoupling might indicate that synaptic strength is not always predicted correctly by PSD size; by extension , it remains possible that synaptic strength is more stable than measurements of PSD-95 content would seem to suggest , because other processes ( for example , changes in glutamate receptor numbers ) acting over faster timescales maintain synaptic function within precise limits . How such processes might achieve this over a background of varying scaffold size , however , is not clear . Furthermore , repeated electrophysiological measurements of the same synaptic connections point to significant spontaneous fluctuations in connection strengths over comparable timescales ( 12 h or less ) , even when activity or synaptic transmission is blocked pharmacologically ( e . g . , [57–59] ) . It thus seems more likely that tagged PSD-95 fluorescence measurements such as those used here and elsewhere provide low-pass filtered estimates of synaptic strength , which underestimate , rather than overestimate , fluctuations in synaptic strength . In this respect , it is worth noting that synaptic contents of AMPA-type glutamate receptors seem to fluctuate at least as much as synaptic PSD-95 contents do ( [11]; see also [60] ) . The third assumption concerns the premise that CI synapses have similar activation histories when these are integrated over many days . Although this is a very reasonable premise [21 , 22] , it is not perfect . Ignoring for the moment the statistical nature of neurotransmitter release ( which would probably average out over these long timescales; [22] ) , presynaptic sites of cultured hippocampal neurons have been shown to exhibit significant functional variability even for sites located along the same axons ( e . g . , [61–65]; reviewed in [20] ) . As a result , activity histories of CI synapses might not be as similar as presumed , which might partially explain the modest covariance of their remodeling . We note , however , that this is an unlikely explanation . First , it was shown that presynaptic functional properties of nearby synapses formed by the same axons on the same dendrites are much more similar than those formed on different dendrites [61 , 63] . Consequently , had presynaptic variability been at the source of differential remodeling , the covariance of CISD synapse remodeling should have been much higher than that observed for CI synapses . Such a difference , however , was not apparent ( compare Fig 6B , 6C , 6E and 6F; S4B , S4C , S4E and S4F Fig ) . Along this line , measurements made in cortical neurons suggested that release properties of presynaptic sites formed by the same axons on the same neurons are remarkably similar , even when such synapses are formed on different dendrites of target neurons ( a phenomenon referred to as Normalization of Release Probability; [42] ) . Perhaps more importantly , however , here ( Fig 3 ) and elsewhere ( e . g . , [5 , 6 , 11 , 13 , 66] ) , it was shown that synaptic remodeling continues at significant rates even when activity and/or synaptic transmission is blocked . It therefore seems more likely that the modest remodeling covariance observed for synapses belonging to the same CI pairs is due to spontaneous remodeling processes occurring autonomously at each synapse , independent of activity , specific or otherwise . In fact , we suspect that the variability of presynaptic functional parameters observed in studies such as those mentioned above might be the outcome , rather than the cause , of such spontaneous remodeling processes . This would not be surprising , given the strong coupling between PSD and active zone remodeling ( e . g . , [13 , 18 , 67 , 68]; see also [52 , 59 , 69] ) . Moreover , the skewed ( heavy tailed ) distributions of presynaptic properties [61–65 , 69] as well as previously reported features of active zone remodeling dynamics [13 , 16] are archetypical hallmarks of a stochastic process known as the Kesten process , which was previously shown to capture the spontaneous remodeling processes of excitatory [16] and inhibitory [17] synapses . The fourth concerns the assumption that the differences between CI and non-CI synapse covariance stemmed entirely from differences in commonality of activation histories . Even in modular networks , however , some network-wide bursts did spread from one module to the other , suggesting ( as elaborated on above ) that activity histories of non-CI synapses were not entirely dissimilar . This might have led us to overestimate the covariance contributed by ( postsynaptic ) neuron/dendrite-wide , nonspecific processes ( non-CI synapse remodeling; gray regions in Fig 8E ) . We note , however , that the very low values of these estimates ( 7% to 8% ) leaves little room for lowering them further . Conversely , the experiments described in Fig 3 and S1 Fig hint that , even in inactive networks , remodeling covariance of CI synapses might still slightly exceed that of non-CI synapses . It thus remains possible that some of the covariance exhibited by CI synapses reflects contributions of factors other than common activity histories . Thus , for example , spontaneous neurotransmitter release from presynaptic boutons belonging to the same axons could be coordinated by a variety of intra-axonal processes , such as molecular and synaptic vesicle interchange [2 , 70] . Similarly , processes acting extracellularly ( such as receptor spillover and retrograde messengers ) might act preferably on CI synapses even in the absence of overt activity . Consequently , we may have overestimated the contributions of specific activity history-dependent processes to synaptic remodeling ( Fig 8E , green sectors ) . It is important to note , however , that neither of these deviations from the underlying assumptions would affect our estimations of the largest component , that is , synapse-autonomous , spontaneous remodeling processes ( Fig 8E , blue sectors ) , and , thus , these deviations were unlikely to significantly impact the main conclusions of this study . Finally , it should be noted that our experiments were carried out in networks of dissociated rat cortical neurons in primary culture . In the context of this study , the system was advantageous not only because of the experimental access it provided but also because it allowed us to focus on activity history dependence of synaptic remodeling in a manner free from other influences such as neuromodulation . Yet , it might be asked to what degree the conclusions reached here apply beyond our experimental system . As described in the introduction , the observation that spine volumes and PSD sizes fluctuate in the intact brain is well documented . Where such observations are concerned , however , it remains unknown what fraction of these fluctuations represents bona fide history-dependent synaptic remodeling and which represents other , possibly stochastic , processes . Nevertheless , we note that history-dependent remodeling processes should ultimately control synaptic size , and when sizes of synapses with apparently identical histories are compared , their sizes correlate quite poorly , not only in our data but in the intact mouse neocortex as well ( Fig 8; [21] ) . A similar finding emerges from another recent electron microscopy study for a much smaller data set ( 17 pairs of CI synapses from adult rat hippocampi [22] ) . Here , it was found that , on average , within each CI pair , spine volumes and PSD areas differed by factors of ~2 and ~3 , respectively . We thus cautiously suggest that our findings , obtained in culture , might apply to the intact brain as well , although the actual fraction of activity history-dependent remodeling might differ somewhat and vary , perhaps , according to behavioral state . The assertion that synaptic strength is defined by the history of pre- and postsynaptic activity is one of the oldest , yet widely accepted tenets of contemporary neuroscience [71 , 72] . One facet of this assertion concerns activity-dependent structural plasticity of synaptic connections , including changes in the sizes of existing synapses [73] . Indeed , the capacity of particular activity paradigms to drive excitatory synapse enlargement ( and shrinkage ) is now well established ( reviewed in [23] ) . The stimulation paradigms used in such studies , however , are typically brief and artificial ( e . g . , tetanic or theta burst stimulation , glutamate uncaging in low extracellular Mg2+ , sometimes in the presence of various pharmacological agonists ) . In contrast , relationships between histories of protracted , more natural activity forms and synapse remodeling are less established . In the current study , we show that sizes of synapses with shared activity histories co-vary more than sizes of synapses with different activity histories ( Figs 3 and 6 , S1 and S4 Figs ) , thus demonstrating that histories of spontaneously occurring activity forms can significantly affect synaptic remodeling as well . Somewhat surprisingly , our data also suggest that the contributions of shared activity histories and the contributions of spontaneous processes to synaptic size remodeling are of comparable magnitudes . It might be argued that other activity regimes , which differ from those present in the networks studied here , might be more effective in controlling synaptic sizes . Had this been the case , however , it might have been expected that sizes of CI synapses would be more similar in vivo as compared to the situation in culture , given that activity regimes in vivo are richer and more physiologically relevant . Contrary to these expectations , however , sizes of CI synapses in vivo were no more similar than the sizes of CI synapses in culture ( r = 0 . 23; Fig 8 ) . It might be further argued that this poor correlation is attributable to local details such as bouton to bouton variability , as described above . We note , however , that such sensitivity to local details would further undermine the notion that predictable relationships exist between synaptic remodeling and particular histories of pre- and postsynaptic activities . We thus suspect that , regardless of activity regime , the governance of synapse remodeling by particular activity histories is partial at best . In this respect , it is worth noting that the magnitude of PSD enlargement induced by the aforementioned experimental stimuli ( 50% or less; e . g . , [18 , 19 , 74] ) is not that different from the magnitude of spontaneously occurring changes in PSD sizes observed in the intact cortex of mice merely maintained in their home cages ( 44% on average; [15] ) . It is also worth noting that both covariance measures used here ( i . e . , Pearson’s correlation and , even more so , Spearman’s rank correlation ) only quantify similarities in the trends of synaptic remodeling but are quite indifferent to the similarities in the absolute magnitudes of such remodeling . Thus , the governance of synapse remodeling by particular activity histories might be even more limited than our estimates indicate . How then can one reconcile the overwhelming evidence for activity history-dependent synapse remodeling with such a significant degree of spontaneous remodeling ? How can persistent functions be embedded in neuronal networks if directed and spontaneous changes in synaptic sizes are of similar magnitude ? This experimental and conceptual gap might be partially bridged by considering the following matters . The first matter concerns the growing appreciation that synaptic plasticity is affected or even gated by various neuromodulatory systems [72 , 75 , 76] . The absence of neuromodulatory systems in the networks used here was useful to examine the net contributions of particular activity histories; yet , in the intact brain , timed neuromodulator release might significantly enhance the contributions of specific activity histories and thus minimize the relative contributions of spontaneous remodeling , at least during behaviorally important time windows . We note once again , however , that this entails an expectation that CI synapse sizes would be quite similar in vivo , an expectation that is not matched . A second matter concerns the fact that functional connections between neurons are often based on multiple synapses ( reviewed in [20] ) , and , thus , activity history-independent fluctuations at individual synapses might average out at the level of neuron-to-neuron connections . Furthermore , changes in connection strength might be most reliably modified by increasing or decreasing the number of synapses connecting two neurons ( e . g . [21]; see also [77] ) . Indeed , it has recently been shown that numbers of synapses formed between particular axons and dendrites are very different from what might be expected by chance [21 , 44] . It remains to be seen , however , if the time course over which synapses are added/removed , the actual numbers of such synapses , and the signal-to-noise ratios of multiple synapse connections can satisfactorily address the discordance described above . In this regard , it worth noting a recent in vivo ( mouse ) study in which basal rates of spine formation and loss were found to be almost unaffected by chronic blockade of calcium channels and N-methyl-D-aspartate ( NMDA ) receptors [66] . A third matter to consider is the possibility that persistent changes in network function involve vast numbers of synapses and neurons such that fluctuations at the individual synapse level are mitigated by massive redundancy [78] or rendered insignificant by the sheer numbers of synapses involved . Indeed , a recent study provided evidence suggesting that the acquisition of a new motor skill in mice involves about 4 , 700 motor cortex neurons and about 410 , 000 synapses [79] . In this regard , it is interesting to note that in his influential monograph , Hebb [73] considered this matter and suggested that , although stochastic processes might preclude predictable actions in small parts of the system , statistical constancies might emerge in larger systems . Indeed , when large numbers of synapses are followed over time , their remodeling dynamics do seem to obey certain well-defined statistical rules [5 , 8 , 16] . A final matter to consider is the possibility that stochastic changes in synaptic properties are crucially important components in the organization of network learning , as they enable networks to explore and sample synaptic configurations for those most congruent with input from the external world or with desired functions [80] . This recent study suggests that changes in synaptic weights are driven not only by deterministic , activity-dependent rules ( and biological constraints ) but also by stochastic processes , which dramatically improve the ability of networks to generalize and compensate for unforeseen changes . Within this context , our finding that the magnitudes of deterministic and stochastic components are comparable would seem to suggest that the contribution of exploratory processes is at least as significant as the contribution of deterministic processes , lending further support to this emerging view of synaptic plasticity .
All experiments were performed in primary cultures of rat neurons prepared according to a protocol approved by the "Technion , Israel Institute of Technology Committee for the Supervision of Animal Experiments" ( ethics approval number IL-019-01-13 ) . The thin glass MEAs ( MultiChannelSystems—MCS , Germany ) used here for monolithic networks contain 59 flat , round electrodes made of titanium nitride arranged in an 8 x 8 array with an inter-electrode spacing of 200 μm . In this arrangement , the corner electrodes are missing , and one of the leads is connected to a large reference ( ground ) electrode . Although the recording and reference electrodes are opaque , the very thin glass ( 180 μm ) substrate and the Indium Tin Oxide leads are fully transparent , allowing excellent optical access to the cells growing on the array . Modular MEAs were prepared using 4Q , commercially available MEAs ( MultiChannelSystems ) fabricated to our request on thin glass . Apart from their layout , 4Q MEAs used here were identical to the thin glass MEAs described above . A PDMS insert was sealed onto the MEA surface , effectively dividing the MEA into two modules , separated by a number of thin channels ( similar to the method described in [38] ) . The PDMS inserts were made using a silicon mold microfabricated using standard , single-layered SU8 photolithography techniques [81 , 82] . Briefly , SU-8 2002 ( Microchem , Inc . ) was spun on a 4-inch silicon wafer at a nominal thickness of 3 μm , baked , exposed with a dark-field transparency channel mask , baked again , and developed . Each mold had multiple barrier patterns with channel numbers ranging from 6 to 12 ( 3 μm x 13 μm x 400 μm; H x W x L ) . The mold was silanized ( [tridecafluoro-1 , 1 , 2 , 2-tetrahydroocytl]-1-trichlorosilane evaporated for 1h in vacuum ) to allow easier release and slowly filled with PDMS silicone rubber ( Sylgard 184; 10:1 ratio of pre-polymer [base]: cross-linker [curing agent]; Dow-Corning , Midland , Michigan ) , to 2-mm height and de-gassed in a vacuum desiccator . Once the PDMS spread over the entire wafer , it was cured for 3h at 65°C . Following curing , 17-mm diameter circular barriers were cut , and two 5-mm-wide wells were punched on each side of the channels; the finalized inserts were then stored for future use . On the day of cell culture preparation , each barrier was aligned to the electrodes of pre-coated 4Q MEA dishes ( see below ) using a drop of 70% ethanol and heated for 2h at 54°C to allow ethanol evaporation and PDMS sealing . Finally , the dishes were cooled to 37°C in a cell culture incubator . Primary cultures of rat cortical neurons were prepared as described previously [6] . Briefly , cortices of 1–2-d-old Wistar rats of either sex were dissected and dissociated by trypsin treatment followed by trituration using a siliconized Pasteur pipette . For monolithic cultures , a total of 1–1 . 5 x 106 cells were plated onto thin-glass MEA dishes , the surfaces of which had been pretreated with polyethylenimine ( PEI , Sigma ) to facilitate cell adherence . Modular cultures were prepared on 4Q thin glass MEAs described above ( see also [37] ) as follows: 100 μl aliquots of cells in suspension ( at 1–1 . 5 x 106/ml cells ) were infected with predetermined amounts of viruses and incubated for 2 h in a tissue culture incubator at 37°C . Following the incubation , the infected cells were spun down for 60 s at 2 , 000 g , and 60 μl of the supernatant were replaced with pre-warmed culture medium , and the cells were resuspended by gentle pipetteation . The process was repeated two more times ( three washes in total ) . After the third spin down and resuspension , the cells were pipetted thoroughly , and 20–25 μl of cells in suspension were seeded in their respective module . No contact was allowed between the two droplets . 160 μl of uninfected cells at similar concentrations were seeded dropwise at the dish perimeter ( outside the PDMS barrier ) to enrich the environment with diffusive nutritional factors . Dishes with droplets were put in 10-cm petri dishes containing small vessels with water ( to maximize humidity ) and incubated overnight in a humidified tissue culture incubator at 37°C in a gas mixture of 5% CO2 , 95% air . The next morning , 2 ml of culture medium were added to each dish . Both uniform and modular preparations were kept in a humidified tissue culture incubator and grown in medium containing minimal essential medium ( MEM , Sigma ) , 25 mg/l insulin ( Sigma ) , 20 mM glucose ( Sigma ) , 2 mM L-glutamine ( Sigma ) , 5 mg/ml gentamycin sulfate ( Sigma ) , and 10% NuSerum ( Becton Dickinson Labware ) . Half of the volume was replaced three times a week with feeding medium similar to the medium described above but devoid of NuSerum , containing a lower L-glutamine concentration ( 0 . 5 mM ) and 2% B27 supplement ( Invitrogen ) . All DNA constructs ( except GCaMP6s; see below ) were introduced into neurons using third generation lentiviral expression vectors based on the FUGW backbone [83] . The construct used for expressing PSD-95:EGFP ( FU-PSD-95:EGFP-W ) was described in detail in [6] . The construct used to express Cer:SV2 ( FU-Cer:SV2a-Wm ) was made as follows: FUGW was modified to FUGWm by moving the XhoI site from the 3’ to the 5’ side of the woodchuck hepatitis post-transcriptional regulatory element ( WPRE ) . Cerulean [30] , flanked with AgeI ( 5' ) and BsrGI ( 3' ) sites , was synthesized de novo and inserted into FUGWm instead of EGFP using the AgeI and BsrGI sites , resulting in the interim construct FUCWm . SV2a was then cut out of FU-EGFP:SV2a ( a generous gift by Craig C . Garner; [84] ) using BsrGI ( 5’ ) and XhoI ( 3’ ) sites and inserted into FUCWm , resulting in FU-Cer:SV2a-Wm . Sequencing confirmed 100% identity with Rattus norvegicus SV2A ( GenBank accession: L01788 . 1 ) . The construct used to express PSD-95:mTurq2 was made as follows: Large-scale gene synthesis was used to synthesize a fusion of PSD-95 and mTurquoise [85] flanked by of AgeI ( 5’ ) and EcoRI ( 3’ ) as detailed in [13] , and this segment was inserted into FUGWm instead of EGFP using the AgeI and EcoRI sites . A point mutation was then inserted to convert mTurquoise into mTurquoise2 ( Isoleucine to Phenylalanine; [40] ) . Sequencing confirmed 100% identity with Rattus norvegicus discs large homolog 4 ( NM_019621 . 1 ) . All cloning and gene synthesis was done by Genscript ( Piscataway NJ , US ) . The construct used to express EGFP:SynI ( FU-Syn:EGFP-W ) was provided as a generous gift by Craig C . Garner [86] . GCaMP6s [41] was expressed using an Adeno Associated Viral ( AAV ) vector obtained from the Penn Vector Core ( University of Pennsylvania ) . Lentiviral particles were produced in house as previously described [17] . Briefly , HEK293T cells were transfected using Lipofectamine 2000 ( Invitrogen ) , a mixture of the three ViraPower kit packaging plasmids ( Invitrogen ) , and the expression vector . Lentiviral stocks were prepared by collecting the supernatant after 48 h , filtering it using 0 . 45-μm filters , and storing it as small aliquots at -80°C . Transduction of monolithic cortical cultures was performed at 5 d in vitro by adding 10–20 μl of lentiviral stock solution to each MEA dish . Transduction of modular cultures was performed as described above . MEA network activity was recorded using a commercial 60-channel headstage ( Inverted A1060 , MCS ) . Signals were first amplified by the internal headstage amplifier ( 1024x ) , multiplexed into 16 channels , amplified further ( x10 ) by a 16-channel amplifier ( Alligator technologies , US ) , and then digitized by an A/D converter ( Microstar Laboratories , US ) at 12 KSamples/sec per channel . Software used for data acquisition and display was based on AlphaMap ( Alpha-Omega , Israel ) . Spiking activity data were stored as threshold crossing events ( threshold = -40 μV ) and analyzed offline using custom scripts written within the Matlab ( MathWorks , US ) programming environment . Fluorescence and brightfield images were acquired using a “homemade” confocal laser scanning microscope built around a Zeiss Axio Observer Z1 . All imaging was carried out using a 40× , 1 . 3 N . A . Fluar objective ( Zeiss ) . The system , controlled by software written by one of us ( NEZ ) , allows for automated , multisite time-lapse microscopy . The MEA headstage described above was attached to the system’s motorized stage ( Märzhäuser Wetzlar , Germany ) , and the MEA dishes were placed firmly within it . PSD-95:mTurq2 and Cer:SV2 were excited using a 457-nm solid state laser ( Cobolt , Sweden ) . PSD-95:EGFP , EGFP:SynI , and GCaMP6s were excited using a 488-nm solid state laser ( Coherent , US ) . Fluorescence emissions were filtered through 467–493-nm and 500–550-nm bandpass filters ( Semrock , US and Chroma Technology , US ) . Laser intensity modulation of the 457-nm solid state laser in experiments such as those described in Fig 7 was performed using the digital interface and software provided by the manufacturer . Time lapse recordings were typically performed by averaging five frames collected at 10–11 focal planes ( 0 . 9 μm apart ) . Images were collected at a resolution of 640 x 480 pixels , 12 bits/pixel . The confocal aperture was kept fully open to minimize illumination intensities . The software-controlled motorized stage was used to collect data sequentially from up to 12 predefined locations . PSD-95:EGFP was imaged at 30–60-min intervals and Cer:SV2 at 7 . 5-h intervals . PSD-95:mTurq2 was imaged at 1-h intervals and EGFP:SynI at 1–3-h intervals . Focal drift was corrected before collecting data from each location by automatically locating the glass/medium interface plane and moving the focal position to a user-defined offset above this plane . GCaMP6s transduced axons were imaged for ~1 min ( 600 frames , ~130 msec per frame ) using a cooled EMCCD ( Andor ) controlled by custom written software . To maintain neuronal network viability , the MEA dishes were covered with a “cap” equipped with ports through which sterile air mixtures and perfusion media were introduced and removed [6 , 17 , 24] . In addition , the cap was equipped with a dipping reference electrode made of thin platinum wire and a removable transparent glass window . The preparations were continuously perfused with feeding media at a rate of 2 ml/day using silicone tubes connected to the cap through the aforementioned ports and an ultra-slow peristaltic pump ( Instech Laboratories Inc . , US ) . In addition , a 95% air/5% CO2 sterile mixture was streamed continuously into the dish at rates regulated by a high-precision flow meter ( Gilmont Instruments , US ) . The MEA dishes were heated to 36–37°C by the heating base at the bottom of the headstage/amplifier and by a custom objective heater as previously described [17] . To minimize perturbations , all pharmacological agents were added to 100 μl media drawn from the MEA dish by temporarily removing the aforementioned caps glass window . The media was then returned and mixed gently using a sterile pipette , followed by returning the removable glass window . The same reagents were then added to the perfusion media at identical final concentrations , which were 1 μM for TTX; ( Alomone Labs ) and 20 μM for CCh ( Sigma ) . Analysis of imaging data was performed using an application ( “OpenView” ) written by one of us ( NEZ ) . This application provides features for automated tracking of punctate fluorescent spots in time series of multiple images and the quantification of their fluorescence over time ( see [24] for further details ) . 9 × 9 pixel ( ~1 . 3 x 1 . 3 μm ) regions of interest ( “boxes” ) were centered on postsynaptic puncta , and average pixel intensities within these boxes were obtained from maximal intensity projections of all focal ( Z ) sections . As the reliability of automatic tracking was not absolutely perfect , all tracking was verified and , whenever necessary , corrected manually . Puncta for which tracking was ambiguous were excluded . Concomitant juxtaposition of marked presynaptic puncta was verified at every relevant time point and Z section . Whenever presynaptic puncta disappeared ( even for a single time point ) or became separated from their putative postsynaptic counterpart ( both in XY or Z plane ) , the data for this synapse were excluded . Identification of CI synapses as such was limited to short , relatively straight axonal stretches , which did not intersect with other axons within the short stretch . To further facilitate CI disambiguation , low-magnification images of the imaged areas were collected for the purpose of resolving the branching structure of labeled axons and determining if axonal segments could be traced back to common origins . By keeping axonal labeling as sparse as possible , these procedures allowed for high-confidence CI synapse identification . Analysis of GCaMP6 time series was performed by first averaging four frames obtained between network bursts and thereafter subtracting these images from all images in the time series . GCaMP6 fluorescence was then quantified using OpenView as described above . Covariance of CI synapses was calculated after smoothing PSD-95:EGFP or PSD-95:mTurq2 data with a 2 . 5- to 3-h low-pass filter ( depending on imaging frequency ) . For CI synapse pairs ( Figs 3 and 4 and 6D–6F , S4D–S4F , S5C and S5D and S6C and S6D Figs ) , covariance of CI synapses was calculated for the two synapses belonging to each pair , whereas covariance for non-CI synapses was calculated for CI1 to Ref1 , CI2 to Ref2 , CI1 to Ref2 , and CI2 to Ref1 ( four comparisons ) to minimize potential effects of inter-synaptic distance . For multiple CI synapses formed between one axon and any dendrite ( Fig 6A–6C , S4A–S4C , S5A and S5B and S6A and S6B Figs ) , covariance values for all possible CI pairs and CI-Ref pairs were calculated . Pearson’s and Spearman’s covariance values were calculated using Matlab and Microsoft Excel ( using the Real Statistics Resource Pack; http://www . real-statistics . com ) . Data compilation , statistical testing , and plotting were performed using Microsoft Excel ( and Real Statistics ) . Image examples ( Figs 2 , 5 and 7 and S2 Fig ) were prepared using OpenView and Adobe Photoshop . Final figures were prepared using Microsoft PowerPoint . Sampled raw activity measurements were analyzed using custom written scripts in Matlab . Briefly , specific algorithms were used to identify bursting activity in each module ( defined as activity in at least 25% of active electrodes in the module during 300-msec windows ) . A successful propagation of a burst from one module to the other was defined as a burst initiated in one of the modules followed by the appearance of a burst in the second module with a delay of no more than 50 msec between first spikes in each burst . The inter-modular synchronization measure S was calculated as S= ( BjB1+B2+Bj ) where Bj is the number of joint bursts , and B1 and B2 are the number of bursts in modules #1 and #2 , respectively , which did not propagate into the second module . | The modification of synaptic connections by specific activity histories ( a phenomenon known as synaptic plasticity ) is widely believed to represent a major substrate of processes collectively referred to as learning and memory . Recent studies indicate , however , that synapses also change spontaneously , even in the absence of specific activity histories—or , for that matter , any activity whatsoever . This raises a fundamental question: how do changes directed by specific activity histories quantitatively compare to spontaneous changes in synaptic properties ? Put differently—what is the “signal-to-noise ratio” of synaptic plasticity at individual synapses ? To address this question we followed—over several days—pairs of synapses formed between the same neurons under the assumption that their common activity histories should drive similar changes in their sizes . Indeed , sizes of such synapses tended to change in a correlated manner; yet the extent of this correlation was surprisingly modest , accounting for less than half of the changes that such synapses exhibited . Moreover , sizes of synapses with apparently common activity histories tended to be quite different . Our findings thus indicate that the “signal-to-noise ratio” of synapse remodeling might be rather poor , on the order of 1:1 or less . | [
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"neuron... | 2016 | Relative Contributions of Specific Activity Histories and Spontaneous Processes to Size Remodeling of Glutamatergic Synapses |
Conservation priority-setting schemes have not yet combined geographic priorities with a framework that can guide the allocation of funds among alternate conservation actions that address specific threats . We develop such a framework , and apply it to 17 of the world's 39 Mediterranean ecoregions . This framework offers an improvement over approaches that only focus on land purchase or species richness and do not account for threats . We discover that one could protect many more plant and vertebrate species by investing in a sequence of conservation actions targeted towards specific threats , such as invasive species control , land acquisition , and off-reserve management , than by relying solely on acquiring land for protected areas . Applying this new framework will ensure investment in actions that provide the most cost-effective outcomes for biodiversity conservation . This will help to minimise the misallocation of scarce conservation resources .
Many sophisticated approaches exist for identifying priority areas for conservation at a global scale . These “biodiversity hotspots” or “crisis ecoregions” are typically identified using data on endemic species richness , total biodiversity , and past habitat conversion [1–3] . With few exceptions , these approaches neglect economic costs and provide a static assessment of conservation priorities . They therefore cannot provide guidance on how funds should be distributed between regions , nor can they inform when the funds should be spent . Recent theoretical advances incorporate economic considerations and landscape dynamics into priority-setting , and provide an analytical framework for deciding where , when , and how much money should be invested for biodiversity conservation [4–8] . While these theoretical advances incorporate economic considerations , they treat land acquisition , or the creation of protected areas , as a surrogate for the broader suite of actions available to protect biodiversity . Conservation practitioners routinely invest in a diverse array of activities such as fire management , invasive species control , and revegetation , with the aim of enhancing or sustaining biodiversity . In many places land acquisition is not feasible , and neither appropriate nor affordable . In addition , the spatial extent of many threats is usually greater than the area of land that can be acquired . A framework is urgently needed that can support the more sophisticated funding allocation decisions required from conservation practitioners . Such a framework could help to allocate limited conservation funds to threat-specific conservation actions in areas where they are likely to achieve the greatest potential biodiversity benefit . Here , we develop an action- and area-specific framework for conservation investment and illustrate its application using Mediterranean-type habitats ( Figure 1 ) . Mediterranean ecoregions boast exceptional species diversity but are poorly protected , highly degraded , and exposed to multiple persistent threats [9–13] . Consequently , they have been ranked among the world's highest conservation priorities [3 , 14 , 15] . How might funds be allocated to conserve Mediterranean ecoregions in the most cost-effective way ? To apply our framework ( Figure 2 and see Materials and Methods ) we require an explicit statement of the overall conservation objective and the budget ( steps 1 and 2 of Figure 2 ) , and an understanding of the threats operating in each ecoregion and the potential conservation actions to abate them ( steps 3 and 4 ) . Our objective is to maximise the total number of species ( vascular plants and vertebrates combined ) conserved across these ecoregions , through strategic investment in a suite of conservation actions , given a fixed annual budget . The amount of money allocated annually to each conservation action in each ecoregion depends on the area of land currently receiving and requiring the action , the cost of the action per unit area , and the biodiversity benefited by the investment ( the number of plant and vertebrate species predicted to persist in an ecoregion after investment in a conservation action; see Materials and Methods ) . Our aim is to develop investment schedules for Mediterranean ecoregions that reflect the relative returns from investing in different conservation actions in order to maximise our objective ( step 5 of Figure 2 ) . We deliver investment priorities that change through time depending on the cumulative impacts of investments ( step 6 ) . While we address a global-scale problem , our framework and analytical approach is also applicable at national and regional scales .
We apply our framework to the 17 ( of 39 ) terrestrial Mediterranean ecoregions for which data are most readily accessible . This subset of Mediterranean ecoregions covers parts of Australia ( ten ecoregions; Table 1 ) , Chile ( one ecoregion; Table 2 ) , South Africa ( three ecoregions; Table 3 ) , and California and Baja California ( three ecoregions; Table 4 ) ( Figure 1 ) . Although we recognise that alternative delineations of Mediterranean habitats are available , we employ the delineations provided by the World Wildlife Fund , given their utility for global-scale analyses . Through consultation with regional experts , we identify the key threats in each ecoregion to achieving our objective of maximising the total number of species ( in our case , vascular plants and vertebrates combined ) conserved given an annual budget of US$100 million ( steps 1–3 of Figure 2 ) . We also identified the actions undertaken to abate these threats ( step 4 ) . Hereafter we term each ecoregion–conservation action combination an “ecoaction” . We assume that the impact of each ecoaction is independent . Through a combination of expert input , literature review , and analysis of regional datasets in geographic information systems we determine the areas requiring , and already receiving , each ecoaction and estimate the cost associated with its implementation ( steps 4a–4c of Figure 2; Text S1 ) . While the costs incurred for some conservation actions , such as land acquisition or revegetation , are one-time costs , the costs of other actions , such as invasive predator control , are incurred annually . To convert the latter to one-time costs , we endow the annual cost over 20 y ( unless otherwise stated ) , after which further funds are required for these conservation actions to continue . We determine the endowed value by calculating the net present value over the timeframe of interest , assuming an inflation rate of 3 . 2% and discount rate of 6 . 04% . This discount rate is equivalent to a 10-y US government bond rate , and the inflation rate represents that of the US dollar in 2005 . We account for the costs of ongoing management for ecoactions that involve land acquisition and for the costs of establishing agreements with private landholders ( if such investments are considered necessary for an ecoaction to proceed or to be long-lasting; Text S1 ) . The cost of each ecoaction is based on the perceived expenditure required for successful interventions , and we therefore assume that investment in each ecoaction will prevent the local extinction of species at risk from the relevant threat . In this paper , the number of species benefited by each ecoaction—its “biodiversity benefit”—is the number of plant and vertebrate species predicted to persist in an ecoregion after investment in the ecoaction ( step 4d of Figure 2 ) . If appropriate data were available for each ecoaction , we could modify the predicted biodiversity benefit by the likelihood that the ecoaction will succeed in abating the relevant threat . To operationalise our approach we need a functional form for the relationship between investment in an ecoaction and its biodiversity benefit . Every investment shows diminishing returns and therefore we assume that the marginal benefit of investment in a particular ecoaction decreases as the size of the investment increases . We represent diminishing returns using the functional form of the species–area relationship , where the total number of species ( S ) present in area A is a power-law function of that area [16]: We calculate the constant α by dividing the total number of species in the ecoregion by the estimated area of original habitat , after raising this area to the power of z ( see Text S2 ) . In the baseline scenario we assign z a value of 0 . 2 , a typical value for terrestrial , non-island regions [16] . We therefore assume that the incremental number of species protected with a given increase in area protected follows the form of a standard species–area curve . When we account for the cost of each ecoaction , we simply replace the area protected by the cost of protecting the equivalent area ( to generate a species–investment curve; step 5a of Figure 2 ) . This relationship is straightforward for habitat protection or restoration , but requires further thought for the diverse array of conservation actions considered here . The adaptation of species–area curves to conservation actions other than reserving or restoring land is based on the premise that investment in these actions will also exhibit diminishing returns . The major refinements required are that the area of “protected” habitat is the area of investment in each ecoaction ( each with a pre-specified cost ) and the number of species protected is threat-specific . Since we currently do not have an ecological basis for an alternative parameterisation of this relationship for the range of ecoactions considered here , we evaluate the sensitivity of the allocation schedules to the value of z . We choose z randomly from a uniform distribution ( between 0 . 1 and 0 . 4 , n = 30 ) to reflect the uncertainty about the relationship between the number of species protected and the amount of money invested in each ecoaction , specifically , the rate at which the returns from investment diminish . To determine the biodiversity benefit of an ecoaction that abates a specific threat we consider only those species impacted by that threat . We calculate the number of “at risk” species by determining the proportion of plant and vertebrate species regarded as threatened by each type of threat ( using the World Conservation Union [IUCN] Red List for each country [17] and excluding those species that are of least concern or data deficient ) , and multiply this proportion by the total number of plant and vertebrate species occurring in each ecoregion [18 , 19] . Thus we assume that the proportion of species that would be protected by investment in a particular ecoaction is the same as the proportion of IUCN-listed species identified nationally as being at risk from the relevant threat , and that the species in this subset benefit equally from an investment . For invasive predator control in Australia , we limit the biodiversity benefit calculation to just vertebrates by restricting the IUCN search to vertebrate species and multiplying this proportion by the number of vertebrate species occurring in each ecoregion . Obtaining an optimal allocation schedule through time amongst such a large number of ecoactions is computationally intractable [4 , 5] , so we adopt a “rule of thumb” , or heuristic , to approximate the optimal investment schedule . This heuristic , which we term “maximise short-term gain” , directs funds each year to ecoactions that provide the greatest short-term increase in biodiversity benefit per dollar invested ( steps 5b and 5c of Figure 2 ) . Using this heuristic we generate an investment schedule over 20 y , given a fixed annual budget of US$100 million ( step 6 of Figure 2 ) . We assess the sensitivity of the investment schedule to the budget size by repeating the analysis with the annual budget reduced to US$10 million . We use the Spearman coefficient of rank correlation to compare the priority rankings based on the ecoaction-specific framework to those based on a ranking of vertebrate species richness [20] . Because of lack of independence , we test significance against the distributions of Spearman values derived from 100 , 000 random pairings of X and Y variables . The null hypothesis is that the observed coefficient is zero , or the distribution of Y is the same for all values of X [21] . We also compare the ecoaction-specific framework to a simplified model of conservation that focuses only on land acquisition for the creation of protected areas . In this analysis , we estimate the cost of land acquisition using a statistical model ( Table S1 ) and the area requiring acquisition as the area of natural habitat that is currently unprotected ( IUCN status I–IV ) . We estimate the biodiversity benefit of this action as the total number of plant and vertebrate species . Therefore , while the biodiversity benefit under the ecoaction-specific framework is a proportion of the total species richness ( those threatened by each threat type ) , the biodiversity benefit under the acquisition-only framework is the total richness of vertebrate and plant species . As with the ecoaction approach , we assume diminishing returns with cumulative investment and model this relationship using species–investment curves . For the acquisition-only approach we rescale the annual budget to US$148 million dollars ( from US$100 million ) , since the overall cost of achieving our objective under this scenario is 48% greater .
We illustrate how these three factors interact within our conservation investment framework ( steps 4–6 of Figure 2 ) using a regional-scale case study from the Swan Coastal Plain scrub and woodlands ecoregion of Australia ( Figure 3 ) . The curve to the left of the circles indicates the total area of conservation interest ( indicated by the squares ) , that is , the area already receiving the actions ( step 4a ) , and the area requiring them ( step 4b ) . For example , the area of interest for invasive predator control in this ecoregion is about 5 , 832 km2 ( see Text S1 ) . The curve to the right of the circles acknowledges that the persistence of species depends on past changes to habitat and that knowledge of the distribution of species is uncertain ( see Text S2 ) . We estimated the original extent of habitat in this ecoregion to be approximately 15 , 210 km2 and that this area supported a total of 565 vertebrate and plant species now at risk due to habitat fragmentation , 256 plant and vertebrate species now at risk from a soil-borne pseudo-fungus , Phytophthora cinnamomi , and 143 vertebrate species now at risk due to invasive predators ( step 4d of Figure 2 ) . In each case , the total number of species estimated to be at risk is represented by the right-hand endpoint of the species–area curves ( Figure 3A ) . Assuming the costs of undertaking the different actions is the same , we determined that conducting invasive predator control or revegetation over an additional 200 km2 in the Swan Coastal Plain ecoregion will potentially protect three and four species , respectively ( panels II and III of Figure 3B ) . Conducting Phytophthora management over the same area has the potential to protect 108 species because the area of conservation interest lies in the steepest part of the species–area curve ( Text S1; Table 1; panel I of Figure 3B ) . When we modelled the relationship between the biodiversity benefit and dollars invested using species–investment curves ( step 5a of Figure 2; Figure 3C ) , we found that the cost-effectiveness of each action varies widely ( step 5b ) . Revegetation in the Swan Coastal Plain ecoregion costs US$301 , 118 per square kilometre , Phytophthora management costs US$514 , 626 per square kilometre , and invasive predator control costs US$7 , 125 per square kilometre ( Table 1 ) . Phytophthora management was still the most cost-effective action: US$2 million spent on this action will potentially protect 49 species , although the potential benefit reduces rapidly with cumulative investment ( panel I of Figure 3D ) . An initial expenditure of US$2 million on invasive predator control in the Swan Coastal Plain ecoregion has the potential to protect four species , whereas there is negligible benefit from spending US$2 million on revegetation ( panels II and III of Figure 3D ) . The comparatively greater marginal returns from investing in invasive predator control are due to its low cost , despite the fact that the direct biodiversity benefit for this action is restricted to vertebrates . Based on this analysis , initial investment within the Swan Coastal Plain scrub and woodlands ecoregion is prioritised to Phythopthora management ( step 5c of Figure 2 ) . The species–investment curves are then updated given changes in the area receiving the conservation action and the area requiring the conservation action ( step 6 ) . In the next time step , the budget is allocated to the conservation action that now maximises the biodiversity benefit per dollar invested . This regional case study therefore illustrates how the species–investment curves are constructed , and how the actions are prioritised for investment at each time step based on their cost and biodiversity benefits , and the current level of investment in each conservation action . In applying the conservation investment framework at the global scale we encompass a greater mix of threats and candidate conservation actions . Across all 17 ecoregions , only 24 ecoactions ( of the 51 possible ) received investment in the model during the first 5 y . During this time , most funds were allocated to land protection and management ( through land acquisition , off-reserve management , and on-going management ) in the three South African ecoregions ( 66% of the total budget , six ecoactions in total; Table 5 ) . Much of the remaining funds were allocated to invasive plant control in the Chilean ecoregion , the three South African ecoregions , and one of the Californian/Baja Californian ecoregions ( 24% of the total budget; five ecoactions in total; Table 5 ) . These conservation actions yielded the greatest marginal return on investment over 5 y because the potential biodiversity benefit is high and the costs are comparatively low . Over 5 y the greatest amount of money ( 21% of the total budget ) is allocated to land protection and management ( through land acquisition , off-reserve management , and on-going management ) to abate agricultural conversion in the montane fynbos and renosterveld ecoregion of South Africa . This broad ecoregion contains a large area of arable land that is unconverted but largely unprotected . Furthermore , the potential biodiversity benefit of abating agricultural conversion in this region is high , while the cost of this ecoaction is comparatively low ( Table 3 ) . Beyond the first 5 y , we see additional ecoactions prioritised for investment because further investments in initially selected ecoactions exhibit diminishing returns . Consequently , as one moves from a 5-y to a 20-y timeframe , the number of ecoactions identified for investment increases from 24 to 30 , despite a 4-fold increase in the funds available . The greatest investment over 20 y is directed , in equal proportions to the montane and lowland fynbos and renosterveld ecoregions of South Africa , to the conservation action of land protection and management to abate agricultural conversion ( both ecoregions are allocated approximately 14% of the total budget for investment in this conservation action; Table 5 ) . Over 20 y all 17 ecoregions are allocated some funds ( Table 5 ) . Overall , the investment schedule was insensitive to the annual available budget , though some of the lower-priority ecoactions did not receive funding when the annual budget was reduced to US$10 million . For example , under a reduced budget , the Coolgardie woodlands was the only ecoregion in Australia allocated investment in invasive predator control over 20 y , since the current level of investment in this ecoaction is small . Likewise , under a reduced budget , funding was not allocated to invasive plant control in the Californian/Baja Californian ecoregions . It is informative to compare the outcome of this analysis to that of a simpler analysis that ignores costs and benefits , and instead prioritises the ecoregions for investment on the basis of a single ecological criterion—in this case , vertebrate species richness per unit area . There was a lack of concordance ( rs = 0 . 39 , p = 0 . 12 ) between the priorities based on the two approaches , indicating that they would recommend profoundly different ecoregions as investment priorities ( Figure 4 ) . For example , the Chilean matorral ecoregion has the fewest vertebrate species per unit area but received the fourth greatest allocation under the ecoaction approach because of the potentially high biodiversity benefit per dollar invested . Conversely , the Jarrah-Karri forest and shrublands ecoregion in Australia has the greatest vertebrate species richness per unit area but was not a priority using the ecoaction approach ( Figure 4 ) . When we compared the ecoaction-specific framework to a simplified model of conservation that focuses only on land acquisition ( see Materials and Methods ) , we found that greater biodiversity benefits are accrued by investing in actions targeted towards specific threats . The decision steps in the resource allocation process are identical regardless of the investment approach ( Figure 2 ) , with the exception that the land-acquisition-only approach considers only a single conservation action—land acquisition . Based on the data available for this analysis , we estimate that over 5 y many more species could be protected using an ecoaction approach ( 2 , 780 versus 703 species ) . After 20 y slightly more than twice as many species could be protected . The difference is reduced through time because of diminishing returns regardless of the investment approach . Therefore , after accounting for the existing level of investment in each ecoaction ( which in some cases includes land acquisition with the costs of management added; Text S1 ) or in land acquisition alone ( where the costs of managing specific threats are not accounted for; Table S1 ) , greater returns can be achieved using the ecoaction-specific framework . These results were relatively insensitive to the parameterisation of the species–investment relationship , specifically , the rate at which the returns from investment diminish ( as determined by the z exponent ) . The average ratio of species protected using the ecoaction approach and the land-acquisition-only approach over 5 y was approximately 3 . 49 ( this ratio varied from 3 . 46 to 3 . 51 with the value of z for each ecoaction randomly chosen from a uniform distribution between 0 . 1 and 0 . 4 , n = 30; see Materials and Methods ) .
These results illustrate the advantages of an ecoaction-specific framework over priority-setting approaches that ignore economic costs , or that focus only on the acquisition of land for protected areas . The Mediterranean example shows that an ecoaction-specific framework provides better outcomes for biodiversity conservation than the simpler approaches that have dominated the scientific literature [22] . In practice , very few conservation practitioners adopt species richness priorities identified by simple numerical ranking . Instead , they routinely consider the costs of investments , and more complex measures of biodiversity benefits . Our framework provides a standard , transparent , and quantitative template in which to solve complex resource allocation problems . By specifying costs and benefits and a total budget , we produced an investment schedule that reveals shifting priorities through time as the returns from investment change . Because conservation budgets are often reallocated every year , it could be practical to follow flexible and time-varying investment schedules , as opposed to being tied to specific actions simply because they were previously regarded a high priority . Furthermore , if an equitable distribution of a base level of funds is important , then a pre-specified amount of funds could be allocated to each ecoregion , with funds directed to particular actions according to their relative return on investment . Various refinements to our approach would be valuable . The calculation of biodiversity benefits could be improved by incorporating more detailed information from conservation practitioners , either in the form of empirical or expert data . We could also extend our analysis to consider other types of benefits , including the potential returns from the protection of ecosystem services [23] . Under such circumstances , it would be possible to assess the potential collateral benefits of conservation investments beyond the protection of biodiversity and to evaluate the trade-offs involved , as it is likely that different areas will be prioritised to achieve the alternative objectives . Other improvements might entail identifying the individual species that are most at risk due to the different threats , the impact of investment in each ecoaction on the persistence of these species , the likelihood of success of each ecoaction , and the potential for leverage . As with typical conservation planning exercises that focus on protected area establishment , we have assumed that each ecoaction will be totally effective in abating the relevant threat . A plethora of factors ( ranging from natural community succession to climate change ) render this assumption unreliable [24 , 25] . It would be ideal if we had estimates of the likelihood of long-term success of each conservation action in conserving biodiversity , both for the duration of the action , and after investment ceases . These data are unlikely to be available at any time in the near future . Instead , we base the cost of each ecoaction on the assumption that enough funds are invested to have a high likelihood of success . Presently we assume that alleviating the most important threat will protect the species at risk , but the number of protected species will likely be overestimated if some species need to be protected from multiple threats that require different ecoactions . With knowledge of the individual species at risk due to each threat we could identify which species are affected by more than one threat . With this knowledge , the complementarity of each ecoaction in improving species persistence could be incorporated , and this would help to minimise the degree to which benefits are overestimated , assuming of course that all important threats have been identified . In addition , the assumption of diminishing returns with cumulative investment could , in some instances , be replaced with threshold relationships for those conservation actions that yield no benefit until some minimum level of investment is reached . These , however , are straightforward technical modifications of the approach; obtaining the relevant data represents the greatest challenge . Within our dynamic framework , the investment schedules can be updated as our knowledge improves . Application of our framework can also provide insights into research priorities . For example , through our Mediterranean application it has become apparent that information on the likelihood of success and patterns in threat co-variation among species are important subjects of future research . We hope that our framework for conservation investment will encourage conservation practitioners to track and report action-specific data to allow a refined framework to be parameterised . To examine the sensitivity of our results to the budget , we reduced the amount of money available per annum from US$100 million to US$10 million . In this example , varying the annual budget simply altered what was able to be achieved over the timeframe of interest: an investment of US$10 million over 10 y will achieve approximately the same outcomes as an investment of US$100 million in 1 y . This is because the investment schedules are determined only by the area requiring investment and the relative returns of the investment . While the “maximise short-term gain” heuristic closely approximates the optimal solution , especially with funding uncertainty , the urgency of investment could also be incorporated if information were available on the rate of species loss in each ecoregion due to each threat [4] . Under such circumstances , the investment schedules would change over different timeframes because of the rates of species loss influencing both the area requiring each ecoaction and the relative return from investment . Explicitly accounting for ongoing species losses would change our objective to “minimising losses” rather than “maximising gains” [4 , 6 , 26] . Incorporating information on the rates of species loss would further improve the ability to determine when conservation actions should be implemented in order to achieve the greatest outcomes for biodiversity . Presently , data on the rates of loss of species due to particular threats are scarce , and there is limited understanding of how species loss varies with changes in available habitat . We have applied the approach at a global scale , but it will be more effectively applied at local or regional scales , if only because in many cases the required data are more likely to be available and to be more accurately estimated . Application at a global scale is nevertheless important , despite the sparseness of the data . First , global non-governmental organisations and international agencies are interested in decision-making at a global scale , and will make investment decisions at such a scale . Second , there is now a large academic literature on setting conservation priorities at a global scale; these studies are equally beset by sparse data and poorly tested assumptions , they have mainly ignored costs , and they have focussed on protected area establishment . Analysis at a finer spatial scale would further increase the efficiency of the investment schedules by accounting for the heterogeneity in the costs and benefits of conservation actions . Such an analysis will likely require assessment of empirical , modelled , and expert data . With a more detailed and refined analysis we could also account for the actual costs and relative success of conservation actions undertaken in the past . Such an analysis could also allow finer-scale socio-economic and policy data to be incorporated . For example , the area of land predicted to be vulnerable to agricultural conversion in the montane fynbos and renosterveld ecoregion of South Africa is likely to be overestimated by the biophysical models employed as these ignore socio-economic and political factors . The collapse of subsidies in this region may mean that only small areas are currently experiencing conversion pressures [19] . While analysis at a finer scale would allow a refined assessment of investment priorities , it would be at the expense of the global-scale evaluation of investment priorities presented here . By being transferable across scales , our framework can help to bridge the current gap between global-scale analyses and investment decisions that are implemented within regions , as it can provide an understanding of the relative importance of each ecoaction for conserving biodiversity within a global context . Regardless of scale , stakeholders and experts are integral to the success of the ecoaction approach , through identifying threats and actions , determining the relative costs and benefits of each action , and identifying local constraints for their implementation ( see Materials and Methods ) . The results of any assessment must also be interpreted in the context of the value systems of stakeholders , as well as other factors such as the implementation capacity of the relevant management agencies . These factors reflect the fine-tuning of quantitative analyses that is required to account for real-world constraints and opportunities , although the aim is to avoid such post hoc refinements and integrate all important considerations into the analysis . The results of a systematic and transparent assessment make explicit any trade-offs , compromises , and opportunities . When we qualitatively compare the results of our analyses to planning approaches within South Africa [27] , we find a high degree of concordance , as we do when we compare the relative levels of investment in Phytophthora management and predator control in Australia [28–30] . Nevertheless , the identification of investment priorities through a systematic and transparent process will be extremely useful when local experts are not available or there is a need to remove individual biases . At a global or even national scale , there is likely to be a deficit of experts with knowledge of multiple regions and conservation actions and an ability to identify investment schedules across these in an integrated manner . Our conservation investment framework offers substantial dividends for biodiversity conservation by prioritising the most appropriate and feasible conservation actions to abate the threats that operate in a region . Already , there has been a call for conservation organisations to audit their investments and measure their returns [31–34] . For conservation practitioners , this framework represents a much needed tool for incorporating their insights and experience regarding the costs , benefits , and dynamics of a suite of conservation actions to maximise conservation outcomes . | Given limited funds for biodiversity conservation , we need to carefully prioritise where funds are spent . Various schemes have been developed to set priorities for conservation spending among different countries and regions . However , there is no framework for guiding the allocation of funds among alternative conservation actions that address specific threats . Here , we develop such a framework , and apply it to 17 of the world's 39 Mediterranean-climate ecoregions . We discover that one could protect many more plant and vertebrate species by investing in a sequence of conservation actions targeted towards specific threats , such as invasive species control and fire management , rather than by relying solely on acquiring land for protected areas . Applying this new framework will ensure investment in actions that provide the most cost-effective outcomes for biodiversity conservation . | [
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] | [
"ecology",
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] | 2007 | Conserving Biodiversity Efficiently: What to Do, Where, and When |
Household contacts constitute the highest risk group for leprosy development , and despite significant progress in the disease control , early diagnosis remains the primary goals for leprosy management programs . We have recruited 175 seropositive and 35 seronegative household contacts from 2014 to 2016 , who were subjected to an extensive protocol that included clinical , molecular ( peripheral blood qPCR , slit-skin smear qPCR , skin biopsy qPCR ) and electroneuromyographic evaluations . The positivity of peripheral blood qPCR of seropositive contacts was 40 . 6% ( 71/175 ) whereas only 8 . 6% ( 3/35 ) were qPCR positive in seronegative contacts ( p = 0 . 0003 ) . For the slit-skin smear , only 4% ( 7/175 ) of seropositive contacts presented positive bacilloscopy , whereas the qPCR detected 47 . 4% ( 83/175 ) positivity in this group compared with only 17 . 1% ( 6/35 ) in seronegative contacts ( p = 0 . 0009 ) . In the ENMG evaluation of contacts , 31 . 4% ( 55/175 ) of seropositives presented some neural impairment , and 13 . 3% ( 4/35 ) in seronegatives ( p = 0 . 0163 ) . The presence of neural thickening conferred a 2 . 94-fold higher chance of ENMG abnormality ( p = 0 . 0031 ) . Seropositive contacts presented a 4 . 04-fold higher chance of neural impairment ( p = 0 . 0206 ) . The peripheral blood qPCR positivity presented odds 2 . 08-fold higher towards neural impairment ( OR , 2 . 08; p = 0 . 028 ) . Contrarily , the presence of at least one BCG vaccine scar demonstrated 2 . 44-fold greater protection against neural impairment ( OR = 0 . 41; p = 0 . 044 ) . ELISA anti-PGL-I is the most important test in determining the increased chance of neural impairment in asymptomatic leprosy household contacts . The combination of the two assays ( ELISA anti-PGL-I and peripheral blood qPCR ) and the presence of BCG scar may identify individuals with higher chances of developing leprosy neuropathy , corroborating with the early diagnosis and treatment .
Leprosy is a chronic infectious disease caused by Mycobacterium leprae ( M . leprae ) , an obligate intracellular parasite with a predilection for infecting peripheral nerves and skin . Leprosy is a current and challenging disease , because it still represents a problem for public health in developing countries such as Brazil , which ranks second worldwide in the number of new cases [1] . The predominance of multibacillary ( MB ) cases with neural disabilities indicates late diagnosis , reinforcing the ineffective epidemiological control in many countries [2] . In addition , new cases not only with high functional impairment , but also in children , reflect failure of early leprosy detection and indicate ongoing transmission [3 , 4 , 5] . Leprosy contacts of MB patients present a risk towards leprosy occurence 5 to 10 times higher than the general population [6 , 7] . Because of the complex relationships between genetic , immunological and environmental factors , most infected contacts will not develop leprosy , although recent studies have reported that they can be healthy carriers and spread M . leprae to susceptible individuals [8 , 9 , 10] . The investigation of the transmission and infectivity of M . leprae through molecular and immunological tools has shown that half of the leprosy contacts are healthy carriers , evidenced by the presence of M . leprae DNA in nasal swabs and , in nasal turbinate biopsies , and/or in the peripheral blood of healthy individuals , while about 18% presented subclinical infection ( presence of anti-PGL-I IgM antibodies ) with higher risk of illness [10 , 11 , 12] . It is important to emphasize that the subclinical neural involvement in this group has not yet been well defined , and its documentation is fundamental . Such elucidation would enable the discussion of chemoprophylaxis and early treatment , as a complementary strategy for leprosy control . This is a case-control study that aimed to evaluate the clinical and laboratory predictors of subclinical neural impairment in leprosy household contacts .
Epidemiological and clinical data were recorded . All patients underwent a rigorous dermatoneurological evaluation by expert professionals . Intradermal sensory neuropathy or superficial leprosy neuropathy was defined by the presence of sensory abnormalities in a region not respecting the anatomical distribution of a specific nerve or spinal root , as terminal branches of several nerves may be involved in an affected area , while truncal neuropathy was defined as sensory and/or motor loss respecting the anatomical distribution of a specific nerve or spinal root . Bacilloscopy–analyses of bacillary load of intradermal smears from six sites were performed: the two ear lobes , both elbows and knees , as well as from skin and/or nerve biopsy samples . Sample collection was preceded by topical application of cream containing lidocaine ( 7% ) and tetracaine ( 7% ) at all sites . ELISA anti PGL-I IgM serology–Enzyme-linked immunosorbent assay ( ELISA ) was performed on all household contacts . Serum anti-PGL-I IgM antibodies were detected by enzyme-linked immunosorbent assay ( ELISA ) performed against the purified native PGL-I from the Mycobacterium leprae cell wall , according to a methodology previously described elsewhere . The reagent was obtained through BEI Resources , NIAID , NIH: Monoclonal Anti-Mycobacterium leprae PGL-I , Clone CS-48 ( produced in vitro ) , NR-19370 [13] . DNA Extraction and Real-Time Quantitative Polymerase Chain Reaction ( Real-Time PCR ) –the DNA extraction from blood ( 500 μL ) , slit-skin smear , nerve and skin biopsies were performed . The quantitative real-time PCR ( qPCR ) assay targeting M . leprae DNA was performed by targeting the bacillus-specific genomic region ( RLEP ) in a real-time PCR system ( ABI 7300 , Applied Biosystems , Foster City , CA , USA ) [10 , 14 , 15] . ENMG studies were carried out utilizing the MEB 4200K ( NIHON-KODHEN ) device . In the sensory conduction study , the median , ulnar , dorsal hand cutaneous , radial , lateral antebrachial cutaneous , median antebrachial cutaneous , sural , fibular superficial , saphenous and medial plantar nerves were examined bilaterally . In the motor conduction study , the median , ulnar , common fibular , and tibial bilaterally nerves were examined , supplemented by techniques for focal impairment identification at compression sites often affected in leprosy neuropathy , such as median nerve at the wrist , ulnar nerve at the elbow , fibular nerve at the fibular head and tibial nerve at the ankle . The parameters used to evaluate each nerve are described separately as a supplementary file . All of the leprosy contacts selected did not present any skin lesion . For this reason , the biopsy of a small elbow skin fragment ( approximately 1 cm ) was performed , considering that it is a cold region with possible intradermal impairment , and therefore a site often altered in leprosy neuropathy . Nerves that underwent biopsy were selected according to the patient’s clinical condition , and included exclusively sensory nerves that presented sensory changes and/or thickening , and also one of the following electrophysiological changes in the sensory conduction analysis: absence of response on both sides; unilateral absence of response; bilaterally decreased amplitude of the sensory action potential ( SAP ) , considering reference values; and over 50% decrease in the amplitude of the SAP , compared with the contralateral side . During the biopsy , the nerve was isolated and completely transected . All patients signed a specific informed consent form referring to this process . During the procedure , a skin biopsy of the area superjacent to the corresponding territory of the nerve also underwent a biopsy procedure . The biopsied nerve and skin were processed and studied according to routine standard procedures . Formalin-fixed paraffin-embedded were cut longitudinally and transversely at 5-μm thickness and stained with hematoxylin and eosin stain . Additionally , special staining with Masson Trichome was performed to assess fibrosis . Fite-Faraco stain was performed for bacilli identification . The Shapiro Wilk test was used to test data normality within groups . The Wilcoxon-Mann-Whitney U Test was carried out , and the Binomial Test was applied for the Study of Dichotomous Variables , with significance defined as p<0 . 05 . Multiple logistic regression was used to verify the dependence relation between the presence of ENMG abnormality ( categorical variable ) and the independent variables ( ELISA anti-PGL-I IgM , intradermal smear qPCR , skin biopsy qPCR , peripheral blood qPCR and BCG scar ) . After verifying the dependence between variables , odds ratios ( OR ) were determined , and the probability of outcomes analyzed . The statistical program used was the software GraphPad Prism version 7 .
Comparisons of all epidemiological characteristics between groups did not show any significant difference ( Table 1 ) . The mean anti-PGL-I IgM ELISA index was 2 . 05 in seropositive contacts , and 0 . 52 in seronegative contacts ( p<0 . 0001 ) . In the analysis of the peripheral blood qPCR from seropositive contacts , 40 . 6% ( 71/175 ) presented positivity , while only 8 . 6% ( 3/35 ) in seronegative contacts ( p = 0 . 0003 ) . In the intradermal smear analysis , only 4% ( 7/175 ) of the seropositive contacts presented positive bacilloscopy , whereas the evaluation by the qPCR in this group showed positivity of 47 . 4% ( 83/175 ) and only 17 . 1% ( 6 / 35 ) in the seronegative contacts ( p = 0 . 0009 ) , all with negative bacilloscopy ( Table 1 ) . Regarding the clinical evaluation of seropositive contacts , 18 . 3% ( 32/175 ) presented a pattern of intradermal impairment , compared with 14 . 3% ( 5/35 ) in the seronegative contacts ( p = 0 . 5717 ) , defined as multifocal painful hypoesthesia , especially with a greater involvement in the elbow , knee and earlobe regions , setting a temperature-dependent pattern . Sensorial impairment with a specific territory distribution ( truncal pattern ) was present in 17 . 1% ( 30/175 ) of seropositive contacts and in 8 . 6% ( 3/35 ) of seronegative contacts ( p = 0 . 2079 ) . The impairment of the deep sensation ( vibratory and kinetic postural ) and deep osteotendin reflexes was not observed in any case . Only 2 . 3% ( 4/175 ) of seropositive contacts and 2 . 8% ( 1/35 ) of seronegative contacts presented motor manifestation ( p = 0 . 8598 ) . The presence of neural thickening was observed in 21 . 1% ( 37/175 ) of seropositive versus 8 . 6% ( 3/35 ) of seronegative contacts ( p = 0 . 0838 ) . Among contacts with thickening , the ulnar nerve alteration was the most frequently one ( 72 . 5% , 29/40 ) . None of the evaluated contacts presented skin lesion . ENMG evaluation detected some neural impairment in 31 . 4% ( 55/175 ) of seropositive contacts . In seronegative contacts , only 13 . 3% ( 4/35 ) showed changes in this examination ( p = 0 . 0163 ) . ( Table 1 ) Of the 59 contacts with altered ENMG , 81 . 3% ( 48/59 ) were contacts of MB index cases , although this condition did not confer greater chances of alteration in this examination ( OR , 0 . 99; CI95% , 0 . 45 to 2 . 15; p = 0 . 9865 ) . Only 32 . 2% ( 19/59 ) presented neural thickening in the clinical evaluation . However , the presence of neural thickening conferred a 2 . 94-fold higher chance of presenting ENMG abnormality ( OR = 2 . 94; CI95% , 1 . 43 to 6 . 00; p = 0 . 0031 ) . The mean number of nerves affected was 1 . 44 per contact . The nerves most frequently affected are described in Table 2 . In the neurophysiological pattern observed in ENMG , 69 . 5% ( 41/59 ) presented only one altered nerve ( mononeuropathy ) , and 30 . 5% ( 18/59 ) two or more altered nerves ( multiple mononeuropathy ) . According to clinical data and ENMG results , 50 . 8% ( 30/59 ) of leprosy contacts demonstrated at least one nerve eligible for biopsy , but only 60 . 0% ( 18/30 ) of those were submitted to this process . The most frequent biopsied nerve was the sensory ulnar—dorsal cutaneous of the hand ( 72 . 3%; 13/18 ) , followed by superficial fibular ( 16 . 7%; 3/18 ) , sural ( 5 . 5%; 1/18 ) , and deep fibular ( 5 . 5%; 1/18 ) . Only 27 . 8% ( 5/18 ) of the nerves presented some anatomopathological alterations suggestive of leprosy , such as endoneural or epineural infiltrate , presence of fibrosis , perineural thickening or presence of endoneural granuloma . No leprosy contacts presented positive bacilloscopy in the peripheral nerve biopsy . On the other hand , qPCR of nerve biopsies was positive in 61 . 1% ( 11/18 ) of the cases . The qPCR of the suprajacent skin area was positive in 16 . 7% ( 3/18 ) of the nerve biopsies , whereas bacilloscopy was negative in all samples . In order to further explore the complex interaction among results , a multivariate statistical method was conducted to confirm the dependence relation of variables elucidated above with the chance of occurrence of ENMG abnormalities , demonstrating that ELISA anti-PGL-I positivity confers a 4 . 04-fold greater chance of neural damage ( OR = 4 . 04; CI95% , 1 . 24 to 13 . 21; p = 0 . 020 ) , while peripheral blood qPCR positivity presents a 2 . 08-fold higher chance ( OR = 2 . 08; CI95% , 1 . 08 to 4 . 02; p = 0 . 028 ) . The presence of at least one BCG vaccine scar demonstrated 2 . 44-fold greater protection against neural impairment ( OR = 0 . 41; CI95% , 0 . 18 to 0 . 98; p = 0 . 044 ) . There was no dependence relation with the variables intradermal smear qPCR or skin biopsy qPCR ( Table 3 ) . The combination of unfavorable results for the three assays ( no BCG scars , seropositivity of anti-PGL-I IgM , and positive qPCR in peripheral blood ) indicated the highest probability ( 62 . 6% ) of neural impairment in contacts . The presence of BCG scars in combination with other disease predictors led to the reduced probability of neural impairment . The group of contacts with favorable results ( presence of BCG scars , negative anti-PGL-I and negative qPCR in peripheral blood ) was the one with the lowest probability ( 7 . 6% ) of neural damage ( Table 4 )
This is a case-control study in Brazil that measured the chance of occurrence of peripheral neural impairment in asymptomatic leprosy household contacts , through serological , molecular and neurophysiological tests . The prevalence of abnormalities in the ENMG reinforce the importance of epidemiological surveillance and follow-up of leprosy contacts , allowing early recognition , by a combination of diagnostic tools , of neural impairment in this population . Previous studies have already documented neural involvement in leprosy contacts , but none has explored how predictors and laboratorial tests are correlated with such pathological occurrence . This is the first study in an endemic country evidencing that subclinical neural impairment may be the first and only clinical manifestation of leprosy , and when appropriately recognized may contribute to early diagnosis and treatment of leprosy , which by definition is primarily neural [16 , 17 , 18 , 19 , 20] . Some ENMG abnormalities may precede the classic clinical symptoms of leprosy , such as the absence or amplitude reduction of the sensory action potential of some nerves , focal myelinic impairment , which is corroborated by our findings [20–22] . Although asymptomatic , some leprosy contacts already had at least one abnormality detected in the detailed neurologic physical examination , mainly sensory impairment and neural thickening , corroborating the pattern described in the classical forms of leprosy , an asymmetric peripheral neuropathy that is predominantly sensorial . These contacts present a subclinical form in which the ENMG is superior to the thermal , tactile and vibratory sensation evaluation , with capacity for early detection of neural impairment [23 , 24 , 25] . Neural thickening , despite being one of the cardinal signs of leprosy and a risk factor for the presence of ENMG abnormalities , as demonstrated in the present study , is a subjective parameter and does not always show agreement with the ENMG , since only one third of the leprosy contacts with ENMG abnormality presented neural thickening [24 , 25 , 26] . Leprosy contacts of MB patients did not present higher chances of neural impairment , although this factor is associated with an increase in the disease outcome in several prospective studies [6 , 9 , 10 , 11 , 12] , which only evaluated the natural history of the disease , but without a neurophysiological , serological or molecular intervention for early diagnosis , as shown in our report . Our results have demonstrated that ELISA anti-PGL-I is the most important test in determining the increased chance of neural impairment in leprosy contacts , corroborating previous studies that also demonstrated its importance as a screening test in the definition of leprosy contacts that present a higher risk of illness . The use of the ELISA anti-PGL-I test is justified due to its high correlation with MB clinical forms , being directly proportional to bacillary load , and also its association with a increased risk of developing leprosy in seropositive contacts [7 , 10 , 12 , 13 , 27 , 28 , 29] . BCG vaccination has been associated with prevention of leprosy in different studies , especially MB forms [7 , 30] . Based on our results , the presence of one or more BCG scars provided protection against neural damage . Thus , an additional intradermal BCG booster dose should be maintained in leprosy control programs , aiming for protection against leprosy , including neural forms [7] . Concerning the molecular evaluation , studies have shown good prospects regarding the detection of M . leprae in several samples ( blood , skin , swabs , smear ) of leprosy patients and contacts by qPCR , which have contributed to the definition of the existence of healthy carriers and subclinical infection [10 , 11 , 14 , 15] . We have shown previously that the positivity of peripheral blood qPCR in contacts was 6 . 7% with a 5 . 54-fold risk for disease outcome [10] . Our current results reinforce our previous findings , demonstrating an increased chance of neural involvement in contacts with positive peripheral blood qPCR . Although the qPCR positivity of intradermic smear and skin biopsy did not determine an increased chance of neural damage , these tools may play a role in diagnostic confirmation , even allowing the initiation of treatment of asymptomatic contacts . Leprosy household contacts constitute a group of individuals at high risk for disease development , so their participation in the dissemination of M . leprae to susceptible individuals in endemic communities cannot be neglected [10] . Despite significant progress in controlling leprosy in recent years , early diagnosis remains the primary goal and challenge of leprosy control . Therefore , with the prospect of eliminating leprosy as a public health problem , the development and implementation of more specific and sensitive methods for the detection of M . leprae and its neural impairment , using immunological , molecular and neurophysiological tools are mandatory to increase the knowledge of leprosy epidemiology , to break its chain of transmission , thereby enabling effective control of this disease . Taking into consideration our findings , we propose an algorithm for the follow-up of leprosy household contacts ( Fig 1 ) . We suggest annual monitoring through serological ( ELISA anti-PGL-I ) evaluation for at least 5–7 years , considering the better risk-benefit in relation to neural impairment and development of MB clinical forms [28] . The combination of the three assays in ( ELISA anti-PGL-I , peripheral blood qPCR and BCG scars ) may identify individuals with higher chances of developing leprosy neuropathy , not only justifying the treatment initiation in those with confirmed diagnosis , but also indicating chemoprophylaxis in contacts with unfavorable predictors . One of the limitations of the study was that it did not present the follow-up of interventions proposed above , regarding early treatment and chemoprophylaxis , which should be done in future work . In addition , unfortunately , leprosy remains a neglected disease , making it difficult to apply this study to clinical practice in endemic countries . | Despite the apparent progress observed in recent years in leprosy control , early identification of cases remains one of the primary objectives of control programs . In addition , the failure of the current therapeutic scheme on the incidence of leprosy demonstrates that the disease elimination as a public health program promoted by the World Health Organization ( WHO ) depends on an incisive action to interrupt its transmission chain . The long incubation period of leprosy , its insidious symptoms and signs may difficult its diagnosis . Several studies have recently demonstrated that IgM anti-PGL-I seropositive contacts present higher chances to become ill than seronegative ones . Therefore , our question was: do seropositive contacts at greater risk of becoming sick already present subclinical neural damage ? Therefore , our approach was to analyse anti-PGL-I seropositive contacts through electroneuromyography . The development and implementation of more specific and sensitive methods for the detection of M . leprae and its neural impairment , using immunological , molecular and neurophysiological tools are mandatory to increase the knowledge of leprosy epidemiology , to break its chain of transmission , thereby enabling effective control of this disease . This report demonstrated that seropositive contacts is the population group with higher chances of neural impairment . | [
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"disease... | 2018 | Molecular, immunological and neurophysiological evaluations for early diagnosis of neural impairment in seropositive leprosy household contacts |
Quantitation of the nonlinear heterogeneities in Leishmania parasites , sand fly vectors , and mammalian host relationships provides insights to better understand leishmanial transmission epidemiology towards improving its control . The parasite manipulates the sand fly via production of promastigote secretory gel ( PSG ) , leading to the “blocked sand fly” phenotype , persistent feeding attempts , and feeding on multiple hosts . PSG is injected into the mammalian host with the parasite and promotes the establishment of infection . Animal models demonstrate that sand flies with the highest parasite loads and percent metacyclic promastigotes transmit more parasites with greater frequency , resulting in higher load infections that are more likely to be both symptomatic and efficient reservoirs . The existence of mammalian and sand fly “super-spreaders” provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease . Sand fly blood-feeding behavior will determine the efficacies of indoor residual spraying , topical insecticides , and bed nets . Interventions need to have sufficient coverage to include transmission hot spots , especially in the absence of field tools to assess infectiousness . Interventions that reduce sand fly densities in the absence of elimination could have negative consequences , for example , by interfering with partial immunity conferred by exposure to sand fly saliva . A deeper understanding of both sand fly and host biology and behavior is essential to ensuring effectiveness of vector interventions .
In the Indian subcontinent , an effort to eliminate anthroponotic visceral leishmaniasis ( VL ) has been underway for a decade , and the incidence of the most severe clinical form , kala-azar , is at its lowest levels in 45 years . The program appears to be on track to “eliminate VL as a public health problem” by 2020 ( defined as kala-azar incidence <1 case per 10 , 000 population ) [1] . However , true elimination of transmission will be more elusive and requires a deeper understanding of the biology underlying transmission and disease . Substantial VL and cutaneous leishmaniasis ( CL ) burdens occur in many other continents , but the transmission dynamics and reservoir hosts differ , and development of tools for control and elimination are less advanced than in South Asia [2] . In this article , we review recent research that sheds light on the quantitative biology of leishmanial transmission between sand flies and mammalian hosts and use these insights to better understand observed patterns of VL and CL transmission and disease .
Since VL was first studied in India nearly a century ago , investigators have observed incidence cycles that rise and fall with a slow periodicity [3] . Cycles have been documented in India , Bangladesh , Sudan , and Brazil [4–8] . A single cycle tends to last 5 to 15 years , with interepidemic intervals of 10 to 30 years [4 , 5 , 8] . At a regional level , climatic factors may contribute to these periodic cycles [9] . In a community , the fall in incidence after several peak years is thought to result from the buildup of herd immunity , with new epidemic onset occurring when a sufficient number of susceptible residents have accumulated through births and/or in-migration [3 , 6] . The current best measure of protective immunity is the leishmanin skin test ( LST ) , which reflects durable cell-mediated immunity . Individuals with a positive LST have more than 95% lower risk of kala-azar compared to those with negative LST , and the age-related rise in positive LST prevalence parallels an age-related decrease in disease risk [10–12] . In contrast , exposure to infective sand flies is variably age dependent [12 , 13] . A fall in the average age of kala-azar patients may be observed as an epidemic matures [14] . The level of herd immunity required to end an epidemic cycle and the time to reach this level likely vary depending on parasite virulence , transmission intensity , vector exposure patterns , and host factors such as nutritional status and access to treatment . Interventions such as vector control and rapid case detection and treatment may alter the cycle but have not been shown to eliminate the periodicity . Intensive blanket DDT spraying during the malaria eradication program of the 1950s–1960s prolonged the interepidemic period in the Indian subcontinent , but since the resurgence in the 1970s , there have been 3 typical epidemic cycles in India and 2 in Bangladesh ( Fig 1 ) [6 , 15–18] . Periodic epidemic cycles represent clustering in time; the second major characteristic of VL epidemiology is clustering in space . On a global scale , VL is highly clustered , with 90% of the disease burden occurring in relatively few states or districts within just 6 countries: India , Bangladesh , Sudan , South Sudan , Brazil , and Ethiopia [4] . At smaller spatial scales , VL-affected communities and census tracts cluster in space and time [19 , 20] . At the most local scale , strong clustering is seen at the household and near-neighbor levels [20–23] . Small-scale clustering is most marked early in an epidemic cycle when most community residents are susceptible and tends to disappear as the prevalence of immunity rises [21] . Clustering is likely due to macro- and microenvironmental conditions that promote sand fly breeding , survival , and aggregation , including proximity to reservoir and nonreservoir blood sources ( humans , dogs , or other animals ) [9 , 19 , 20 , 24] . Sand fly aggregations are mediated by complex host—sand fly interactions ( e . g . , [25] ) including sex-aggregation pheromones released by males of some species [26 , 27] , host kairomones , or plant phytochemical attractants [28] . Temporal clustering of fly infection prevalence is often greatest in the wet season or at the end of the “sand fly season” , when few nulliparous females are emerging and physiological sand fly age is greatest ( measured by parity ) [29] . Variation in the vector’s propensity to blood feed indoors or outdoors may also determine who receives the most infectious bites .
Household and near-neighbor clustering supports the assumption that untreated kala-azar cases , long known to be infectious to sand flies [30] , comprise the most important infection reservoir fueling transmission during epidemics . Post-kala-azar dermal leishmaniasis ( PKDL ) , a chronic dermatosis that follows apparently successful kala-azar treatment in 5% to 15% of patients in the Indian subcontinent and up to 50% in Sudan , is thought to provide the reservoir that maintains transmission between epidemic cycles [6 , 31–33] . PKDL patients are usually not systemically ill , may remain untreated for years , and have been shown to be infectious to sand flies [6 , 33–36] . Demonstration of infectiousness requires feeding of laboratory-reared sand flies on the patient ( direct xenodiagnosis ) or the patient’s blood via a membrane feeder ( indirect xenodiagnosis ) . Because xenodiagnosis is impractical for population-based studies , investigators have sought proxy measures , such as quantitative polymerase chain reaction ( qPCR ) , but the strength and shape of those relationships in different hosts require validation . In canine leishmaniasis , positive serology or qPCR had high sensitivity ( 97%–100% ) to identify highly infectious dogs but low specificity ( 13% for serology , 22% for qPCR in ear skin biopsy ) [37] . A derived threshold cutoff in ear skin showed sensitivity of 100% to predict highly infectious dogs and specificity of 98% to identify noninfectious ones [37] . Although clinical VL severity was significantly associated with infectiousness , parasite load using the cutoff was a better predictor . These and other canine data also clearly demonstrate that some dogs are “super-spreaders” while others contribute little to transmission: in published xenodiagnosis studies , 15% to 44% of dogs were responsible for >80% of all sand fly infections [38–40] . No such proxies have been validated in human leishmaniasis , although preliminary data from 3 PKDL patients suggest that parasite loads in skin biopsies may provide a proxy for infectiousness [36] .
Currently , a major question facing VL control efforts in the Indian subcontinent is whether persons with asymptomatic infection are sufficiently infectious to sand flies to constitute an epidemiologically significant infection reservoir [41] . Asymptomatic infections based on seroconversion outnumber clinical disease by 4- to 17-fold , with rising ratios as kala-azar incidence falls [23 , 32 , 42–45] . If even a subset of asymptomatic individuals are infectious at a very low level , they could still play an important role in transmission , especially when clinical disease incidence is driven to low levels [46 , 47] . Failure to address this potential reservoir could preclude interruption of transmission [1] . Recent Indian data show that the median blood parasite load by qPCR is 500-fold higher in kala-azar patients than in asymptomatically infected individuals [48] . Data from the same group confirm that parasite loads in peripheral blood correlate well with those in splenic aspirates [49] . High parasite loads were rare among asymptomatic infections and , when present , indicated individuals with high risk of subsequent development of kala-azar [50] . Antibody titers may also help distinguish asymptomatic infection from “presymptomatic” infection . In a longitudinal study in India , 12% of those with direct agglutination test ( DAT ) titers >25 , 600 subsequently developed clinical disease , compared to 1% of those with low titer positive DAT results [44] . In canine leishmaniasis , asymptomatic infected dogs are expected to be less infectious than polysymptomatic dogs through time [38 , 51] , whereas in naturally infected wildlife hosts , infection is usually benign and associated with relatively low parasite loads and degree of infectiousness ( e . g . , foxes in Brazil [52] and lagomorphs in Spain [24] ) . However , asymptomatic animals may have a longer infectious life expectancy than diseased , highly infectious individuals . The canine data reviewed above [37] suggest that qPCR has the most promise as a proxy for xenodiagnosis , but that relationship may vary with parasite tropism and Leishmania species [53] . The best specimen type ( e . g . , peripheral blood or skin biopsy ) , quantitative technique , and threshold will need to be rigorously validated against xenodiagnosis as the gold standard in each epidemiological setting .
In nature , sand flies likely become infected with varying doses of parasites . This initiating dose [54] , combined with sand fly immunity , parasite virulence , the sand fly gut microbiota [55–57] , environmental conditions , and the blood meal [34] , influences parasite development in the gut and subsequent transmission . In particular , the sand fly gut microbiota has recently been shown to heavily influence parasite survival [55–57] and transmission [56] . In an experimental model , transmission via flies infected with varying doses of L . major parasites was quantified [54] . Higher infecting inocula resulted in greater numbers of parasites per sand fly on day 14 postinfection and higher percentages of metacyclic promastigotes . The percentage of metacyclics was the best predictor of subsequent transmission efficiency to the mammalian host . The bites of high-dose infected flies resulted not only in higher transmission frequencies but also increased disease severity . Temperature , humidity , and oviposition status also significantly influenced transmission efficacy [54] . These observations support the concept , as described for dogs , of “super-spreader” blood meal hosts with high parasite loads resulting in flies with high-dose infections that initiate more severe infections upon subsequent transmission .
In an analysis of transmission by single sand flies , most infected mice were inoculated with a low dose ( <600 parasites ) ; however , for 1 in 4 , the inoculum was >1 , 000 parasites . High-dose transmission resulted from heavy midgut infections , incomplete blood feeding , and transmission of a high percentage of the parasite load from the fly [41] . In a related analysis , low-volume ( 5-uL ) injection of low ( 100 ) or high ( 5 , 000 ) doses of sand fly—derived metacyclic promastigotes were inoculated into a restricted dermal site in mice that had been preexposed to sand fly bites . Inoculation of 5 , 000 parasites into the ear dermis resulted in higher initial parasite loads and more severe acute disease . However , high-dose infections resolved more completely , with a lower lesion size during the chronic phase and a trend towards lower parasite numbers in the skin . Similar observations were published by Lira et . al [58] . Several studies have allowed uninfected flies to feed on the site of primary L . major infection . As expected , the parasite load in the dermal site of infection directly correlated with the efficiency of transmission from the mammalian host to the vector , with very low parasite loads typically failing to transmit [58–60] . Although the more severe lesions observed at early time points in mice receiving high dose inocula resulted in highly efficient transmission to uninfected flies , these lesions were less efficient at chronic time points . In contrast , lesions initiated with low doses did not result in transmission back to sand flies during early infection but did act as a moderately efficient reservoir during chronic disease [58 , 59] . These observations suggest 2 non-mutually exclusive modes of transmission ( Fig 2 ) . One mode is the acquisition of low numbers of parasites by uninfected sand flies feeding on individuals with low parasite loads and mild or asymptomatic chronic disease . These flies in turn have infections with low parasite numbers and low frequencies of metacyclic promastigotes , and transmit less severe disease . This “mild/asymptomatic” mode of transmission may help explain the maintenance of the parasite in a given population without severe clinical disease . For example , in an investigation in Bhutan , only 1 kala-azar case was detected in a village , yet 35% of the surveyed residents had positive LST results , and the age-prevalence curve strongly suggested chronic low-level transmission over many years [61] . The second mode of transmission occurs when sand flies feed on a heavily infected individual and develop an infection with high parasite numbers and high frequencies of metacyclic promastigotes . When these flies feed on a second host , they transmit a larger number of parasites , causing a more acute and severe disease . On an individual level , the transition from a mild/asymptomatic transmission event to severe/symptomatic transmission may be modulated , for example , when a mammalian host develops severe disease despite a low-dose inoculum due to host factors such as immune status , nutrition , and genetics [62] . In the sand fly host , individual flies infected with a low-dose inoculum may on occasion develop more robust and transmissible infections due to sand fly host factors such as microbiota or sand fly immunity . Alternatively , a poorly infected sand fly may transmit a larger dose of parasites , something that has been shown to occur experimentally , albeit rarely [59] . High-dose transmission by a poorly infected fly is likely related to sand fly feeding behavior , as described below , for the “blocked fly phenotype . ”
Sand flies probe the skin and lacerate the upper dermal capillaries , forming a pool of blood , and continuously inject saliva into the wound to prevent clotting [63 , 64] . Sand fly salivary gland homogenate has been shown to exacerbate experimental leishmaniasis in naïve animals when co-inoculated with parasites or confer protection in animals exposed to infected sand fly bites or Leishmania plus salivary proteins [65–67] . In experimental models , the most protective salivary proteins induced a delayed type hypersensitivity ( DTH ) response in skin as early as 6 hours post bite , skewed towards a pro-inflammatory ( Th1 ) phenotype [66 , 68] . This focal cellular immunity is thought to act against the earliest stages of infection , reducing parasite survival and ability to initiate disease . Salivary proteins that induce such responses protect against a variety of Leishmania species in animal models [63] , although the specific mechanism has yet to be fully elucidated . In humans , DTH has also been shown to occur in individuals exposed to uninfected sand flies [69 , 70] . Despite this , endemic transmission continues in populations that are frequently bitten by sand flies , suggesting a lack of protection in humans , decay of immune responses between transmission seasons [71] , or variable effects of salivary components [72–74] .
The existence of mammalian and sand fly “super-spreaders” provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease . Blood-feeding vectors , including sand flies , are not uniformly distributed within or between susceptible host species [84 , 85] . Nonhomogeneous mixing of vectors and hosts usually results in higher transmission rates and greater infection persistence compared to homogeneously mixed populations [84–87] . In nature , infections of wildlife hosts of Leishmania are typically subclinical and benign , with varying degrees of tissue tropism , parasite loads , and infectiousness to sand flies , even when hosts live in close association with heavily infected vector populations [53 , 88] . Such observations highlight the specificity of host—parasite—vector relationships and the broad spectrum of possible modes of Leishmania maintenance and transmission . Interventions need to have sufficient geographic coverage to include transmission hot spots , especially as current field diagnostic tools do not distinguish highly infectious vectors or hosts from those that are not infectious [38] . To interrupt transmission , specific rapid tests that identify infectiousness are needed . If an intervention suitable for asymptomatically infected individuals were developed , a similar human test would be needed to enable appropriate targeting to those contributing to ongoing transmission . Interventions must be flexible enough to take the dynamics of the disease into account as the leishmaniasis transmission varies spatially and over the course of an epidemic cycle . Interventions that reduce sand fly densities in the absence of elimination could interfere with potential saliva-conferred partial immunity against Leishmania [63–70] . Such reductions could also affect vector aggregation dynamics , causing a shift in the attractiveness of sand fly leks from dead-end hosts to humans and animal reservoirs . In turn , this could affect sand fly density-dependent blood-feeding success [89]: incomplete feeding or interrupted probing may lead to multiple bites , promoting transmission within spatially defined host populations [85 , 86] . Certainly , sand fly blood-feeding behavior will determine the efficacies of indoor residual spraying , topical insecticides , and bed nets [90 , 91] . Alterations in biting behavior affecting the suitability of these methods could be induced by insecticide pressure , as observed in mosquitoes [92 , 93] , although no such studies have been conducted in sand flies . A deeper understanding of both sand fly and host biology and behavior is therefore essential to ensuring effectiveness of vector interventions and avoiding unintended counterproductive consequences . | We review recent research that sheds light on the quantitative biology of leishmanial transmission between sand flies and mammalian hosts and use these insights to better understand transmission , the observed epidemiology of the disease , and their implications in choice of control strategy . Using animal models , we show how the parasite-induced processes manipulate sand fly blood-feeding behavior and the infectious metacyclic dose to promote host infection and to differentially regulate the onward transmission potential of individual vectors and hosts . The existence of subpopulations of mammalian and sand fly “super-spreaders” provides a biological basis for the spatial and temporal clustering of clinical leishmanial disease . While tools are unavailable to distinguish these individuals in mixed populations , blanket interventions will be necessary to ensure inclusion of transmission hot spots . Interventions that reduce sand fly densities without elimination could interfere with vector—host dynamics and conferred partial immunity to host populations . | [
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"i... | 2017 | Combining epidemiology with basic biology of sand flies, parasites, and hosts to inform leishmaniasis transmission dynamics and control |
Living creatures must accurately infer the nature of their environments . They do this despite being confronted by stochastic and context sensitive contingencies—and so must constantly update their beliefs regarding their uncertainty about what might come next . In this work , we examine how we deal with uncertainty that evolves over time . This prospective uncertainty ( or imprecision ) is referred to as volatility and has previously been linked to noradrenergic signals that originate in the locus coeruleus . Using pupillary dilatation as a measure of central noradrenergic signalling , we tested the hypothesis that changes in pupil diameter reflect inferences humans make about environmental volatility . To do so , we collected pupillometry data from participants presented with a stream of numbers . We generated these numbers from a process with varying degrees of volatility . By measuring pupillary dilatation in response to these stimuli—and simulating the inferences made by an ideal Bayesian observer of the same stimuli—we demonstrate that humans update their beliefs about environmental contingencies in a Bayes optimal way . We show this by comparing general linear ( convolution ) models that formalised competing hypotheses about the causes of pupillary changes . We found greater evidence for models that included Bayes optimal estimates of volatility than those without . We additionally explore the interaction between different causes of pupil dilation and suggest a quantitative approach to characterising a person’s prior beliefs about volatility .
Adaptive engagement with the world requires an understanding of our sensations in terms of the latent ( hidden or unobservable ) states that generated them . This requires an internal ( generative ) model of the world that can be used to make predictions about sensory input [15] . These generative models are necessarily complicated ( i . e . , usually deep , dynamical and nonlinear ) , to capture the subtleties of our ( deep , dynamical and nonlinear ) environment . Despite the complexity of such models , they can be constructed by combining relatively simple models [16] . The simplest that accounts for perceptual inference and planning—in a changing environment—is a Markov decision process ( MDP ) [17] . Technically , in this paper we use a hidden Markov model ( as we do not model any decisions ) , but we retain the MDP rhetoric to emphasise that these results generalise to situations that require active sensing of the world [17 , 18] . In the following section we provide a brief outline of the inversion of this type of generative model . Readers familiar with this sort of modelling are invited to skip this section . An MDP treats the world as comprising a series of states ( s ) that are hidden from an observer . The transitions among these states over time represent the ( stochastic ) dynamics of the environment , and are defined by a ( square ) transition matrix that we denote by B ( see Fig 1 for a Bayes net representation of this process ) . These states give rise to observable outcomes that act as the observer’s sensory stimuli . The relationship between the hidden states and the outcomes they generate is expressed as a likelihood matrix , A . These probability distributions are not trivial: to motivate their importance we appeal to the Good Regulator Theorem [19] . This theorem says that ‘every good regulator of a system must be a model of that system . ’ From this , one may intuit that if a creature inhabits , and wishes to interact with , an environment defined by stochastic state transitions , this creature must be able to estimate the precision ( i . e . , the negative entropy ) of these transition densities . The ensuing inference about environmental dynamics is intertwined with beliefs regarding the likelihood mapping from states to outcomes , since it is only these outcomes that an agent can observe [17] . If the dynamics of the environment are deterministic , and state-to-outcome mappings are well understood , the agent is likely to have precise beliefs about the nature of its environment , and is therefore able to accurately predict what it may expect to see in the future [20] . However , when these mappings from states to outcomes are not deterministic and where state transitions are themselves stochastic—the agent is presented with a confound , since poor inferences about the nature of state-to-outcome mappings may have a detrimental effect on inferences about state transitions [20 , 21] . We represent these imprecise state-to-outcome mappings and state-to-state transitions in the A and B matrices , respectively ( Fig 2 ) . Imprecise state transitions ( a non-deterministic B matrix ) define a volatile environment . In other words , volatility is equivalent to the inverse precision of the B matrix . In a volatile world , even if an animal accurately infers the likelihood mappings and state transitions , the stochastic nature of these dynamics means the agent’s beliefs about what will happen next are necessarily imprecise [22] . Imprecise beliefs over state transitions leave the animal with no way of predicting what might come next . This has been referred to as ‘unexpected uncertainty’ , in contrast to ‘expected uncertainty’ ( that maps to imprecision of A ) [2 , 6] . Previous work has suggested that acetylcholine ( ACh ) and noradrenaline ( NA ) act as the neurochemical analogues of the precision over state-outcome mappings ( i . e . the A matrix ) and the precision over state transitions ( defining the empirical prior and B matrix ) [2] respectively . In other words , ACh is thought to be involved in signalling confidence in our beliefs about the likelihood of what we might see , in a given state , while NA moderates our confidence in prior beliefs about the state we may find ourselves in next [2 , 3 , 5 , 23 , 24] . In this work , we use these proposed relationships—between central noradrenergic signalling and volatility signalling—to motivate predictions about changes in pupil diameter based on environmental volatility . Given that circuits in the brain—that update beliefs over the likelihood mappings from states to outcomes—are thought to use ACh as a transmitter [2 , 23 , 25–29] , a potential confound arises: pupil diameter , particularly the tonic changes examined in this work , may also depend on beliefs about the likelihood of certain stimuli . We therefore consider the possibility that beliefs about sensory mappings may confound the effects of beliefs about state transitions on the pupillary response; acknowledging that beliefs about the likelihood might also vicariously influence beliefs about transitions . Put simply , an unexpected observation could plausibly be explained by imprecision in either the A or the B-matrix . To optimise beliefs about environmental uncertainty ( i . e . , precisions ) , we must first specify how precisions are parameterised . To pursue this formally , we express the precisions as inverse temperature parameters , such that the precision of state transitions is given by ω . This adjusts a ( source ) transition B matrix by virtue of a Gibbs measure ( i . e . , a softmax function ) , as shown in Eq 1 . Here , precision is an exponent on the elements of the transition matrix , which is then normalised , to produce the agent’s beliefs about state transitions [22] . Eq 1 describes how transition matrices are generated from a source matrix . This produces the transition matrices shown in Fig 3 , with all 4 derived from the same ‘source’ matrix . Intuitively , a high prior precision reflects high confidence in prior beliefs , and would be represented by a large value of ω . If ω were to equal infinity , this would represent absolute confidence , and results in a purely deterministic transition matrix , as is shown in Fig 3a . Smaller values of ω ( Fig 3b–3d ) represent increasingly less precise beliefs , resulting in increasingly stochastic transition matrices , and a greater propensity to accommodate randomness in the environment . The updated , normalised ( denoted by the bar notation ) , B matrix is then used to update the expected precision given new sensory outcomes . Under ideal Bayesian observer assumptions [22] this update can be cast as a gradient ascent on variational free energy ( a lower bound on log model evidence ) . Specifically , this scheme updates the volatility ( inverse precision , β = ω−1 ) using the sum of prediction errors , weighted over all possible transitions , as shown in Eq 2 . These prediction errors represent the difference between the observed state transition and the expected state transition , where the expected transition is calculated using the updated B matrix calculated in Eq 1 . The ensuing error term is shown in Eq 3 . Eqs 2 and 3 show how the inferred volatility is inextricably linked to violations of expected transitions , as inferred by the subject [22] . Here , σ refers to the softmax function . Importantly , the non-italic β in Eq 2 represents the prior beliefs an agent has regarding the volatility of the environment ( so β-1 = ω are the prior beliefs over precision ) . Eq 2 therefore shows how the agent’s posterior beliefs about the current environmental volatility ( β ) depends on their prior beliefs . The formulation in Eq 3 provides a useful intuition on belief updating for volatility or precision . It says that , for every possible current state , we compute the difference between the expected next state and the posterior beliefs about that state . These errors are weighted by the posterior probability of the current state . Larger errors then induce greater updates in beliefs about the volatility . Framing belief updating in terms of state prediction errors connects this aspect of active inference to recent work suggesting that much of noradrenergic phenomenology can be reproduced by appealing to similar error terms [30] . Eq 4 shows how we estimate posterior beliefs about the states , and the influence of the volatility on this belief updating [22] . This shows that , in a highly volatile world ( low ω ) , the influence of beliefs about the future and past should have little influence over beliefs about the present , and we should rely to a greater extent upon sensory evidence . In contrast , in minimally volatile environments , we should depend more upon empirical priors from the past and future . When inferring state trajectories , we can use these posterior beliefs to evaluate Eq 3 and update beliefs about volatility . There is a large literature on modelling of volatility in dynamical systems that rely upon autoregressive or Kalman filter like models . While important for cognitive sciences and psychology [31 , 32] , these also find application in the domain of financial modelling and economics [33 , 34] . Some of these approaches rely upon the use of stochastic differential equations for continuous variables ( or their associated density dynamics [35] ) , while others rely upon probability transition matrices . In the former , volatility is simply the variance of random fluctuations , while in the latter it takes the form of a temperature parameter . Common to all , is the notion that the current value of a latent variable does not deterministically predict the next value . All explicitly or implicitly appeal to the imprecision of predictions about the next state , given the current state , as a measure of volatility . Previous work has considered the updating of precisions in continuous state space models , using a hierarchical gaussian filter [36] . In this scheme , beliefs are held at multiple hierarchical levels , with belief updating driven by prediction errors . The precisions at each level are dynamic , and encode the uncertainty ( or the volatility ) about fluctuating continuous states of the environment [36] . Other approaches have considered a delta-rule style belief updating , which has been combined with Bayesian approaches to form a Bayesian delta rule [37 , 38] . These formulations have previously been used to examine the relationship between noradrenergic signalling and the estimation of volatility in both a neurotypical setting , with and without reward , [6 , 38] and in the case of patients with autism [14] . Indeed , optimising beliefs about the uncertainty of state transitions is an essential feature of cognitive flexibility , allowing us to anticipate changes in task contingencies . This means we can assess the relevance of recent events in predicting what might come next . This regulation of beliefs is synonymous with the learning rate in reinforcement learning [6 , 24 , 38 , 39] . In this work we focus on the uncertainty about the environmental contingencies . By formalising the Bayesian updating thought to occur in the brain [6 , 22 , 40] , we can quantify the prior precision ( i . e . , confidence ) participants afford their beliefs about environmental volatility by examining the effect on the belief-updating when presented with a unpredictable outcomes [22] . Crucially , our model makes predictions about the online encoding of uncertainty and accompanying pupillometric responses . This allows us to examine the tonic responses of the pupil without using summary statistics , as in previous work [6 , 41]: usually , trial-to-trial fluctuations in the pupil diameter are measured by taking the average dilation or the change relative to baseline . In this work , we generalise this examination of the trial-to-trial fluctuations in pupil diameter by explicitly parametrising it as a function of inferred precision [22] . This allows us to quantify a participant’s prior precisions over environmental volatility based upon observable ( pupillometry ) responses—a capability that holds promise for applications in theoretical , computational and clinical neuroscience [14 , 22 , 42] .
The study was approved by the UCL Research Ethics Committee ( Project ID Number 4356/002 ) . Both oral and written informed consent was obtained from all participants . We recruited 9 participants between the ages of 18–35 with no reported psychiatric history and neurotypical development . All participants’ data are included in the analysis , and all participants completed the 16 blocks . Each block lasted for just over 2 minutes; allowing for short breaks between blocks ( to avoid discomfort ) . Each session lasted for around 1 hour . Participants rested their head in a chinrest 0 . 5m away from a stimulus presentation screen . Dark numbers on a grey background were used to reduce the effect of pupillary dilatation in response to changes in illumination [37] , and the lights were dimmed ( high illumination leads to a constricted pupil and restricted dilatation ) . Quiet was maintained during each experiment to avoid dilation in response to auditory cues [43] , which would effect the level of surprise [6] . Pupil area was monitored in the left eye using an EyeLink 1000 desktop mount ( SR Research , sampling rate: 1000Hz ) , with calibration performed before the first experiment , as well as after any periods where the participant moved from the chin rest . While fluctuations in pupil diameter can be attributed to a luminance effect , due to the presentation of numbers on the screen , we note that this pupil dilation could alternatively ( and perhaps also ) be due to increased attention [44] . This effect has been reported during auditory paradigms: a recent study showed that the conscious processing of regularities in an auditory paradigm induces a pupillary dilation [4 , 45 , 46] . To measure the pupillary response to changes in environmental volatility , we presented sequences of numbers increasing from 1 to 8 . These were generated using a probability transition matrix ( details below ) , analogous to that used in the generative model ( see Fig 2 ) . We chose numbers because prior exposure to number sequences means that participants are ‘over-trained’ , and do not need to learn new sequences . To increase the volatility of this stimulus , we decreased the precision of the transition matrix used to generate the sequence . This introduced violations of the 1–8 sequence . Every sequence , irrespective of the number of violations , comprised 8 numbers . Each number was presented for 250ms , followed by a 250ms with no stimulus . In other words , each number was always followed by the absence of a number , such that each of these pairs lasted for 500ms . After 8 pairs , there was a delay for 1 second . The small 250ms spaces between numbers were required to make transitions distinct ( for example , without these interludes a transition from a 2 back to a 2 would simply look like an extended presentation of the number 2 ) . The 1 second breaks between sequences helped prevent transitions such as 8 to 1 as one sequence ends and another began immediately after—as this could equally well be seen as a an unexpected transition . Numbers were presented in a dark font on a grey screen; a truncated example of a sequence is shown in Fig 4e . To account for the 250ms number absences between the numbers , and a final 1s break between states ( comprising 4 consecutive 250ms number absences ) , we required a 20x20 probability transition matrix . We ensured that all numbers invariably transitioned to a number absence state , but number absence states could stochastically transition back to the previous number , or to the next number . Specifically , we specified the probability of the ‘correct’ transition ( i . e . to the next number numerically ) as 0 . 99 , and then split the remaining probability mass among the remaining possible state transitions . We then selected 4 levels of volatility that would give , on average , 0 , 1 , 2 or 3 aberrant transitions within a sequence of 8 numbers . These volatilities were used to create the matrices shown in Fig 3a–3d . To generate the sequences shown in Fig 4 , we iterated using the respective matrix 20 times , and—if the resultant sequence was suitable ( i . e . composed of 8 numbers , to ensure all sequences are the same length ) –the sequence was accepted; otherwise we generated a new sequence . A single block comprised 25 sequences in total and each participant performed 16 blocks . The first of these sequences was simply 1–8 . The remaining 24 sequences were divided into 8 sets of 3 sequences . Each set was assigned a level of precision , and the appropriate B matrix was used to generate each of the 3 sequences . The precisions of the sequences throughout the experiment , and the sequences themselves , are shown in Fig 4 . These sequences and their ordering were kept constant throughout all 16 blocks . To ensure participants maintained focus , we asked them to perform an incidental task: they were asked to tap on a tap-counter every time they saw a specific number ( this number was different in each block ) . They were explicitly asked not to count how many times they saw the target number , but rather to focus on the next number ( which they were told should always be 1 greater than the number they just saw ) . Above , we noted the possibility of an interaction between beliefs about likelihood mappings and state transitions . In the context of these sequences , we looked for this interaction by creating combination sequences for 8 of the 16 blocks that participants completed . In these sequences , we took the basic sequences given in Fig 4 , randomly selected 2 numbers and switched them for different numbers . Examples of these switches and the resultant sequences are given in Fig 4 . In summary , each participant completed 16 blocks . 8 are composed of the basic sequences detailed in Fig 4 , while 8 are combination sequences , constructed in the manner shown in Fig 4 . Importantly , in each set of 8 blocks , the sequence of numbers was exactly the same . In a post-hoc debrief , participants were asked what they noticed about the sequences . None reported that the sequences were the same ( either within the sets of 8 blocks or between the sets of 8 blocks ) , and all commented that they simply ignored the random numbers in the combination sequences , which has a heuristic similarity to the results shown by Parr and Friston ( random stimuli that have no informative value are down sampled/ignored ) [47] . Following pre-processing ( detailed in the next section ) , we compared the time series generated from the basic sequences to those generated by the combination sequences . We selected random sections of the time series and used their mean and variance to look for a statistically significant difference in the time series and found none . We ran all analyses ( see sections below ) on both data sets separately , yielding similar results ( though with somewhat larger error due to increased noise from reducing the sample size—see 9a insert for an example of these results ) . We were therefore able to pool the blocks , and do not make any further distinction between the basic and combination sequences . Following acquisition , all data were processed with the same protocols , which are well established in the literature . First , blinks were removed by identifying data for which the pupil diameter is 0 . These time points are padded by 150ms either side , removed , then replaced by linear interpolation [6 , 48] . We then regressed out the effect of a temporal drift , the presence of a violation ( of both types in the combination series ) and the presence of a target number ( those requested for the counting task ) [6 , 49] . The data were then mean centered , low-pass filtered below 10Hz , and down-sampled to 10Hz . Since the analysis we performed later was in the time domain , we had no need to respect the Nyquist frequency . Down sampling to 10Hz from 1000Hz was required since the predictions we generated ( see next section ) were generated at 4Hz . Finally , the data were normalised by their standard deviation , such that the final time series represents the number of standard deviations from the mean diameter . This ensured that we could average the data over subjects , while allowing for the fact that some participants’ responses may have overall smaller pupillary responses due to differential sensitivity to the luminance of the screen . At this point , the data from all 16 blocks for a given participant were averaged together; such that our data-space now comprises 9 time-series ( one for each participant ) performing exactly the same tasks . This allowed us to construct an ‘event related average’ , analogous to the approach used to find evoked responses in EEG research [50] . The grand mean of this average , over subjects , is shown in Fig 5 . To test our hypothesis ( that the central adrenergic system mediates Bayes optimal updating of beliefs over volatility ) , we simulated belief updating in response to our stimuli . The simulations were performed by iterating Eqs 1 , 2 , 3 and 4 in a Matlab script customised from spm_MDP_VB_X . m ( details in supporting materials ) . This scheme inverts a generative model based on an MDP to provide free energy minimizing solutions to the underlying active inference problem ( that entails the solution to Eq 2 ) . Inference about precision is assumed to proceed over a longer time-scale than state inference ( inference about the precision requires beliefs about the current state , which must be inferred from the observed outcomes over a number of time-steps ) . From Eq 2 it is clear that the inferred volatility is dependent on the prior beliefs over precision ( β-1 ) . We therefore generated simulations of ω with a range of β-1 from 0 . 3–20 . The inverse of the ensuing precision is then taken to be the inferred volatility . Different prior precisions have a profound effect on the shape and scale of belief updating , as can be seen in Fig 6 . These simulations are generated at a 4Hz frequency ( notice this is the frequency of the stimulus ) , and we therefore need to up-sample this to 10Hz ( by linear interpolation ) for comparison with our empirical data . While it may appear as if the time-course in Figs 5 and 6 depends only upon whether numerical sequences are violated , it is actually a little subtler than this . The nature of the response is highly dependent on the prior precision of the participant and the participant’s current inferences about precision . Furthermore , in the short periods of differing volatility that our experiment affords , participants with particularly high beliefs over the environmental volatility are less likely to track these small changes , since they are already expected , whereas participants with strong beliefs over the precision of the environment experience a greater prediction error and will track these changes . Notably , even as the pupil responds to individual aberrations that allow updating of beliefs about precision , the tonic state will change to reflect these beliefs . To test for the hypothesized effects of the inferred precision on pupil diameter , we took the grand mean of the data to generate a pupillometry time series , averaged over all blocks for all participants . Noting the shape of the data ( Fig 5 ) , we wanted to consider the balance between optical effects ( changes in luminance due to the numbers presenting on the screen ) and the updating of inferred environmental precision . With this in mind , we generated 4 plausible models , summarised in Table 1 . The explanatory variables detailed in this table accommodate the photic stimulation ( effect of numbers on the screen ) in each model , and then build on this to consider more comprehensive models of pupillary responses . Model 1 comprises photic stimulation only . This represents a null model . Model 2 contains the photic stimulation and an interaction effect , where the inferred precision acts in concert ( i . e . non-additively ) with the optical effects . Model 3 contains the photic stimulation and the inferred precision , suggesting that the inferred precision acts independently from optical effects to drive pupillary dilation . Finally , model 4 contains all three effects ( optical , interaction and precision ) . These models are summarised in Table 1 . We included a further two interesting models: Model 5 supposes that there are no tonic effects beyond a slow return to baseline following an unexpected event ( note this supposes that the tonic effects are simply a due to slow dynamics of the pupil in response the phasic effects ) . Model 6 supposes that the participant immediately knows the current environmental volatility , rather than having to infer it from the observed data . Taking inspiration from the field of neuroimaging , we analysed the pupillometry data using a general linear convolution model [44 , 49 , 51 , 52] , comparing the evidence for each model using Bayesian general linear regression [53] . The prior expectation of regression parameters were set to 0 within uninformative prior variance . The results presented below were robust to changes in this prior variance . This Bayesian general linear model ( GLM ) allows us to balance the increase in accuracy from additional regressors in models 1–6 with the accompanying increase in complexity . We convolved our stimuli with 5 gamma functions ( with associated parameters ) , which can be reasonably expected to model the pupillary response to our stimuli—in the spirit of a pupillary response function; i . e . , the pupillary response to neuronal afference ( modelled by inferred precision ) . While 5 gamma functions are not required to model pupillary dilation ( indeed , analysis of the parameters of each gamma function suggest only the widest gamma function is necessary ) , we did not want to make any prior assumptions about the pupillary response function , and therefore began with a range of possible functions . We retain this full range to allow for the simulations shown in the results section . Photic stimulation was modelled as a boxcar function encoding the presence of a stimulus , the interaction term is the mean centred product of the simulated precision and the optical effects , while the precision terms are generated as described above . These explanatory variables are then convolved with a basis set comprising ( five ) gamma functions , such that the design matrix for model 3 has 10 columns . We added a constant term to account for the z-scoring performed in the pre-processing . Examples of these design matrices for a β−1 of 1 . 75 are shown in Fig 7 . Model comparisons are performed with flat priors over each model , to avoid favouring one model over another .
We may perform the same analysis used above ( a Bayesian linear regression with uniform priors ) to optimise the model of data generated by each of the 9 participants . This allows one to identify the optimal prior precision for each participant . To do this , following the regression analysis , we can pass the log model evidence of model 3 –for different prior precisions—through a softmax function [53 , 55] to obtain the posterior probability over prior precision . These results are shown in Fig 9 , which shows the posterior probabilities for each of the 9 participants . In Fig 9 , we also report a confusion matrix , constructed by simulating data with different prior precisions ( rows ) , and then computing the posterior probability afforded to each level of prior precision ( columns ) by each simulated dataset . This is to demonstrate that—if we simulate data—we can easily recover the parameter used to generate the simulations ( demonstrating the sensitivity of our measure ) . To illustrate the face validity of this approach , we simulated data using the parameter values inferred for 4 of the participants . The correspondence between these and the measured data are shown Fig 9c–9f . Fig 9a shows that our participants displayed a range of β-1 , with three below the average , four close to the average and two with slightly higher β-1 . Importantly , we are able to identify an optimal value for all participants . This conclusion is reinforced by Fig 9b , where we show that if we generate simulated time series for 5 similar values of β-1 , we can accurately assign the precisions to the correct simulation; through model comparison of models with different prior precisions . This characterisation of model identifiability is reflected in the fact that the highest probabilities lie on the ( confusion ) matrix diagonal . In other words , we can recover the correct model that generated pupillometry data based on , and only on , the data themselves . To characterise the simulations that are used to find the optimal prior precision , in Fig 9c–9f we overlay the simulations on the recorded data for 4 participants , showing that the regression parameters can be used to generate plausible data . Note in Fig 9c there is a large deviation between the simulation and the recorded data around 400ms; this might explain the double peak seen in the thick blue trace in Fig 9a .
To establish the role of inferred precision ( inverse volatility ) in explaining pupillometry data , we considered 6 models , each of which has a unique physiological interpretation . Model 1 proposed that optical factors alone are sufficient to explain the data . Had Model 1 won over the others , this would have represented evidence against our hypothesis; namely , that pupil dilation tracks inferred volatility . However , we show that models 2–4—all of which include the inferred precision in some form—are superior to the optical model . Furthermore , the results suggest that the model with inferred precision acting directly on the pupil diameter ( Model 3 ) is the most effective over the largest range of prior precisions; including for the prior precisions shared by most of the participants . Interactions between inferred precision and optical effects have little explanatory value for these data . This suggests that the effect of precision on pupil diameter is distinct and separable from the optical impact ( within the bounds of maximum and minimum pupil diameter ) . Finally , we are able to estimate , in a Bayes optimal fashion , the prior precision for our participants , demonstrating the sensitivity of this estimation in relation to intersubject variability . While the results presented here are highly complementary to those in previous work taking a Bayesian perspective on pupillary dynamics ( most notably by Nassar et al , 2012 and Krishnamurthy et al 2017 , 6 , 31 ) , our approach offers two additional benefits . First , our focus is on inference , as opposed to learning . Intuitively , this generalises previous approaches that focus on the optimisation of parameters of a generative model ( learning ) to accommodate beliefs about current states of the world ( inference ) , and their changeability . Second , we have formulated our generative model to be consistent with a Markov decision process formulation of Active Inference ( 16 , 18 ) . The importance of this is threefold . This sort of model is equipped with a process theory that has been used to account for a range of behavioural and electrophysiological observations , affording it a face validity . In addition to this face validity , the capacity to use exactly the same model to generate pupillary responses and choice behaviour ( or evoked EEG responses ) provides an opportunity to test the predictive validity of our model . In future work , we hope to be able to use the estimated parameters from pupillary data for individual subjects ( or groups of subjects ) to predict what one might measure using ( for example ) electroencephalography . Finally , given established associations between other neurotransmitter systems and parameters of these generative models [2 , 22 , 56] , we are now in a position to investigate the interactions between these systems ( e . g . how does my uncertainty about the changeability of my environment influence my uncertainty in how I am going to act ? ) . While pupillary dilatation is typically associated with central noradrenergic signalling , it is notable that other neurotransmitter systems have also been correlated with these responses in both humans [57] and animals [58] . As such , the link between pupillary dilatation and the precision of transitions demonstrated here could be a manifestation of other transmitter systems in addition to ( or in place of ) noradrenaline , as well as different sources of noradrenergic stimulation [59–62] . For these reasons , we can only conclude that neuronal processes upstream of fibres projecting to the pupillary muscles are engaged in estimation of precision ( or volatility ) , and that noradrenaline is the likely substrate of this . However , to implicate noradrenaline with greater confidence , it will be necessary to dissociate this from alternative transmitters . This could be through fMRI , comparing activity in the locus coeruleus , dopaminergic midbrain , and basal forebrain nuclei . Alternatively , it could be through the use of pharmacological intervention , exploring whether a central noradrenergic blockade abolishes the responses observed here . These experiments , when paired with a suitable paradigm to probe changes in likelihood mappings , could be used to further explore the theories of Yu and Dayan and others [2 , 56 , 63 , 64] in probing the neurotransmitter systems that underlie different forms of uncertainty . Given recent work on the role of aberrant prior beliefs in autism and anxiety disorders , we also suggest that the techniques introduced above could be used to quantify group differences between neurotypical persons and people with autism . In practical terms , this would provide clinicians with a tool to quantitatively phenotype patients and provide a diagnostic aid for autism . Recent findings suggest that these measures may correlate with the severity of symptoms [14] . This suggests there may be utility in this type of phenotyping in quantifying the effects of therapeutic interventions . However , if this was to be used as a diagnostic tool , a change in experimental paradigm would be needed; autism spectrum disorders are often diagnosed very early in life ( around 3–4 years old ) [65] , an age at which children are often not yet able to count . The first step to such a tool would be to use the current paradigm to examine differences between a small group of neurotypical people and patients with autism , within the age range examined in this work ( 18–35 ) . If these experiments were successful in finding differences between the two sets of participants , subsequent studies could examine the efficacy of the paradigm in younger age groups , adjusting the paradigm to suit those not yet able to count—and those who find it difficult to focus on the stimulus . Finally , we refer to the introduction and our argument that belief updating over the precision of state transitions is essential for intelligent life . While this work simply shows that humans do appear to use Bayes optimal updating for beliefs regarding volatile state transitions , it provides a solid framework from which to launch further exploration of the subtleties of this precision updating , including its interaction with belief updating for precision of likelihood mappings and for actions . With a solid theoretical and practical understanding of these concepts , the leap to a general artificial intelligence would be less of a jump , and almost a trivial consequence of ( variational ) optimality principles . | Humans are constantly confronted with surprising events . To navigate such a world , we must understand the chances of an unexpected event occurring at any given point in time . We do this by creating a model of the world around us , in which we allow for these unexpected events to occur by holding beliefs about how volatile our environment is . In this work we explore the way in which we update our beliefs , demonstrating that this updating relies on the number of unexpected events in relation to the expected number . We do this by examining the pupil diameter , since—in controlled environments—changes in pupil diameter reflect our response to unexpected observations . Finally , we show that our methodology is appropriate for assessing the individual participant’s prior expectations about the amount of uncertainty in their environment . | [
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... | 2019 | With an eye on uncertainty: Modelling pupillary responses to environmental volatility |
Cutaneous leishmaniasis ( CL ) is a major public health problem in Libya . In this paper , we describe the eco-epidemiological parameters of CL during the armed conflict period from January 2011 till December 2012 . Current spatiotemporal distributions of CL cases were explored and projected to the future using a correlative modelling approach . In addition the present results were compared with our previous data obtained for the time period 1995–2008 . We investigated 312 CL patients who presented to the Dermatology Department at the Tripoli Central Hospital and came from 81 endemic areas distributed in 10 districts . The patients presented with typical localized lesions which appeared commonly on the face , arms and legs . Molecular identification of parasites by a PCR-RFLP approach targeting the ITS1 region of the rDNA was successful for 81 patients with two causative species identified: L . major and L . tropica comprised 59 ( 72 . 8% ) and 22 ( 27 . 2% ) cases , respectively . Around 77 . 3% of L . tropica CL and 57 . 7% of L . major CL caused single lesions . Five CL patients among our data set were seropositive for HIV . L . tropica was found mainly in three districts , Murqub ( 27 . 3% ) , Jabal al Gharbi ( 27 . 3% ) and Misrata ( 13 . 7% ) while L . major was found in two districts , in Jabal al Gharbi ( 61% ) and Jafara ( 20 . 3% ) . Seasonal occurrence of CL cases showed that most cases ( 74 . 2% ) admitted to the hospital between November and March , L . major cases from November till January ( 69 . 4% ) , and L . tropica cases mainly in January and February ( 41% ) . Two risk factors were identified for the two species; the presence of previously infected household members , and the presence of rodents and sandflies in patient’s neighborhoods . Spatiotemporal projections using correlative distribution models based on current case data and climatic conditions showed that coastal regions have a higher level of risk due to more favourable conditions for the transmitting vectors . Future projection of CL until 2060 showed a trend of increasing incidence of CL in the north-western part of Libya , a spread along the coastal region and a possible emergence of new endemics in the north-eastern districts of Libya . These results should be considered for control programs to prevent the emergence of new endemic areas taking also into consideration changes in socio-economical factors such as migration , conflicts , urbanization , land use and access to health care .
Leishmaniasis is a group of vector–borne diseases caused by obligatory intracellular parasitic protozoans belonging to the genus Leishmania . The clinical manifestations range from cutaneous and muco-cutaneous leishmaniasis ( CL and MCL ) which are characterized by localized lesions in the skin and mucous membranes , to visceral leishmaniasis ( VL ) which is the most severe form and mostly fatal in developing countries if untreated [1] . These manifestations ( dermotropic and viscerotropic ) depend on the causative Leishmania species and genotypes , the geographical origin of the cases [2–4] , and the immune response of the infected host [5] . CL is prevalent in more than 70 countries throughout Africa , Asia , South Europe , and North and South America [6] . The countries with the highest number of reported cases are Algeria , Brazil , Iran , Syria , Afghanistan , Pakistan , Tunisia and Peru [7] . All countries around the Mediterranean Sea are endemic for CL , including North Africa from Morocco to Egypt [7 , 8] where CL transmission has been increasing since the 1980s and thousands of cases are reported every year [8] . However , underreporting of CL is a serious problem in many endemic countries [7 , 9] . Four species of Leishmania were identified as causative agents of CL in the Old World . Leishmania major is most frequent and causes more than 90% of the registered cases in Algeria , Tunisia and Libya . Leishmania tropica ( syn . L . killicki ) is more prevalent in Morocco causing 30–40% of the CL cases in some districts [10] . Leishmania aethiopica is found exclusively in Africa and is considered as the main causative agent of CL in Ethiopia and Kenya [7 , 11] . Less frequently , CL can be also due to L . infantum , the well-known agent of VL in the Mediterranean region [3 , 7–9 , 12–14] . The three species L . major , L . tropica and L . infantum are sympatric in all North African CL endemic countries , although they differ in modes of transmission , zoonotic vs . anthroponotic [8 , 15] , reservoir hosts [8 , 15–19] , Phlebotomus sandfly vectors [8 , 20] , and eco-epidemiological characteristics [7 , 8 , 12 , 21] . This polymorphic presentation of CL is quite complex and challenges the national prevention and control programs established by many North African countries . Different measures applied for the containment of vectors and animal hosts failed to stop the spread of CL [22] . Moreover , no vaccine for CL is available and treatment failures are reported in many endemic countries . This has consequently increased CL burden on human health and societies [23 , 24] . In Libya , CL is distributed nearly exclusively in north-western districts where L . major is the dominant causative species followed by L . tropica [12] , while L . infantum is hypo-endemic and restricted to young people under 20 years old [14] . In 2009 an emerging focus was reported from Sirte in the northern center of the country [25] . The first case of CL caused by L . tropica was documented from the district Misrata ( Beni Walid ) in 2006 [26] . In 2012 molecular typing could prove for the first time the occurrence of this species in the districts Al Jabal Al Gharbi , Misrata , Murqub [12] , Nuqat al Khams , Zawiya , and Jafara [26] , and additionally in Nalut [14] . Four years later , the circulation of L . tropica was confirmed by molecular methods also in Tripoli and Al Jabal Al Gharbi ( Zantan and Gharyan ) [27] . The most southern proven occurrence of L . tropica was in Wadi Al Hayaa [12] . Leishmania major was found mainly in rural regions , L . tropica rather in urban areas [27] . Seasonal distribution of CL was documented and has shown a peak during November-February [12 , 14] . The reservoirs of L . major in Libya are Psammomys obesus ( sand rat ) and Meriones spp . ( gerbils and jirds ) with Phlebotomus papatasi as the transmitting vector [7 , 28] . In Misrata district Leishmania DNA was detected in Ph . papatasi and Ph . longicuspis sandfly species , however the causative species remained undetermined [29] . Parasite life cycles , transmitting vectors and reservoir hosts of CL caused by L . tropica were not yet investigated , usually L . tropica is associated with anthroponotic transmission , however , the existence of putative animal reservoirs is also discussed [8 , 30] . Diagnosis of leishmaniasis in Libya is still based on evaluation of the clinical picture and microscopy of stained skin biopsies and only recently first molecular typing studies were published [12 , 14 , 27] . Since February 2011 , Libya is living in an armed conflict ( Libyan revolution ) that led to the collapse of the old regime in October 2011 and its replacement by the National Transitional Council which declared the end of the war and the liberation of Libya . However , sporadic clashes continued across the country and fighting broke out again in January 2012 descending Libya into an ongoing low-level civil war . This situation has influenced all life aspects in Libya including migration of civilians and poorer coordination between humanitarian agencies . It also led to the aggravation of the health care system and the disruption of national disease control programs , thus accumulating risk factors that contributed to the spread and the proliferation of CL . In this study , we continue our survey of eco-epidemiological parameters of CL in Libya that started with a general analysis and molecular identification of the causative agents of cases that occurred between 1995 and 2008 [12] . Here we analyze CL cases that were recorded during the period of the armed conflict from January 2011 till December 2012 and we investigated demographic characteristics of all cases , the clinical evaluation of patients and molecular characterization of parasites . We explored the spatial distribution of CL cases and projected future distributions based on climate change scenarios that could affect occurrence and/or density of the sandfly vectors to assess the expansion potential and risk factors of the disease in Libya . Moreover , we are discussing the activities of Leishmania national control program ( LNCP ) in Libya and the consequences of the armed conflict on CL surveillance and control . We compared the results of this study with our previous study done in 1995–2008 [12] .
During the period between January 2011 and December 2012 , we investigated all patients ( in total 312 ) presenting to the Dermatology Department in the Tripoli Central Hospital ( TCH ) with skin lesions . These patients were referred for clinical evaluation and diagnosis for CL by direct smear microscopy . Patient’s skin lesion and adjacent areas were cleaned with 70% ethanol for sterilization . Tissue biopsies were taken using scalpel blades . Small incisions were made in the lesion’s margin to remove and pick up skin tissues which were then smeared on two clean glass microscope slides . One slide was stained with Wright’s Giemsa stain and the other kept for DNA extraction . Stained slides were examined for the presence of Leishmania amastigote bodies by light microscopy at 400 x magnification . Demographic data for spatial and epidemiological analysis was collected for each patient including date of infection , age , gender and place of residence . Clinical presentation including number and location of lesions and treatment response were documented . Moreover , patients were questioned about household members previously diagnosed with CL , and the presence of rodents and sandflies in their neighborhood to assess their risk . All slides were kept at 4°C until being transferred and analyzed in Al-Quds University in Palestine and Technical University of Applied Sciences Wildau , Germany . 250 μl of lysis buffer composed of 50 mM NaCl , 50 mM Tris , 10 mM EDTA , pH 7 . 4 were added to each glass slide . Materials were then scraped from slides by using filter tips and transferred into 1 . 5ml Eppendorf tubes . Triton X-100 and Proteinase K were added to final concentrations of 1% and 200 μg/ml , respectively and the tubes were incubated overnight at 60°C to accomplish cell lysis . The phenol-chloroform extraction method was applied to extract DNA from lysates as described previously [31–33] . DNA pellets were dried using a speed vacuum dryer and re-dissolved in 40 μl TE buffer ( 1mM EDTA and 10mM Tris , pH 7 . 5 ) . Additional DNA purification was carried out using a DNA purification kit ( Qiagen ) . The DNA was then kept at -20°C until use . A PCR targeting the ribosomal internal transcribed spacer 1 ( ITS1 ) of Leishmania was performed using the primer pair LITSR ( 5`-CTGGATCATTTTCCGATG-3´ ) and L5 . 8S ( 5´-TGATACCACTTATCGCACTT-3´ ) [34] including also negative and positive controls as described elsewhere [12] . This spacer is polymorphic among different Leishmania species which can be distinguished by digesting the ITS-1 amplicon with the restriction enzyme HaeIII [35 , 36] . WHO reference strains for L . major ( MHOM/SU/1973/5ASKH ) , L . tropica ( MHOM/SU/1974/SAF-K27 ) and L . infantum ( MHOM/ES/1993/PM1 ) were included as controls and for comparison in each experiment . RFLP products were separated by electrophoresis in 2% Metaphor gels in 1xTAE buffer or alternatively by using the Fragment Analyzer ( Advanced Analytical Technologies , Inc . ) . Correlative distribution modelling was performed with the database of CL cases confirmed by microscopy for the time periods 1995–2008 ( 450 cases ) , 2011–2012 ( 312 cases ) and for the whole period 1995–2012 ( 762 cases ) as occurrence data . Conclusions were made based on these data regarding the distribution of climatically suitable areas for the disease transmission based on sandfly biology . The geographical location ( latitude , longitude ) was obtained using GeoLocator ( http://tools . freeside . sk/geolocator/ ) ( version 1 . 35 ) . Based on a literature review of ecological and environmental factors affecting sandfly distribution [37 , 38] , the following eight of the 19 bioclimatic variables of Worldclim ( http://www . worldclim . org/bioclim ) [39] were selected for the current and future projections: BIO1—Annual Mean Temperature , BIO2—Mean Diurnal Range ( mean of monthly ( max temp—min temp ) ) , BIO4—Temperature Seasonality ( standard deviation *100 ) , BIO7—Temperature Annual Range ( BIO5-BIO6 ) , BIO10—Mean Temperature of Warmest Quarter , BIO11—Mean Temperature of Coldest Quarter , BIO12—Annual Precipitation , and BIO15—Precipitation Seasonality ( Coefficient of Variation ) . All climate data have spatial resolution of 5 arc-minutes ( approximately 10 km ) . To model the distribution of CL four modelling algorithms were applied: GLM ( Generalized Linear Model ) [40] , GBM ( Generalized Boosted Model ) [41] and RF ( Random Forest ) [42] included in the biomod2 R-Package version 3 . 3–7 ( https://CRAN . R-project . org/package=biomod2 ) [43] and MAXENT ( Maximum-Entropy-Modelling ) [44] Ensemble modelling [45 , 46] was performed using these four algorithms with each algorithm having the same weighting ( 25% ) . The available data from 1995–2008 , 2011–2012 and for the whole period ( 1995–2012 ) were used to calculate the current and the future projections ( 2041–2060 ) for CL . For future projections , the climate model mpi-esm-lr ( http://ccafs-climate . org/data_spatial_downscaling/ ) and the emission scenario RCP 4 . 5 were used . RCP 4 . 5 is an intermediate scenario expecting a raise in the global mean surface temperature between 1 . 1°C and 2 . 6°C until the end of the century [47] . Mapping of the derived projections was done using GIS software ( QGIS 2 . 8 . 4-Wien , http://www . qgis . org/de/site/ ) . The study design and protocols were revised and approved by the Research Ethics Committee in the Faculty of Medicine , University of Tripoli , Libya . Study objectives and procedures were explained to each patient ( respectively parent ) . Informed consent was obtained in written form from each participant ( or the parents/guardians on behalf of the children under the age of 15 ) . All samples were anonymized and given special codes which have been used for laboratory experiments and data analysis of each sample .
During the study period , 312 patients with skin lesions were investigated for CL infections at the Dermatology Department of Tripoli Central Hospital ( TCH ) . The patients came from 81 endemic areas located in 10 districts mainly in Northwest Libya . 134 patients were from Jabal al Gharbi , 50 from Tripoli District , 38 from Jafara , 42 from Murqub , 10 from Nuqat al Khams , 12 from Zawiya , 16 from Misrata , two from Jabal al Akhdar , 7 from Nalut and one from Sirte District ( Fig 1A , Table 1 ) . The case numbers collected from the respective districts for the present time period and between 1995 and 2008 in our previous study that included all microscopically confirmed cases stored in the archive of the Libyan National Centre for Infectious Diseases and Control ( LNCIDC ) [12] are compared in Fig 2A , Table 1 and Fig 1A–1C . The progression in time of the collected and verified cases including both time periods is shown in Fig 2B . The district with the highest number of cases was for both time periods Jabal al Gharbi , followed by Misrata , Nuqat al Khams and Zawiya in 1995–2008 , and Tripoli , Murqub and Jafara in 2011–2012 ( however this could be attributed to the sampling of cases in different hospitals ) . An increase of the overall number of cases per year was observed from 2004 on with the highest number of cases in 2008 till 2012 . This high number in 2011 and 2012 is striking taking into consideration , that those cases were collected only in a single hospital . Leishmania amastigotes were seen in all Giemsa stained slides by light microscopy , however no records were available concerning the parasitic load of the respective samples . The second copy of each slide was subjected to DNA extraction and species identification using PCR-RFLP . Amplification of the ITS1 region was successful for 81 patients showing the ~300 bp band of the PCR product characteristic for the Leishmania genus [35 , 36] . The digestion of this amplicon with restriction endonuclease HaeIII revealed two causative species when compared to RFLP profiles of reference strains . Leishmania major was detected in 59 of the 81 samples ( 72 . 8% ) and L . tropica in 22 ( 27 . 2% ) ( Table 1 ) . Two of the latter cases were brothers from Tarhuna in Murqub District . About 74 . 2% of the CL cases collected during this study were recorded from November to March with the highest peak in January ( 27 . 6% ) , and a decline between April and October ( Fig 3A ) . The same trend of seasonality was observed in our study during 1995–2008 when the highest peak being in January ( Fig 3B ) [12] . The percentages of cases per month were similar for both time periods ( e . g . April-October < 9% ) . Noticeable was the very high number of cases in January in the time period 2011–2012 . Analysis of the whole data set collected during 1995–2012 showed that 71 . 6% of cases were recorded during the months November-March with a maximum of cases of 21 . 1% in January ( Fig 3C ) . Seasonal distribution of causative species showed a peak of L . major from November–January ( 69 . 4% ) , while L . tropica cases peaked in January and February ( 41% ) and no cases ( with one exception in April ) between April and August ( Fig 3A ) . In the time period 1995–2008 the majority of cases were recorded in February , however there was a continuous occurrence of several cases in the summer months ( Fig 3B ) . The seasonality analysis of cases of the combined time period ( 1995–2012 ) is shown in Fig 3C: L . tropica is recorded with the same frequency and in low numbers all over the year with a maximum of cases in January and February ( 13% and 24 . 1% , respectively ) ; L . major cases were recorded mainly from November till January ( 56 . 7% ) . The male: female ratio in the present study was 1 . 29: 1 . The average age at the onset of disease was 30 years ranging from 6 months to 85 years ( median = 28 ) . The 312 patients included in this study presented with single ( 170 cases , 54% ) or multiple lesions ( 142 cases , 46% ) , respectively . 77 . 3% of L . tropica CL lesions were single compared to 57 . 7% of L . major lesions . The lesions appeared typically on exposed parts of the body , most commonly on face and extremities ( arms , legs and feet ) . Table 2 shows per-species distribution of CL cases with site and frequency of lesions . These lesions were usually painless and evolved from papules to nodular plaques to ulcerative lesions with depressed centers , raised borders and variable surrounding indurations . Some of the lesions were covered by crust in the central part and became painful especially after secondary bacterial infection . However , nodular lymphangitis was noticed in many cases . Fig 4 summarizes the clinical symptoms of CL in our patient cohort . Interestingly , one patient from Zuwarah ( Nuqat Al Khams district ) had a leishmaniasis recidivans ( lupoid ) with a recurrence of lesions two years after successful treatment ( Fig 4A ) . All patients were treated successfully with intramuscular sodium stibogluconate ( Pentostam ) pentavalent antimony applying 20 mg Sb5 per kg body weight daily for 21 days . No changes in treatment regime and response were reported in this patient cohort . Five CL patients among our studied cases were seropositive for HIV . All of them were males between 22 and 47 years old with an average age of 37 . 4 years . They were treated efficiently with Pentostam and no treatment failure was reported . Patients were surveyed about the occurrence of previous CL infections of household members , and the presence of rodents and sandflies in their neighborhood . One hundred and sixty-eight patients ( 53 . 8% ) reported the presence of at least one previously infected household member . The presence of sandflies or rodents or both was reported by 230 patients ( 73 . 7% ) , 56 patients ( 17 . 9% ) did not recognize their presence and 26 patients ( 8 . 3% ) did not provide data . Spatiotemporal analysis based on climatic conditions shows different climatic suitabilities for disease transmission in different parts of the country . This analysis included all microscopically confirmed clinical cases of CL , considering also the different periods of data collection . Fig 5 shows the present and potential future climatic suitabilities for CL cases for the period 1995–2008 ( Fig 5A ) , 2011–2012 ( Fig 5B ) and the total period 1995–2012 ( Fig 5C ) . The future projection was calculated for the years 2041–2060 . Molecular typing results also were added to the presence projections ( indicated as different symbols in the map ) , showing the occurrence and the number of L . major and L . tropica in the different regions of the country , however including only the respective subset of cases that were PCR positive .
Although several countries in North Africa , including Libya , have established national control and surveillance programs for containing the sandfly vector and recommending regimes for the treatment of CL infections , the disease continues to spread drastically . During the study period ( 2011–12 ) , no changes were noticed in the geographical distribution of CL cases in Libya . Most cases came from the northwestern districts adjacent to the Mediterranean Sea that have climatic and environmental conditions favorable for the spread of CL . This distribution is consistent with previous studies done in Libya and in other North African countries [12 , 13 , 30 , 48–50] . Interestingly , L . tropica was found mainly in the three districts Jabal al Gharbi , Murqub and Misrata in both study periods . This distribution was confined to the urban areas of these districts compared to L . major which was found mainly in rural regions . These findings are in agreement with a recent study done by El-Badry et al . [27] . The regions with the highest climatic suitabilities for CL in the presence are found in the northwestern part of the country , where the main CL foci are located , including the districts Nuqat Al Khams , Zawiya , Jafara , Tripoli , Murqub and the northern parts of Nalut , Misrata and Jabal Al Gharbi ( including the Djebel Neffoussa mountain chain ) as well as the coastal region of Sirte . Further regions with a high climatic suitability are projected in the northeast of Libya in the districts Benghazi , Marj , Jabal Al Akhdar and Derna . Generally , there is high risk of CL occurrence due to climatic suitability for the vectors along almost the whole coastal region , with exception of the central coastline ( Al Wahat ) . However , in Sirte only a very narrow band of the coastal region has a high suitability . Besides the coastal regions , also in the western central part of Libya regions are projected as suitable for the occurrence of CL . These regions are located in the districts Wadi Al Hayaa , Sabha and Wadi Al Shatii . The future projections indicate a stabilization of the northwest as a highly suitable region and an increase of the suitability in the northeast including also new parts in the most eastern coastal region ( district Butnan ) . In general , the coastal regions that possess a high suitability and hence a high CL risk will expand slightly to the interior parts of the country . The foci in the central western regions of Libya are projected to disappear in the future and only the very North of the country will be at high risk of CL . Spatiotemporal projections based on climatic conditions showed that coastal regions are projected to offer more favourable conditions for vectors transmitting Leishmania and therefore are at higher level of risk . Moreover , the future projection until 2060 indicates an increasing suitability for CL in the north-western regions , and a possible emergence of new endemics in the north-eastern districts of Libya ( potential spread of CL along almost the whole coastal regions which are characterized by a Mediterranean climate ) . Conversely , a decreasing suitability can be expected in the central districts Wadi al Shatii , Sabha and Wadi al Hayaa because of the projected climate change with respect to the included bioclimatic variables . These changes include an increase of the temperature and a decrease of the humidity to a level that cannot be tolerated by the transmitting sand fly vectors . These results should be considered for monitoring and vector control programs to prevent the emergence of new endemic areas . The present conclusions are principally in line with recent findings from Samy et al . ( 2016 ) [51] where they projected the potential distribution of L . major along the coastline of Libya . Further analyses of the niche breadth of L . major revealed a preference for low elevations and maximum temperatures below 25–37°C [52–54] . The distribution of the vector depends on the environmental conditions . One of the most important factors in the survival , development , behaviour and activity of sandflies is the temperature [55] . Sandflies have a nocturnal activity , hence the daily maximum temperature is less important , but daily minimum temperature that normally occurs in the evening and at night is crucial . Another factor is the monthly activation , where the maximum and minimum temperature have a huge influence on survival , complete generation , quantity and fertility of sandflies as well as disease transmission . Humidity coming as rainfalls and condensation is important for the egg and larvae growth with a lesser role in adulthood . Therefore , as an independent factor in disease transmission it has less importance in adults but indirectly is very important in the pre-adult stages to reach the stage of a mature individual . However , in general humidity is less important than temperature . Climate change has the potential to increase the incidence and geographic range of leishmaniasis in Africa [56] . The incidence is associated with rainfall and minimum temperature . Expected climatic changes in North Africa are related to a likely increase in annual minimum and maximum temperature , where the minimum temperature is assumed to experience a greater increase . In contrast , precipitation has experienced a strong decrease over the last decades , especially in winter and early spring . Future projections expect a reduction in rainfall with seasonal drying and warming . On the other hand , anthropogenic changes , e . g . the construction of dams , can change the temperature and moisture of soils and therewith the vegetation leading to changes in sandfly and rodent density and composition [56] . Hence , further research is needed due to the manifold underlying factors and their potentially contrasting effects . When comparing demographic characteristics of patients in the present study , male to female ratio and patient’s average age and median did not show any significant differences and were constant with all studies done before in Libya [14 , 25 , 57–59] . Per-month distribution of CL cases during 2011–2012 was consistent with the seasonal occurrence of CL reported in Libya during 1995–2008 [12] . The only difference observed was the remarkable high number of cases in January in the present data set . The consistent seasonality was probably related to the sandfly’s biting season around the Mediterranean Sea which extends from May to October [54] , different biting behaviors of transmitting sandfly species of L . major and L . tropica [60] and to the incubation period after infection which may last from one month for L . major to three months for L . tropica [58 , 61] . The clinical presentation of CL caused by L . major and L . tropica in Libya in terms of site and frequency of lesions is similar to that in other North African and Middle Eastern countries [30 , 62 , 63] . However , we reported one case of leishmaniasis recidivans in the Zuwarah District , which is a prolonged , relapsing form of CL that may persist for many years with a chronic and relapsing course . This form of leishmaniasis is not very common in Libya . L . tropica has been shown to be the causative species of this rare variant in the Old World . Molecular identification of the causative CL species was , however not possible for this patient . Data about Leishmania-HIV co-infection in Libya is scarce and there was no report until 2014 [64] . Diagnosis of CL was based on microscopy and molecular identification of the causative species for this CL/HIV co-infection has failed unfortunately . An atypical presentation of CL was earlier reported from a Libyan HIV patient from Jabal al Gharbi who presented with contact dermatitis-like symptoms which was confirmed as localized CL due to the observation of Leishmania bodies in stained slit-skin smears after weeks of wrong treatment [64] . Thus , the development and implementation of diagnostic and control programs against CL-HIV coinfections in Libya is crucial . The causative CL species in our sample collection was mostly L . major , and L . tropica was less prevalent . The clinical picture for all patients was compatible with the WHO manual for the management of CL cases without significant clinical differences depending on the infecting species [65] . The presence of L . major and L . tropica as causative agents of CL in Libya is consistent with all studies done previously in different CL foci in North Africa [8 , 12 , 21] . However , CL cases due to L . infantum were not detected though it has been described recently in the Nalut District [14] . This form of CL is probably not very common in Libya . Leishmania infantum is mainly causing VL in North African countries as also in general , the causative agent belongs to the MON-1 zymodeme and is distributed in the northern parts of these countries [8 , 65] . CL by L . infantum is rather rare , caused by dermotropic zymodemes as MON-24 and MON-80 and found mainly in the central parts of the countries as described in Algeria and Tunisia [2 , 3] . Molecular detection of L . infantum in CL lesions could be hindered by many factors , such as low intra-lesional parasitic load , DNA degradation and PCR inhibition . The same factors may also explain the low number of PCR positives in our sample set . Hence , DNA purification and the use of inhibition control should be mandatory for molecular diagnosis and species identification of Leishmania isolated from clinical samples . Another reason of the low number of PCR positives might be that we used duplicates of the clinical sample slides and in case of low parasitic load the outcome from DNA isolation might be too low . Some samples showed faint PCR bands , that were not suitable for RFLP analysis and hence were excluded from the study . The infecting agent affects treatment options , efficacy and duration as described by the WHO manual for case management of CL in the WHO Eastern Mediterranean Region [62] , therefore species identification by molecular methods should be introduced to all endemic areas for better detection , diagnosis and well-directed treatment of CL . The high percentage of infected household members ( 53 . 8% ) in our sample and the detection of two brothers in Tarhuna infected with L . tropica ( known as anthroponotic in many densely populated areas [66–68] indicated possible person-to-person transmission among households in Libya . The latter was identified as risk factor for CL and VL in many countries [33 , 69] , hence screening of household members of CL patients should be considered for better surveillance of the disease . The life cycles of CL caused by different species of Leishmania in Libya need to be fully investigated since sandflies and rodents , or both , were frequently observed in the patient’s neighborhood . This will lead to a better understanding of disease dynamics and risk factors . Pentostam is the first line treatment of CL in Libya . No significant changes in treatment regime and response were reported and treatment failure or resistance are not common . However , treatment unresponsiveness to Pentostam in a HIV co-infected Libyan CL patient was recently described [64] . Alternative therapy of such unresponsive cases , and when IM Pentostam is contra-indicated , includes combination of oral rifampicin ( 600 mg/day ) and isoniazide ( 300 mg/day ) , thermotherapy and cryotherapy with or without intra-lesional Pentostam [64] . In 1991 , the Dermatology Department in TCH was established to offer diagnostic and treatment services for leishmaniasis patients throughout Libya . Since then , many hospitals and clinics started to collaborate in the CL management at the national level . This included the establishment of 16 local leishmaniasis clinics by the LNCP in all over Libya which performed clinical diagnosis of CL and offered free treatment to all patients . Moreover , they were part of control and surveillance activities organised by LNCP and were involved in data collection and reporting of cases , spraying against sandfly vectors and control measures to contain putative reservoir hosts such as rodents and dogs . All leishmaniasis control activities offered by the LNCP clinics were suspended in 2011 due to the armed conflict . The latter hindered all efforts to introduce molecular diagnostic techniques for CL even to large cities in Libya , since traveling of experts from Libya for training abroad is intricate and recruitment of international experts is convoluted . Furthermore , all control and surveillance activities of CL during 2011–2012 had to be suspended due to the fact that most endemic areas were considered to be war zones and also due to the loss of equipment , infrastructures and vehicles owned by LNCP clinics . This situation has pushed many CL patients from the CL foci in northwest Libya to seek medical help in Tripoli Central Hospital TCH and other hospitals in more secure districts . The stock of Pentostam was adequate only in Tripoli hospitals and clinics . However , drug delivery to local leishmaniasis clinics in remote areas was completely suspended during 2011 until early 2012 when they partially reopened and started to work on part-time bases . Moreover , massive movement of naïf population was seen during 2011–2012 for seeking secure or more peaceful regions . Migration and a reduced sanitary environment has increased the exposure to CL during this period and is considered as an accumulative risk factor . It should be emphasized that the reporting system of leishmaniasis in Libya is still interrupted and accurate information about disease spread and burden are not fully available . | Cutaneous leishmaniasis ( CL ) is a skin infection caused by a single-celled parasite that is transmitted by the bite of a phlebotomine sandfly . CL is the most common form of leishmaniasis characterized by localized lesions in the skin and mucous membranes . The disease is prevalent in all countries around the Mediterranean Basin . In this paper , we describe spatiotemporal and eco-epidemiological parameters of CL in Libya . Moreover , we explored current spatiotemporal distributions of CL cases and explored the future projection of the disease . Our study indicates the presence of higher risk of CL in the coastal regions of Libya . Future projection until 2060 showed a trend of increasing incidence of CL in the north-western part of Libya , a spread along the coastal region and a possible emergence of new endemics in the north-eastern districts of Libya . These scenarios should be considered by health authorities in order to develop appropriate intervention strategies and plan effective control programs . | [
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"sym... | 2017 | Spatiotemporal and molecular epidemiology of cutaneous leishmaniasis in Libya |
Salmonella enterica serovar typhimurium extensively remodels the host late endocytic compartments to establish its vacuolar niche within the host cells conducive for its replication , also known as the Salmonella-containing vacuole ( SCV ) . By maintaining a prolonged interaction with late endosomes and lysosomes of the host cells in the form of interconnected network of tubules ( Salmonella-induced filaments or SIFs ) , Salmonella gains access to both membrane and fluid-phase cargo from these compartments . This is essential for maintaining SCV membrane integrity and for bacterial intravacuolar nutrition . Here , we have identified the multisubunit lysosomal tethering factor—HOPS ( HOmotypic fusion and Protein Sorting ) complex as a crucial host factor facilitating delivery of late endosomal and lysosomal content to SCVs , providing membrane for SIF formation , and nutrients for intravacuolar bacterial replication . Accordingly , depletion of HOPS subunits significantly reduced the bacterial load in non-phagocytic and phagocytic cells as well as in a mouse model of Salmonella infection . We found that Salmonella effector SifA in complex with its binding partner; SKIP , interacts with HOPS subunit Vps39 and mediates recruitment of this tethering factor to SCV compartments . The lysosomal small GTPase Arl8b that binds to , and promotes membrane localization of Vps41 ( and other HOPS subunits ) was also required for HOPS recruitment to SCVs and SIFs . Our findings suggest that Salmonella recruits the host late endosomal and lysosomal membrane fusion machinery to its vacuolar niche for access to host membrane and nutrients , ensuring its intracellular survival and replication .
Salmonella enterica serovar typhimurium ( hereafter Salmonella ) is a Gram-negative facultative intracellular pathogen that causes gastroenteritis in a human host and a typhoid-like disease in mice . Salmonella replicates inside the non-phagocytic and phagocytic mammalian host cells in a unique membrane-bound vacuolar compartment known as the Salmonella-containing vacuole or SCV . Modulation of SCV association with the host endocytic machinery and reorganization of the host late endosomes and lysosomes is a major virulence strategy used by this pathogen . Salmonella invasion into the host cell and its replication inside the SCV is facilitated by bacterial effector proteins translocated into the host cytosol by its two type III secretion systems ( T3SS ) -1 and ( T3SS ) -2 , encoded by the Salmonella pathogenicity island ( SPI ) -1 and -2 respectively [1 , 2 , 3] . During early time points of infection , SCV acquires markers of early endosomes including Rab5 , EEA1 ( Early endosome antigen 1 ) , SNX1 , and PI ( 3 ) P [4 , 5 , 6] . Within 30–60 min post infection ( p . i . ) , early SCV matures into late SCV by loss of early endosomal proteins and simultaneous acquisition of selective late endosomal and lysosomal proteins including Rab7 , lysosomal glycoproteins ( lgps ) such as , LAMP1 and LAMP2 and v-ATPases [5 , 7] . Although the SCV acquires characteristics of late endocytic compartments including acidification , it does not become bactericidal , due to reduced presence of lysosomal hydrolyases [8] . Onset of bacterial replication in host cells begins at 3–4 hr p . i . and coincides with the formation of a tubular membrane network that emanate from the SCV , known as Salmonella-induced filaments ( SIFs ) [9 , 10 , 11] . SIFs have been observed in both Salmonella infected epithelial cells and phagocytic cells , and are characterized by presence of lgps such as LAMP1 [12] . Recent studies have shown that early SIFs formed during 6–8 hr p . i . are highly dynamic structures that continuously acquire content from the late endosomes and lysosomes of the host cell [10 , 13 , 14] . Detailed ultrastructure analysis of SIFs has revealed that a subset of these are double membrane structures wherein the space ( that harbors the bacteria ) between the outer and the inner lumen is accessible to content from the host late endosomes and lysosomes [13] . Notably , this crosstalk with the host’s endocytic compartments is essential for supply of nutrients to the SCV . As previously reported , auxotrophic strains of Salmonella acquire external amino acids by inducing SIF formation and redirecting host vesicular transport to the SIFs and SCV membranes [14 , 15] . Moreover , as SIFs are a large interconnected network of tubules forming a continuum with SCVs , this is proposed to rapidly dilute the antimicrobial activities transferred to the bacterial vacuole upon content mixing with the host late endosomes and lysosomes , preventing degradation of the vacuolar Salmonella [14] . These studies signify a crucial role of Salmonella effectors that mediate SCV interaction with the host endocytic machinery and SIF formation required for the survival and replication of this pathogen within its intravacuolar niche . Several T3SS-2 effectors including SifA , SseJ , SseG , SseF , SopD2 , and PipB2 contribute to SCV maturation , vacuole integrity and SIF formation [16] . The most severe phenotype on the intracellular Salmonella growth is observed with strains lacking SifA that are highly attenuated in systemic infection and replication [17 , 18] . Functionally , SifA regulates the integrity of SCV and is essential for SIF formation [19] . SifA interacts with the host protein SKIP ( SifA and Kinesin interacting protein ) /PLEKHM2 ( Pleckstrin homology and RUN domain containing protein M2 ) that in turn bind to the motor protein , kinesin-1 [8 , 20 , 21] . SifA-mediated SKIP recruitment on SCVs is thought to relieve the auto-inhibition of kinesin motor , which in turn promotes the microtubule-dependent extension of SIFs [22] . SifA also interacts with the host protein PLEKHM1 ( Pleckstrin homology and RUN domain containing protein M1 ) , which has similar domain architecture as SKIP , and regulates membrane biogenesis of the SCV compartment , and intracellular Salmonella proliferation [23] . Although components of the host late endosome-lysosome fusion machinery are known to localize to SCV and SIFs ( such as Rab7 [24] and Arf-like ( Arl ) GTPase8b [25] ) , but their function in Salmonella replication and whether Salmonella modulates their recruitment for its own survival needs further exploration . Lysosome fusion with other membrane-bound compartments requires the small GTPases Rab7 and Arl8b and their effectors; PLEKHM1 and tethering/docking factor HOPS complex , respectively as well as the SNARE proteins [26 , 27 , 28 , 29 , 30 , 31 , 32] . HOPS complex is an evolutionarily conserved multisubunit tethering complex ( MTC ) that mediates lysosome fusion with late endosomes , phagosomes , and autophagosomes [33] . HOPS is a hexameric complex where four of the six subunits namely , Vacuole protein sorting ( Vps ) 11 , Vps16 , Vps18 and Vps33a form the core complex , and Vps39 and Vps41 are the accessory subunits [34] . The four core subunits of the HOPS complex are shared with CORVET ( class C core vacuole/endosome tethering ) , an early endosomal MTC . Vps3/TGFBRAP1 ( Transforming Growth Factor Beta Receptor Associated Protein 1 ) /TRAP1 and Vps8 are the accessory subunits of the hexameric CORVET complex [35 , 36] . In yeast and in mammalian cells , CORVET complex is recruited on to the early endosomal membranes by TGFBRAP1 binding to early endosomal Rab protein , Rab5 [37 , 38] . In yeast , but not in mammalian cells , Rab7 directly binds to the accessory subunits Vps39 and Vps41 to recruit HOPS complex to the vacuolar membranes , promoting their homotypic fusion [27 , 39] . Interestingly , in metazoans including C . elegans and in mammalian cells , Vps41 subunit of the HOPS complex binds to Arl8b , which then mediates assembly of HOPS complex on lysosomes [27 , 28 , 40] . Fewer studies have explored the role of mammalian HOPS subunits in maturation of pathogen-containing vacuole . Previous work has shown that HOPS complex plays an inhibitory function in regulating intracellular survival of the pathogen Coxiella burnetti by mediating fusion of bacterial phagosomes with lysosomes [41] . Consequently , C . burnetti-mediated phosphorylation of Vps41 subunit of the HOPS complex prevents its membrane localization , and thereby , its function in phagolysosome fusion . HOPS complex has also been shown to be essential for Ebola virus replication with loss of HOPS subunit expression preventing viral escape to the cytosol from host’s late endosomes/lysosomes [42 , 43] . Although SCV compartment is known to acquire content from the late endocytic compartments of the host cell [9 , 12] , little is known if Salmonella employs HOPS complex to mediate fusion with host endolysosomes . Previous studies have shown that HOPS subunit Vps39 interacts with SKIP/PLEKHM2 and PLEKHM1 , both of which bind to the Salmonella effector SifA [27 , 29] . Moreover , HOPS complex is an effector of the small GTPase Arl8b that localizes to SCVs and SIFs in Salmonella-infected HeLa cells [25 , 27] . A more direct evidence of HOPS function during Salmonella infection was shown where depletion of HOPS subunits ( similar to PLEKHM1 depletion ) altered SCV morphology with multiple bacteria present within a single enlarged vacuole [23] . However , Salmonella infection in these experiments was visualized after 20 hr p . i . while SCV interaction with host late endosomes/lysosomes and SIF formation is observed as early as 6 hr p . i . [9] . Thus , with regard to the role of HOPS complex in Salmonella infection , several important questions remain unanswered , for instance , whether HOPS complex regulates Salmonella replication , does it regulate SCV maturation and SIF formation and what are the bacterial and host factors required for recruitment of HOPS complex to SCVs and SIFs . Here , we demonstrate an essential role of HOPS complex in mediating intracellular Salmonella replication in non-phagocytic and phagocytic cells and in a mouse model of Salmonella infection . Live-cell imaging experiments and transmission electron microscopy ( TEM ) studies revealed that SIF formation and fusion of mature SCVs with late endosomes and lysosomes were severely compromised upon depletion of HOPS subunits . Consequently , nutrient access to SCVs from the host late endocytic compartments was also impaired upon HOPS depletion . Notably , we found that bacterial effector SifA , in complex with the host protein SKIP , interact with HOPS complex and mediate HOPS localization to SCVs , enabling fusion with LEs/Lysosomes . Surprisingly , we did not find a role PLEKHM1 in mediating HOPS recruitment to SCVs although previous studies had shown that it independently binds to both SifA and Vps39 [23 , 29] . In conclusion , our results demonstrate that Salmonella recruits the host vesicle fusion machinery to gain access to nutrients and membranes from the late endocytic compartments to build its replicative niche inside the host cells .
To investigate the role of HOPS complex in SCV maturation and fusion with late endosomes/lysosomes , we first examined the time-dependent localization of HOPS subunits to SCVs and SIFs in Salmonella-infected human epithelial cell line ( HeLa ) . Vps41 that recruits other subunits of the HOPS complex to lysosomes [27] , showed weak association with early SCVs at 10 min p . i . Most SCVs were positive for the early endosomal marker-EEA1 at this time point ( S1a Fig ) . Recruitment of HOPS subunits , Vps41 and Vps18 , around the SCVs was observed starting at 1 hr p . i . ( 53±4% for Vps41 ) that became more evident by 3 hr p . i . where 74±1% SCVs were positive for Vps41 ( Fig 1a , 1b and 1f; S1b and S1c Fig ) . By these time points , SCVs undergo maturation and as shown in the images acquire late endosomal/lysosomal marker- LAMP1 ( Fig 1a and 1b; S1b and S1c Fig ) . Epitope-tagged Vps41 and Vps33a were similarly recruited to the mature SCVs in Salmonella-infected HeLa cells that were briefly treated with mild-detergent prior to fixation to remove the cytosolic signal of the overexpressed proteins ( S1f–S1i Fig; quantification of HA-Vps41 SCVs shown in Fig 1g ) . Prior treatment with detergent resulted in non-specific nuclear staining , as observed in confocal micrographs of the transfected cells ( S1f–S1i Fig ) . Notably , endogenous and epitope-tagged HOPS subunits also localized to LAMP1-positive vesicles , supporting their reported subcellular distribution to late endosomes/lysosomes ( see red arrowheads in insets of Fig 1a and 1b ) . We were unable to observe the subcellular localization of the HOPS-specific subunit-Vps39 in these experiments , due to lack of an antibody against the endogenous protein and the fact that its overexpression results in striking coalescence of lysosomes into large aggregates [44] . Previous studies have shown that four of the six subunits of the HOPS complex ( Vps11 , Vps16 , Vps18 and Vps33a ) are shared with CORVET , which is an early endosomal tethering factor [35] . To determine whether CORVET complex also localizes to SCV membranes , we analyzed distribution of epitope-tagged CORVET specific subunit-TGFBRAP1 in Salmonella-infected cells at 10 min , 30 min , 1 hr , 3 hr and 6 hr p . i . ( S2a–S2e Fig ) . As expected , TGFBRAP1 colocalized with EEA1 ( see yellow arrowheads in insets of S2a Fig ) but not LAMP1 . Further , little or no recruitment of TGFBRAP1 on SCVs and SIFs was observed at different time points of infection ( S2a–S2e Fig ) . Beginning at 6 hr , but better visualized at 10 hr p . i . , HOPS subunit Vps41 localized to >93±4% SCVs and also localized to SIFs , identified by co-immunostaining for LAMP1-a well-characterized marker for these tubular membranes that frequently extend from the surface of mature SCVs ( Fig 1c and 1d ) . We also observed a similar striking localization of Vps18 subunit of the HOPS complex to SCVs and SIFs in infected cells beginning at 6 hr , but primarily at 10 hr p . i . ( S1d and S1e Fig ) . Localization of HOPS subunits to SIFs was also verified by expressing epitope-tagged-Vps41 and -Vps33a in Salmonella-infected cells ( S1j–S1m Fig ) . We observed that localization of Vps41 appeared to be discontinuous and in discrete domains along the length of the SIFs . Previous studies have described similar discrete distribution of LAMP1 on SIFs attributed to poor preservation of the tubular membranes in fixed cells [10 , 16] . To elucidate whether the punctate localization of HOPS subunits observed on the SIFs was due to fixation , we infected HeLa cells with Salmonella constitutively expressing monomeric DsRed ( DsRed-Salmonella ) followed by transfection with GFP-tagged Vps41 at 2 hr p . i . Live-cell imaging at 9–10 hr p . i . revealed Vps41 was present around the SCVs and on SIFs in a continuous manner rather than as discrete domains ( S2k Fig and S1 Movie ) . To reduce the cytosolic signal that interfered with visualizing the membrane localization of overexpressed Vps41 , we co-expressed small GTPase Arl8b , which recruits Vps41 to lysosomes ( Fig 1e and S2 Movie ) [27] . Moreover , as shown in a previous study [25] and as can be appreciated in S2f–S2j Fig , Arl8b itself localizes to SCVs starting at 1 hr p . i . , and to SIFs at 6 hr and 10 hr p . i . , and is an excellent marker to visualize these compartments . Notably , we found that GFP-Vps41 was completely cytosolic and failed to localize to SCVs in CRISPR/Cas9 Arl8b-knockout cells ( S2l and S2m Fig and S3 Movie ) . Quantification of Vps41-positive SCVs at 10 hr p . i . in wild type ( WT ) - and Arl8b knockout-HeLa cells demonstrated an essential role of Arl8b in recruitment of HOPS complex to SCV membranes . ( S2n Fig; mean percentage Vps41-positive SCVs in WT: 91±2% and Arl8b KO: 6±1% ) . Consistent with this role of Arl8b , we found a striking recruitment of GFP-tagged Vps41 to SCVs and SIFs in Arl8b co-expressing cells where Vps41 was present around the SCVs and SIFs in a continuous manner , with fewer Vps41-positive vesicles remaining in the cell ( Fig 1e and S2 Movie ) . We also observed the dynamic extension and retraction of Vps41-labeled SIFs in these cells ( see white arrowheads in Fig 1e and S2 Movie ) . Further , Vps41-positive vesicles were also observed to fuse with the existing tubules and vesicles that were moving along the length of the tubules ( see red arrowheads in Fig 1e ) . Next , we examined whether recruitment of HOPS subunits to Salmonella-associated membranes increased as a function of time . To analyze this , we resolved the Salmonella-infected homogenates by two-step density gradient ultracentrifugation and confirmed the presence of Salmonella by immunoblotting with antibodies against the bacterial protein-DnaK ( Fig 1h , fractions 8–10 ( labeled as SCV fraction ) ) . Comparison of the Salmonella-infected cell homogenates processed at 3 hr and 8 hr p . i . demonstrated that endogenous HOPS subunits along with LAMP1 ( a known SCV marker ) were enriched in the SCV fraction from 3 hr to 8 hr p . i . ( Fig 1h ) . As expected , the early endosomal marker-EEA1 was associated with the SCVs at 3 hr p . i . but not at 8 hr p . i . Similarly , CORVET-specific subunit- TGFBRAP1 was weakly associated with SCV fractions at 3 hr p . i . but not at 8 hr p . i . , supporting our earlier results of little or no recruitment of TGFBRAP1 to early SCVs ( Fig 1h ) . To verify HOPS enrichment on late SCVs and SIFs , we also employed a recently described method of SCV isolation by immunoprecipitation ( IP ) of SseF-an integral membrane SPI2-T3SS effector protein [45] . As shown in Fig 1i , HOPS subunits were specifically enriched in the SseF-IP eluate but not control IP with levels comparable to the known SCV markers , such as LAMP1 and Rab7 . In contrast , little or no co-IP of GAPDH or Catalase with SseF was observed , substantiating the specificity of this approach for SCV isolation ( Fig 1i ) . Our results indicate correlation between recruitment of HOPS complex with time points wherein SCV is known to acquire content from late endosomes and lysosomes [9] . Indeed , in a recent study by Santos et al . , where proteomes of early SCV and late SCV were compared , enrichment of HOPS subunits Vps11 , Vps16 , and Vps18 was observed in the late SCV fractions [46] . To elucidate the significance of HOPS complex during Salmonella infection , we assessed the intracellular replication of Salmonella in cells depleted of various HOPS subunits . Western blotting and qRT-PCR analysis confirmed efficient depletion of HOPS subunits in HeLa cells ( S3a–S3e Fig ) . Control- and HOPS specific-siRNA treated HeLa cells were infected with Salmonella and fixed at 2 hr and 10 hr p . i . , and labeled with anti-Salmonella antibodies to enumerate the intracellular bacterial load by immunofluorescence microscopy ( Fig 2a ) . At 2 hr p . i . , both control- and HOPS-siRNA treated cells showed a similar bacterial load with ~35% cells containing 6–10 bacteria/cell , ~17–30% of cells containing 11–20 bacteria/cell , and ~2–7% of cells were containing >20 bacteria/cell . These results suggest that HOPS complex is not required for Salmonella invasion into the host cells ( Fig 2a ) . In contrast to this early time point of infection , at 10 hr p . i . , while ~73% of control siRNA treated cells had >20 bacteria/cell and <13% had 11–20 bacteria/cell , only 10–30% of HOPS siRNA treated cells showed a similar bacterial load with almost equal distribution of cells containing either 6–10 bacteria/cell ( 20–30% ) or 11–20 bacteria/cell ( 30–35% ) ( Fig 2a ) . These results indicate a severe defect in bacterial replication upon depletion of HOPS subunits . We corroborated these observations by determining the number of Colony Forming Units ( CFUs ) present in control- and HOPS siRNA-treated HeLa cell lysates at 2 hr and 10 hr p . i . As shown in Fig 2b ( quantification of CFUs/well ) , we observed a ~3 fold increase in bacterial replication in control cells , while only ~1 . 09–1 . 4 fold increase was observed in HOPS-depleted cells . Consistent with our previous data depicting weak or no association of CORVET subunit TGFBRAP1 with SCVs and SIFs , a ~2 . 8 fold increase in bacterial replication was observed upon TGFBRAP1 depletion ( Fig 2b and S3f Fig showing knockdown efficiency >70% ) , which was not significantly different from control cells . These results suggest that HOPS , but not CORVET complex , regulates intracellular Salmonella replication . Since HOPS complex is one of the components of the late endocytic vesicle fusion machinery , we compared bacterial burden in HOPS-depleted cells with cells depleted of small GTPases and SNARE proteins also required for late endosome-lysosome fusion . To this end , we analyzed bacterial replication in cells treated with siRNA against Rab7 , Arl8b , and late endosomal/lysosomal SNAREs proteins-Vti1b , Syntaxin 8 , Syntaxin 17 , and Vamp7 . Western blotting and qRT-PCR analysis were done to confirm efficient depletion of these proteins in HeLa cells ( S3g–S3l Fig ) . Similar to HOPS depletion , only ~1 . 1–1 . 3 fold increase in bacterial replication from 2 hr to 10 hr was observed in Rab7 and Arl8b depleted cells ( Fig 2c ) . Amongst SNAREs , Syntaxin8 showed the most significant decrease in bacterial replication ( ~1 . 15 fold; Fig 2d ) followed by Vti1b and Syntaxin 17 ( ~1 . 75 and ~1 . 8 fold , respectively; Fig 2d ) whereas bacterial replication was modestly ( but significantly ) decreased in Vamp7-depleted cells ( ~2 . 3 fold; Fig 2d ) . In addition to HeLa cells , we also verified that HOPS subunit-Vps41 is required for Salmonella replication in primary mouse embryonic fibroblasts , MEFs ( S3p and S3r Fig; knockdown efficiency >70%; control siRNA: ~3 . 3 fold , Vps41 siRNA: ~2 . 5 fold increase in bacterial burden from 2 hr to 10 hr p . i . ) . It is well understood that macrophages are the major reservoir of Salmonella in host organisms [47] . Accordingly , to determine whether HOPS complex is required for Salmonella replication in macrophages , we performed CFU assays in control- , Vps39- and Vps41-siRNA treated macrophage-like cell line-RAW264 . 7 , a well-established cell line model for in vitro studies of Salmonella infection ( S3m and S3n Fig; knockdown efficiency >80% ) . As compared to control where ~4 fold increase in bacterial burden was observed , we found only a ~2 . 8 fold and ~1 . 3 fold increase in Salmonella burden upon Vps39 and Vps41 depletion , respectively , in macrophages reinforcing that HOPS complex is essential host factor for intracellular Salmonella replication in both epithelial and macrophage cells ( Fig 2e and 2f ) . A similar trend ( but overall less replication ) was observed in the control and Vps41 lentiviral-mediated shRNA transduced cells ( S3o and S3q Fig; knockdown efficiency >70%; control shRNA: ~2 fold and Vps41 shRNA: ~ 0 . 9 fold change in bacterial burden from 2 hr to 10 hr p . i . ) To corroborate the bacterial infection experiments performed under in vitro cell culture conditions , we next assessed whether HOPS subunits are required for in vivo replication of Salmonella in a mouse model . To determine this , we used morpholino-based approach to downregulate Vps41 expression in mice that were further infected with Salmonella by intravenous injection . As a control , standard negative control morpholino was injected in age-matched mice . At day 3 p . i . , CFU counts were analyzed from the liver and spleen homogenates of control- and Vps41-morpholino treated mice . The efficiency of Vps41 depletion in both liver and spleen was found to be >80% and >70% , respectively , while no change in the levels of Vps18 ( that directly binds to Vps41 ) was observed ( Fig 2g ) . Similar to our previous findings in cultured cells , striking decrease in in vivo replication of Salmonella was observed upon Vps41 depletion ( Fig 2h ) . Consistent with this , DnaK signal was also strikingly reduced in tissue homogenates from Vps41 morpholino-injected mice ( Fig 2g , third panel ) . Overall , our findings reveal HOPS complex as an essential host factor required for Salmonella proliferation in multiple cell types and in a murine infection model . To establish its intracellular replicative compartment , Salmonella dynamically interacts with , and acquires both membrane and luminal content from host late endosomes/lysosomes [2] . Since HOPS complex is a crucial factor required for tethering and fusion of incoming cargo with lysosomes , we hypothesized that HOPS function is required for SCV fusion with late endosomes and lysosomes . To this end , we first analyzed SCV maturation upon HOPS depletion by quantifying recruitment of early SCV marker ( EEA1 ) and late SCV marker ( LAMP1 ) at different time points in control- , Vps41- and Vps39-siRNA treated cells . At 10 min p . i . , no significant differences in the percentage of EEA1-positive SCVs were evident in HOPS-depleted cells as compared to the control cells ( S4a–S4c Fig and quantification shown in Fig 3g and 3h; control siRNA: ~78–84% , Vps41 siRNA:~75% , and Vps39 siRNA: ~71% ) . LAMP1 acquisition was not observed in either control or HOPS depleted cells at this early time point of infection ( S4a–S4c Fig; see intensity profile ) . At 1 hr p . i . , ~70% of SCVs in control siRNA treated cells were now EEA1-negative and had acquired LAMP1 ( Fig 3a , see intensity profile; quantification shown in Fig 3g and 3h ) . In contrast , upon HOPS depletion , we found that EEA1 was still retained around ~40% of SCVs while LAMP1 acquisition was observed only around ~14–25% SCVs at 1 hr p . i . ( Fig 3b and 3c; quantification shown in Fig 3g and 3h ) . These findings suggest a delay in SCV maturation upon depletion of HOPS subunits . Interestingly , by 6 hr p . i . , ~62–70% of SCVs had acquired LAMP1 staining in HOPS siRNA treated cells and none were found to be positive for the early endosomal marker , EEA1 ( Fig 3d–3f; quantification shown in Fig 3g and 3h ) . As LAMP1 is distributed on both late endosomes and lysosomes , we also analyzed localization of a specific late endosomal markers-Rab7 and -LBPA on SCVs in control and HOPS depleted cells at 1–6 hr p . i . As previously reported [48] , we did not observe acquisition of the late endosomal lipid-lysobisphosphotidic acid ( LBPA ) to SCV membranes either in control or HOPS depleted cells at 1 hr and 6 hr p . i . ( S5 Fig ) . Interestingly , Rab7 acquisition was unchanged upon HOPS depletion wherein >80–90% SCVs were positive for Rab7 at 1 hr , 3 hr , and 6 hr p . i in both control and HOPS depleted cells ( Fig 4a–4f; quantification shown in Fig 4g ) . Notably , as compared to the control siRNA treated cells , we did observe a modest decrease in Rab7 intensity around the SCVs in HOPS depleted cells at 1 hr p . i . that was recovered by 3 hr p . i . ( Fig 4h and 4i ) . Our findings suggest that especially at 1 hr p . i . , several SCVs in HOPS-depleted cells retain characteristics of both early endosomes and late endosomes . ( see quantification shown in Figs 3g , 3h and 4g ) . Taken together , these results signify a delay but not a complete block in SCV maturation upon depletion of HOPS subunits . Our findings indicate that SCV maturation follows a scheme similar to maturation of early endosomes to multi-vesicular bodies/late endosomes upstream of HOPS-mediated fusion of late endosomes and lysosomes [49 , 50] . In agreement with this , previous studies have shown that endocytic machinery required for early to late endosome maturation such as Vps34 and Rab7 is also required for SCV maturation [24 , 51 , 52] . To confirm that LAMP1 acquisition by SCVs is not inhibited upon fusion with lysosomes , we treated cells with Bafilomycin A1 ( Baf A1 ) , a routinely used chemical inhibitor of vesicle fusion with lysosomes [53] . Baf A1 inhibits fusion of lysosomes with other compartments by inactivating the ER Ca2+-ATPase ( SERCA ) whose activity is required to maintain the lysosomal Ca2+ stores [54 , 55] . As shown in S4d and S4e Fig , LAMP1 acquisition around SCVs was not impaired in cells pretreated with Baf A1 ( see intensity profile graphs in S4f and S4g Fig ) although SIF formation was abrogated in the presence of this drug . These findings support our conclusion that LAMP1 acquisition by SCV does not require heterotypic fusion with lysosomes , which in turn is mediated by HOPS complex . Previous studies have shown that Salmonella colonizes and hyper-replicates within the cytosol of epithelial cells [56 , 57] . To address whether the cytosolic hyper-replicating Salmonella population is increased upon HOPS depletion , we determined bacterial burdens in control and Vps41 depleted cells using the previously described modified gentamicin protection assay where cells are treated with chloroquine ( CHQ ) before the end of infection time point . CHQ is a lysosomotrophic agent that accumulates within endosomes/lysosomes and has been shown to degrade vacuolar but not cytosolic bacteria [56 , 57] . We observed a modest but not a statistically significant increase in the number of cytosolic bacteria at 7 hr p . i ( peak time point of cytosolic replication [57] ) in Vps41 siRNA treated cells ( S4h Fig: control siRNA: 28±3% and Vps41 siRNA: ~36±4% ) , suggesting that majority of bacteria ( ~70% ) continue to harbor their vacuolar niche upon HOPS depletion . In concordance with these studies , immunogold-EM of ultrathin sections of Salmonella-infected Vps41 depleted cells at 10 hr p . i . showed presence of several vacuolar bacteria surrounded by limiting membrane positive for late endosomal and lysosomal markers-Rab7 and LAMP1 ( Fig 4j–4m ) . Salmonella survival and replication inside its vacuole strictly correlates with its ability to form SIFs , which begins at 5–6 hr p . i , . and is best visualized at 8–10 hr p . i . by immunostaining for lysosomal glycoproteins in Salmonella-infected cells [10] . Notably , as compared to the control cells , we did not observe SIF formation at later time points of infection ( 6 hr and 10 hr p . i . ) in cells depleted of either of the six HOPS subunits ( S6a–S6i Fig ) . In contrast , SIF formation was observed in TGFBRAP1-depleted cells ( S6j Fig ) , however SIFs were “beaded” and thinner in these cells , which might explain the modest defect in Salmonella replication as shown in Fig 2b . To establish whether formation or stability of SIFs was reduced upon HOPS depletion , we performed live-cell imaging to visualize GFP-LAMP1 ( marker for SIFs ) dynamics in control- , Vps39- and Vps41-siRNA treated cells that were infected with DsRed-expressing Salmonella . At 9 hr p . i . , time-lapse imaging revealed extensive SIF formation in control cells that was completely absent in Vps41- and Vps39-depleted cells ( S4–S6 Movies ) . Moreover , as compared to the control cells , significantly fewer LAMP1-positive vesicles were found to interact with SCVs in Vps41- and Vps39-depleted cells ( S5 and S6 Movies ) . Previous studies have shown that SCV association with the late endocytic compartments is significantly increased by 6–8 hr p . i . , time points that correlate with the onset of SIF formation [10] . However , whether SIF formation is dependent upon SCV fusion with late endosomes/lysosomes and the host machinery that regulates this fusion is not known . Our results demonstrating that HOPS complex localizes to SCV and SIFs , suggest that similar to its role in mediating late endosome-lysosome fusion , this tethering factor could facilitate SCV fusion with lysosomes . To test this , prior to infection we pre-loaded control siRNA- and Vps41 siRNA-treated HeLa cells or control and Vps41 shRNA stably transduced RAW264 . 7 macrophages with Alexa 647-conjugated dextran ( dextran-647 ) that specifically labels lysosomes , as shown schematically in Fig 5a . Live-cell imaging performed at 10 hr p . i . in control HeLa and RAW264 . 7 cells showed several dextran-positive endosomes undergoing fusion with the SCVs , resulting in acquisition of dextran by the SCVs ( Fig 5b and 5d; S7 and S9 Movies ) . SIF formation was also observed in both control siRNA/shRNA-treated cells ( S7 and S9 Movies ) . In contrast , little or no interaction of SCVs with the dextran compartment was observed in Vps41 depleted HeLa and RAW264 . 7 cells ( Fig 5c and 5e; S8 and S10 Movies ) . Quantification of SCVs positive for dextran-647 and its signal intensity , revealed significantly lower dextran acquisition in Vps41 depleted cells compared to control ( Fig 5f–5i; percentage of dextran-positive SCVs in HeLa and RAW264 . 7 cells-control: ~65–70% , Vps41 depletion: 10–15% ) . These results suggest that HOPS complex is required for acquisition of fluid-phase content by the SCVs from late endosomes and lysosomes . In agreement with these findings , imaging of ultrathin sections of Salmonella-infected control cells by TEM demonstrated several late endosomes ( containing numerous MVBs ) and lysosomes ( containing lamellar membrane sheets ) docked at or in close apposition to the SCVs ( Fig 6a and 6b; S7b and S7c Fig; see magnified insets ) . In contrast , late endosomes/lysosomes docking at the SCVs were highly reduced in Vps41 depleted cells ( Fig 6c and 6d; S7a , S7d and S7e Fig; see magnified insets ) . Further , as previously noted in another study [23] , we also observed several abnormal “bag-like” SCVs upon Vps41 depletion ( Fig 6d; see magnified inset ) . Additionally , in few TEM sections , SIF formation was also observed in control but not Vps41 depleted cells ( S7b Fig , middle panel ) . Analysis of several TEM images in control cells revealed that of the ~100 SCVs imaged , ~40 SCVs had closely apposed late endosomes , whereas only ~10 of the 100 SCVs in Vps41 siRNA treated cells and none of the ~60 SCVs imaged in Vps41 shRNA transduced cells showed docked late endosomes . As previously reported [58] , we also noted that lysosomes ( containing lamellar membrane sheets ) were reduced in Vps41 siRNA treated cells while several large MVB-containing compartments were observed ( Fig 6c and 6d; indicated by white arrowheads ) . Although docking of late endocytic compartments at the SCVs was reduced upon Vps41 depletion , this did not indicate a general defect in the formation of late endocytic compartments . This was confirmed by LysoTracker Red uptake in control and Vps41 depleted cells , which is a selective probe that labels acidic organelles and routinely used as a specific marker to label late endosomes and endolysosomes . Immunofluorescence analysis and quantification of LysoTracker Red signal intensity by flow cytometry revealed no significant difference in control and Vps41 depleted cells ( S8a–S8e Fig ) . The specificity of this probe was confirmed by treating cells with Baf A1 that neutralizes the pH of late endocytic compartments , and hence the signal intensity was reduced to background fluorescence levels ( S8e Fig ) . We also confirmed that functional endo-lysosomes are formed upon Vps41 depletion by comparing levels of mature cathepsin B and D in control and Vps41 siRNA treated cells ( S8f Fig ) . Taken together , our findings suggest that HOPS complex is a crucial host factor required for SCV fusion with the late endocytic compartments that provide membranes for formation of a replicative vacuolar niche for this pathogen . Recent studies have shown that content mixing of SCV with the late endocytic compartments and SIF formation not only provides membrane for vacuolar integrity for the growing bacterial population but also provides nutrient access to the vacuolar bacteria for replication [14 , 15 , 17] . This was in part established by use of auxotrophic strains of Salmonella that were deficient in biosynthesis of particular amino acids . The mutant strains were able to replicate by obtaining nutrients from the growth medium of the host cells , only if they were proficient in SIF formation [15] . Based on our findings that HOPS complex mediates SIF formation by promoting SCV interaction with the host late endocytic compartments , we investigated role of HOPS subunits in mediating nutrient access from host cell to SCVs . To this end , we infected control- and Vps41-siRNA treated cells with proline auxotrophic strain of Salmonella ( proC ) . This strain lacks the last enzyme required for proline biosynthesis , and is defective in intracellular replication unless proline is provided in the mammalian cell growth media [15] . As previously noted [15] , we also found that proC strain was replication-defective as compared to the wild-type ( WT ) Salmonella strain . This growth defect was completely augmented by addition of proline in the culture media of control siRNA-treated HeLa cells ( Fig 6e ) . In contrast , upon depletion of HOPS subunit Vps41 , only a modest increase in the replication of proC strain in presence of extracellular proline was observed , which was significantly less than the control cells under the same experimental conditions ( Fig 6e ) . These results suggest that HOPS complex provides nutrient access from the host late endosomes and lysosomes to the bacteria within the confinements of the vacuole , enabling intravacuolar replication of Salmonella . Previous studies have revealed that Salmonella mutant strains deficient in SPI2-T3SS effectors sifA , pipB2 , sseF and sseG show the most striking changes in SIF formation [12 , 16] . The most severe phenotype was observed in Salmonella strain lacking sifA where SIF formation was completely abrogated and vacuolar integrity was disrupted , leading to bacterial release in the host cytosol [18] . Our findings thus far indicate that HOPS complex is a crucial host factor required for SCV and SIF fusion with the late endocytic compartments , providing a continuous supply of membranes for SIF formation . To determine whether Salmonella effectors involved in SIF formation promote HOPS recruitment to SCV membranes , we visualized and quantified the recruitment of HOPS subunits Vps41 ( both epitope-tagged and endogenous ) and Vps18 ( endogenous ) to LAMP1-positive SCVs in mutant strains deficient in either sifA ssej , pipB2 , sseF or sseG effectors ( Fig 7 and S9 Fig ) . We used the sifA sseJ double-mutant strain in these experiments instead of the sifA single mutant strain as the latter loses its vacuolar integrity over time and becomes cytosolic [17] . Surprisingly , as compared to the WT strain of Salmonella , we did not observe recruitment of HOPS subunits-Vps41 and -Vps18 to sifA ssej SCVs , although association of these SCVs with the vacuolar membrane marker-LAMP1 was observed ( Fig 7a and 7b; quantification shown in Fig 7g; S9a , S9b , S9g and S9h Fig ) . Notably , Vps41 and Vps18 continued to localize at the SCVs in cells infected with Salmonella mutant strains pipB2 , sseF and sseG ( Fig 7c–7f; quantification shown in Fig 7h; S9c–S9f and S9i–S9l Fig ) . These results suggest that SifA , but not other Salmonella effectors , involved in SIF formation are crucial for recruitment of HOPS subunits . Intriguingly , we had previously found that SifA interaction partner-SKIP colocalizes and interacts with Vps39 subunit of the HOPS complex [27] . Based on these observations , we hypothesized that SifA in complex with SKIP targets HOPS complex to SCV membranes . Indeed , while little or no colocalization of Vps39 with SifA was observed , Vps39 colocalized with SKIP on peripheral structures shown to be lysosomes , which are transported in an anterograde manner by direct binding between Arl8b-SKIP complex to the plus-end microtubule binding motor-kinesin-1 ( Fig 8a and 8b ) [20 , 59 , 60] . Notably , colocalization between SifA and Vps39 was strikingly enhanced upon co-expression with SKIP and the three proteins were localized on the peripheral pool of lysosomes ( compare Fig 8a and 8d ) . The other subunits-Vps18 and Vps41 , also showed a significantly higher colocalization with SifA in presence of SKIP ( compare S10a and S10c Fig; compare S10b and S10d Fig ) . Quantification of Pearson’s Correlation Coefficient ( PCC ) from 25–30 transfected cells over three independent experiments demonstrated a significant increase in colocalization of HOPS subunits with SifA in presence of SKIP ( Fig 8g and 8h ) . Recently PLEKHM1 , a protein with domain architecture similar to SKIP , was reported to interact with both SifA and HOPS subunits Vps39 and Vps41 [23 , 29] . While it was speculated that PLEKHM1 acts as a linker between SifA and HOPS complex , no experimental evidence was shown to prove the same . Indeed , we found a strong colocalization of Vps39 with PLEKHM1 , which was significantly higher than its colocalization with SKIP ( Fig 8b and 8c; quantification of PCC shown in Fig 8f ) . To determine whether PLEKHM1 , similar to SKIP , promotes colocalization of HOPS subunits with SifA , we co-expressed Vps39 and SifA with PLEKHM1 . Surprisingly , while Vps39 and PLEKHM1 continued to colocalize on punctate structures , SifA was not recruited to these punctae ( Fig 8e; quantification of PCC shown in Fig 8g ) . These results indicate that PLEKHM1 does not promote HOPS subunit association with SifA . We also noted that colocalization and interaction of SifA with PLEKHM1 was significantly weaker than with SKIP , as revealed by colocalization coefficient quantification and growth curve analysis of yeast two-hybrid assay using SifA as a bait , and SKIP and PLEKHM1 as prey proteins ( S10e–S10h Fig ) . These findings were corroborated by GST pulldown assay where pull down of PLEKHM1 with GST tagged-SifA was found to be much lower as compared to SKIP from transfected cell lysates ( S10i and S10j Fig ) . Additionally , qRT-PCR analysis revealed that SKIP mRNA levels in HeLa cells were ~2 . 5 fold higher than PLEKHM1 levels ( S10k Fig ) . Taken together , these results imply that at least in this cell line , more amount of the secreted bacterial effector SifA must be bound to SKIP as compared to PLEKHM1 . To conclusively determine whether SKIP is a linker between SifA and HOPS subunit-Vps39 , we employed yeast three-hybrid assay to test interaction of SifA and Vps39 in the presence of either SKIP or PLEKHM1 as well as a SifA binding-defective mutant of SKIP ( SKIP G828D ) . In this assay , linker protein is under the control of the Met25 promoter that remains repressed in the presence of methionine in the growth media . As depicted in Fig 9a , under methionine-deficient conditions , SifA showed interaction with Vps39 only in the presence of SKIP , but not SKIP G828D mutant or PLEKHM1 . To corroborate these results , we also performed GST pulldown using GST tagged-SifA as bait to pulldown Vps39 in cells with endogenous or overexpressed levels of SKIP . We observed a dramatic increase in the levels of Vps39 pulldown with SifA upon SKIP overexpression ( Fig 9b and 9c ) . This striking increase in pulldown of HOPS subunits was also reflected upon probing for endogenous Vps11 , which directly binds to Vps39 during assembly of the HOPS complex ( Fig 9b ) . Vps41 pulldown with SifA was also increased upon SKIP overexpression , although this was less striking as compared to Vps39 and Vps11 ( Fig 9b ) . To establish that endogenous levels of SKIP are sufficient to drive this interaction , we performed co-IP of SifA and Vps39 in control and SKIP depleted cells ( Fig 9d; >90% gene silencing efficiency observed ) . As shown in Fig 9e and 9f , co-IP of Vps39 with SifA was significantly reduced upon SKIP depletion , and was restored upon expression of the siRNA-resistant SKIP construct , suggesting that SKIP acts as a linker to facilitate interaction between SifA and HOPS complex . In line with these observations and in accordance with previous studies [20 , 61] , we found a significant defect in bacterial replication in SKIP-depleted cells as compared to control ( S10l Fig; control siRNA: ~3 fold and SKIP siRNA: ~1 . 3 fold increase in bacterial burden from 2 hr to 10 hr p . i . ) . Notably , we did not observe any increase in pulldown of HOPS subunit Vps39 with GST tagged-SifA upon PLEKHM1 overexpression ( Fig 9b and 9c ) . Similarly , no effect on the levels of co-IP Myc-tagged SifA with HA-Vps39 was observed upon PLEKHM1 depletion ( Fig 9g; >90% silencing efficiency observed; Fig 9h ) , suggesting that PLEKHM1 does not facilitate interaction between SifA and HOPS complex . To then determine whether SKIP is required for recruitment of HOPS subunits to SCV membranes , we visualized Vps41 localization to SCVs in control and SKIP siRNA treated cells . As shown in Fig 10a and 10b , while Vps41 was present around the SCVs in control siRNA treated cells , little or no association was observed in SKIP depleted cells at 10 hr p . i . Quantification of Vps41-positive SCVs in control and SKIP siRNA treated cells demonstrated that Vps41 recruitment to SCV membranes was abrogated upon SKIP depletion ( Fig 10g ) . These findings were corroborated by live-cell imaging experiments of GFP-tagged Vps41 either expressed alone or co-expressed with Arl8b in control and SKIP siRNA treated cells ( S11–S14 Movies; S11a and S11b Fig ) . Recruitment of Vps41 to SCVs was rescued by expression of siRNA-resistant SKIP , confirming specificity of SKIP siRNA treatment ( Fig 10e–10g ) . In contrast , Vps41 continued to associate with SCV membranes in PLEKHM1 depleted cells at 10 hr p . i . ( S11c and S11d Fig; Fig 10g ) , which supports our previous results that PLEKHM1 does not regulate SifA interaction with HOPS complex . To corroborate these findings , we used an independent method to disrupt interaction of SifA and SKIP i . e . infection with Salmonella strain expressing a point mutant of SifA ( L130D ) , which is defective in binding to SKIP and formation of SIFs [21 , 61] . Using co-IP approaches , we confirmed that SKIP does not interact with the previously reported SKIP-binding interface mutants of SifA ( S11e Fig ) [21] . Also , unlike SifA deletion ( sifA ) , bacteria expressing SifA ( L130D ) do not escape to the cytosol and continue to be surrounded by LAMP1-positive vacuolar membrane [61] , which allowed us to analyze whether HOPS complex was recruited to the SCVs surrounded by an intact vacuolar membrane . Notably , as compared to the cells infected with the sifA strain expressing SifA ( WT ) -2xHA plasmid , in cells infected with sifA strain expressing point mutant SifA ( L130D ) -2xHA , little or no association of HOPS subunit-Vps41 with SCVs was observed at 10 hr p . i . ( Fig 10c and 10d ) . Quantification of Vps41-positive SCVs infected with either strain demonstrated that recruitment of HOPS subunit Vps41 to SCV membranes was abrogated in the presence of SKIP-binding defective mutant of SifA ( Fig 10h ) . These findings were corroborated by live-cell imaging experiments of GFP-tagged Vps41 either expressed alone or co-expressed with Arl8b in cells infected with either Salmonella strain ( S15–S18 Movies; S11f and S11g Fig ) . A previous study has shown that SifA protein expression in host cells results in the extensive clustering/aggregation of specifically the late endocytic compartments marked by LAMP1 and V-ATPase immunostaining [62] . Taking our results presented here into consideration , SifA could promote SCV fusion with late endosomes/lysosomes by virtue of its interaction with the host factors , SKIP and HOPS complex . Indeed , endogenous HOPS subunits-Vps18 and -Vps41 , were enriched on the vertices of these clustered LAMP1-positive compartments induced by ectopic expression of SifA ( Fig 11a and 11b ) . To test whether SifA-mediate clustering and aggregation of late endosomes and lysosomes requires presence of SKIP and HOPS subunits , we transfected SifA in control- , Vps39- and SKIP-siRNA treated cells and analyzed particle size of LAMP1-positive compartment . Our results show that SifA-mediated increase in lysosomal particle size depends upon the expression of SKIP and HOPS subunit-Vps39 ( Fig 11c–11f ) . Taken together , our findings indicate that Salmonella virulence factor SifA in complex with the host protein , SKIP , recruits the vesicle fusion machinery of the host including the tethering factor HOPS complex to SCV membranes , thereby , enabling SCV fusion with late endosomes and lysosomes .
Salmonella typhimurium is a successful intracellular pathogen that has developed an array of sophisticated strategies to massively remodel the host endosomal system for its own survival and propagation . Previous studies have shown that SCV biogenesis involves extensive interactions with the host endocytic pathway including late endosomes/lysosomes [2] . However , little is known about how Salmonella mediates these interactions and whether it co-opts the late endosomal-lysosomal vesicle fusion machinery of the host cell for building its replicative niche . Conflicting reports have shown that while Salmonella inhibits activation of the small GTPase Rab7 [24 , 63] , it actively recruits Arl8b on SCV and SIFs [25] wherein both Rab7 and Arl8b are components of protein machinery required for late endosome-lysosome fusion [26] . Intriguingly , Arl8b-positive lysosomes are less acidic and have reduced proteolytic activity than Rab7-positive endosomes [64] . It is interesting to speculate that Arl8b- but not Rab7-positive lysosomes act as source of membrane for SCV biogenesis and SIF formation during later time points of infection . This would ensure membrane and cargo delivery to SCVs without increasing the proteolytic activity within Salmonella’s replicative niche . In this study , we have investigated the role of HOPS complex , a multisubunit tethering factor required for vesicle fusion with lysosomes , in regulating Salmonella survival and replication inside its vacuole . Our results reveal that HOPS complex is a target for Salmonella effector SifA , which in collaboration with its known binding partner SKIP and the host GTPase , Arl8b , recruits HOPS complex to SCV membranes , thereby enabling SCV fusion with lysosomes ( Fig 11g ) . As late endocytic compartments are a source for both membrane and fluid-phase cargo , including nutrients for Salmonella residing in the vacuole [2 , 14] , silencing of HOPS subunits inhibited Salmonella replication under both in vitro and in vivo conditions . Unlike the defense strategies used by intracellular pathogens such as M . tuberculosis and C . burnetti [65 , 66] , Salmonella does not block the maturation of its phagosome , which rapidly ( ~30–60 min p . i . ) acquires several ( but not all ) characteristics of the late endocytic compartments but does not become bactericidal [2] . The acidic pH of the SCV ( ~<5 ) is required for the induction of the SPI-2 effectors , which in turn facilitate Salmonella replication inside the host cell [67] . At 1–2 hr p . i . , we found weak but consistent localization of HOPS subunits on mature SCVs , which correlated with the recruitment of the lysosomal marker , LAMP1 . While HOPS complex localized to the mature SCVs , we did not find an essential role of HOPS subunits , Vps41 and Vps39 , in SCV maturation as indicated by a modest delay but not a block in LAMP1 acquisition in HOPS depleted cells . Our results support previous studies suggesting that SCV maturation is akin to an early to late endosome maturation event , regulated by proteins including PI ( 3 ) kinase and Rab7 ( acquired upon HOPS depletion , Fig 4 ) that act upstream of the HOPS complex in endo-lysosome fusion [24 , 51] . Previous live-cell imaging studies of Salmonella-infected HeLa cells and RAW264 . 7 macrophages have shown that at 6–8 hr p . i . , 90% of SCVs interact with dextran-loaded terminal lysosomes , and acquire not only membrane but also fluid-phase cargo from these compartments [9 , 12] . Besides delivering membranes for SCV biogenesis , fusion with late endosomes/lysosomes provides access to nutrients for bacterial replication [14 , 15] . Intravacuolar Salmonella can access nutrients from the host endolysosomal compartments by direct fusion of SCV with these membranes or from cytosol by recruitment of nutrient transporters on SCV and SIF membranes . In both of these scenarios , extensive membrane network will be required , which is delivered by host vesicle fusion machinery including HOPS complex . Accordingly , the ability of proline auxotrophic Salmonella strain to acquire proline from the extracellular media was also abrogated in HOPS-depleted cells . Besides their role as a tethering factor , HOPS subunits bind to SNARE proteins , which mediate membrane fusion [32 , 35] . We found a comparable defect in Salmonella intracellular replication when we depleted other components of the vesicle fusion machinery including small GTPases-Rab7 and -Arl8b , as well as SNARE proteins: Vti1b , Syntaxin 8 , Syntaxin 17 and VAMP7 that are known to regulate late endosome-lysosome fusion [68] . These results indicate that Salmonella co-opts the host vesicle fusion machinery for survival and replication within its intravacuolar niche . One of the hallmarks of Salmonella intracellular lifestyle is presence of striking tubular membranes or SIFs that emanate from the juxtanuclear SCVs [11] . The ability to form SIFs was found to directly correlate with Salmonella’s ability to replicate both under in vitro and in vivo conditions , as supported by the replication defect observed in Salmonella strains defective in SIF formation [19] . Recent studies have now shown that SIF formation allows Salmonella to convert the host cell endosomal system into a continuum with the SCV , not only providing SCVs access to the endocytosed material but the extensive SIF network is proposed to rapidly dilute the antimicrobial activities transferred to the vacuole upon its fusion with the host late endosomes and lysosomes . As a result , SCVs competent to form SIFs have bacteria with significantly higher metabolic activity than one that cannot form SIFs [14] . Using live-cell imaging we found that depletion of HOPS subunits completely inhibited SIF formation by Salmonella , supporting the strong replication defect observed in these cells . SifA is the most well characterized Salmonella effector named for its essential role in mediating SIF formation [11] . Accordingly , Salmonella strains lacking SifA show a strong replication defect , as they fail to induce SIF formation and escape into the cytosol [19] . SifA has been shown to interact with two host proteins namely SKIP/PLEKHM2 and PLEKHM1 via pleckstrin homology ( PH ) domains of these proteins [20 , 23] . We found that SKIP , but not PLEKHM1 , acts as a linker to mediate interaction of HOPS complex with SifA by simultaneously binding to HOPS subunit-Vps39 . These results were surprising given the fact that previously PLEKHM1 was implicated in recruitment of HOPS complex to mediate SCV fusion with detoxified lysosomes [23] . However , the role of PLEKHM1 as a linker was never directly tested in this study and it was speculated based on the fact that PLEKHM1 binds to both HOPS complex and SifA [23 , 29] . A direct comparison of PLEKHM1 and SKIP’s linker role and their relative binding affinities for SifA as well as comparison of expression levels of both proteins in HeLa cells led us to conclude that SifA-SKIP promotes recruitment of HOPS subunits to SCV compartment . It will be interesting to determine whether SifA and Vps39 have overlapping binding sites on PLEKHM1 , preventing SifA recruitment to PLEKHM1 and Vps39-positive compartment . Our study also suggests a novel role for SKIP in promoting Salmonella intracellular replication , besides its known function in preventing kinesin-1 accumulation on SCVs and regulating vacuolar integrity [20 , 22 , 59] . HOPS complex localization to SCVs and SIFs also required small GTPase Arl8b , which is highly enriched on these compartments and regulates lysosomal localization of both of its effectors-SKIP and Vps41 subunit of the HOPS complex [25 , 27 , 60] . Recently , we have uncovered that PLEKHM1 , like SKIP , binds to Arl8b via its RUN domain and is a shared effector of Rab7 and Arl8b , which simultaneously binds to both GTPases to promote cargo trafficking to lysosomes [26] . Since Salmonella has devised a strategy to inhibit Rab7 activation , on the other hand Arl8b is enriched on SCVs and SIFs , it will be relevant to determine whether PLEKHM1 role in SCV fusion with lysosomes is dependent upon its interaction with Arl8b . Unlike Salmonella typhimurium , much less is known about the intracellular lifestyle of the human-restricted pathogen-Salmonella typhi , the typhoid-causing strain of the same serovar . Intracellular S . typhi secretes the typhoid toxin inside its SCV , which is then packaged into vesicular carriers that are then transported into the extracellular space to mediate its effect in an autocrine and paracrine manner on the host cells [69 , 70] . Interaction of S . typhi vacuole with the host endocytic machinery and mechanisms regulating formation and transport of the typhoid toxin-containing vesicular carriers are only beginning to be understood [71 , 72] . Indeed , like S . typhimurium , intracellular replication of S . typhi was impaired in Rab7-depleted cells , suggesting that S . typhi might also manipulate host late endosomes and lysosomes to regulate biogenesis of its SCV and growth inside the host cells [73] . Future studies are required to address whether the host endocytic machinery regulates S . typhi replication and biogenesis of the typhoid toxin vesicular carriers that will reveal novel targets for development of antimicrobial molecules .
HeLa , HEK293T , and RAW264 . 7 cells were obtained from the American Type Culture Collection and maintained in DMEM ( Lonza ) supplemented with 10% heat-inactivated Fetal Bovine Serum ( FBS; Life technologies ) at 37°C in 5% CO2 humidified incubator . All the cultures were used between passage numbers 5–15 . An Arl8b-KO HeLa cell line was previously described [26] . Arl8b-knockout ( KO ) HeLa cells were generated using the Arl8b sg/RNA ( Target sequence: 5′-GATGGAGCTGACGCTCG-3′ ) CRISPR/Cas9 All-in-One Lentivector Set ( Applied Biological Materials ) . For stably silencing the expression of Vps41 in RAW264 . 7 cells , lentivirus mediated shRNA gene silencing approach was used . Briefly , for lentiviral transduction , RAW264 . 7 cells were seeded in a 35-mm tissue culture dish ( Corning ) in Polybrene ( 8 μg/ml; Sigma-Aldrich ) and mixed with 500 μl of viral supernatant ( day 0 ) . Puromycin ( Sigma-Aldrich ) was added after 24–48 hr at 5 μg/ml for a minimum of 3 days to select transductants , and experiments were performed on days 5–15 after transduction . shRNA target sequences were as follows: Mission ( negative control sequence ) , CAACAAGATGAAGAGCACCAA and mouse Vps41 , GAGTGGCCTGGAGATCTATAT . Development of HeLa-Vps41 shRNA cell line was previously described using Vps41 shRNA , 5′-CCATTGACAAACCACCATTTA-3′ [27] . Primary mouse embryonic fibroblast ( MEF ) cells were isolated from the embryos of BALB/c mouse . Briefly , embryos were harvested from female mice 15 days after the appearance of the copulation plug . Embryos were placed in 1 ml of 0 . 05% trypsin/EDTA solution ( Life technologies ) and finely minced using a sterile razor blade and repeated pipetting was performed to dissociate cells . The trypsin was inactivated by adding DMEM supplemented with 10% FBS and the culture was centrifuged to pellet MEF cells . The pelleted MEF cells were resuspended in culture media , and plated at optimal density in tissue culture dishes at 37°C in 5% CO2 humidified incubator . The following antibodies were used in this study: mouse anti-FLAG M2 clone ( F1804; Sigma-Aldrich ) , mouse anti-HA ( MMS-101P; Covance ) , rabbit anti-HA ( sc-805; Santa Cruz Biotechnology ) , rat anti-HA clone 3F10 ( 11867423001; Roche ) , mouse anti-Myc 9E10 clone ( sc-40; Santa Cruz Biotechnology ) , mouse anti-α-tubulin ( T9026; Sigma-Aldrich ) , mouse anti-GAPDH ( sc-166574; Santa Cruz Biotechnology ) , mouse anti-EEA1 ( 610457; BD Biosciences ) , rabbit anti-EEA1 ( ab2900; Abcam ) , mouse anti-LAMP1 ( 555798; BD Biosciences ) , rabbit anti-LAMP1 ( ab24170; Abcam ) , rabbit anti-PLEKHM1 ( ab171383; Abcam ) , rabbit anti-SKIP/PLEKHM2 ( HPA032304; Sigma-Aldrich ) , mouse anti-TGFBRAP1 ( sc-13134; Santa Cruz Biotechnology ) , mouse anti-LBPA ( Z-PLBPA; Echelon Biosciences ) , rabbit anti-Catalase ( 12980; Cell Signaling Technology ) , rabbit anti-Rab5 ( 3547; Cell Signaling Technology ) , rabbit anti-Rab7 ( 9367; Cell Signaling Technology ) , rabbit anti-Cathepsin D ( K50161R; Meridian Life Sciences ) , mouse anti-Cathepsin B clone 4B11 ( 414800; Thermo Fisher Scientific ) , rabbit anti-Salmonella O-antigen ( 225341; BD Biosciences ) , and mouse anti-DnaK ( ADI-SPA-880-F; Enzo Life Sciences ) . Rabbit anti-PLEKHM1 antibody generated against the N-terminal 497 amino acids of human PLEKHM1 protein was a gift from Prof . Paul Odgren ( University of Massachusetts Medical School , Worcester , MA ) and has been previously used to detect PLEKHM1 by immunofluorescence and Western blotting [26 , 74] . Rabbit anti-Arl8 antibody used in this study has been described previously [28] . For detection of HOPS subunits , the following antibodies were used: rabbit anti-Vps11 ( ab125083; Abcam ) , rabbit anti-Vps18 ( ab178416; Abcam ) , rabbit anti-Vps33a ( 16896-1-AP; ProteinTech ) , rabbit anti-Vps41 ( ab181078; Abcam ) , and mouse anti-Vps41 ( sc-377271; Santa Cruz Biotechnology ) . All the Alexa fluorophore-conjugated secondary antibodies were purchased from Molecular Probes ( Thermo Fisher Scientific ) . HRP-conjugated goat anti-mouse and goat anti-rabbit were purchased from Jackson ImmunoResearch Laboratories . Alexa Fluor 647-conjugated Dextran , LysoTracker Red DND-99 and DAPI were purchased from Molecular Probes ( Thermo Fisher Scientific ) . L-Proline , Cytochalasin D , Bafilomycin A1 , Polybrene , Streptomycin , Gentamicin and Puromycin were purchased from Sigma-Aldrich . Yeast drop-out media were purchased from Clontech . All the Salmonella typhimurium strains and plasmids used in this study are described in Table 1 . For infection of HeLa and MEF cells , late-log S . typhimurium cultures were used and prepared using a method optimized for bacterial invasion [5] . Briefly , wild-type and mutant bacteria were grown for 16 hr at 37°C with shaking and then subcultured ( 1:33 ) in LB ( Difco ) without antibiotics and grown until late exponential phase ( O . D . = 3 . 0 ) . Bacterial inocula were prepared by pelleting at 10 , 000 x g for 2 min , diluted 1:100 in Phosphate buffer saline ( PBS ) ( pH 7 . 2 ) , and added to cells ( at the specified MOI ) for 10 min at 37°C to allow invasion and synchronized infection . After infection , extracellular bacteria were removed by extensive washing using warm PBS and 50 μg/ml gentamicin was added to the medium at 30 min p . i . for incubation at 37°C . After 2 hr p . i . , the concentration of gentamicin in the medium was decreased to 5 μg/ml . Following this infection protocol , cells were processed for microscopy and biochemical experiments as described in the individual figure legends . For infections of RAW264 . 7 cells , stationary-phase bacterial cultures incubated at 37°C with shaking were diluted ( O . D . = 1 ) and opsonized in PBS supplemented with 20% FBS for 20 min at 37°C . After three washes in PBS , bacteria were resuspended in growth medium without antibiotics , and added to the cells ( MOI of 50:1 ) for 20 min to facilitate phagocytosis . The remaining protocol was similar as in case of infection of HeLa cells . Cells grown on glass coverslips ( VWR ) were transfected with desired constructs using X-tremeGENE-HP DNA transfection reagent ( Roche ) for 16–18 hr . For gene silencing , siRNA duplexes for non-targeting siRNA pool , control siRNA ( 5′-TGGTTTACATGTCGACTAA-3′ ) , human Arl8b ( 5′-AGGTAACGTCACAATAAAGAT-3′ ) , human Rab7a ( 5′-CTAGATAGCTGGAGAGATG-3′ ) human Vps11 ( 5′-GAGGCTGAGCTGAGCCTCGTATT-3′ ) , human Vps18 ( 5′-CTAGATAGCTGGAGAGATG-3′ ) , human Vps33a ( 5′-CATTGCAGTGTTGCCTCGATATG-3′ ) , human Vps39 ( ON-TARGET plus SMART pool ) , mouse Vps39 ( ON-TARGET plus SMART pool ) , human Vps41 ( 5′-CCATTGACAAACCACCATTTA-3′ ) , mouse Vps41 ( ON-TARGET plus SMART pool ) , human PLEKHM1 ( 5′-CCGGTCTCTGCAAGAGGTATTGT-3′ ) , human SKIP ( 5′-CTTCTGAACTGGACCGATT-3′ ) , human Vti1b ( ON-TARGET plus SMART pool ) , human Stx8 ( ON-TARGET plus SMART pool ) , human Stx17 ( ON-TARGET plus SMART pool ) , human Vamp7 ( ON-TARGET plus SMART pool ) and human TGFBRAP1 ( ON-TARGET plus SMART pool ) were purchased from GE Healthcare ( Dharmacon ) , and transfection was performed using Dharmafect 1 as per the manufacturer's instructions . Cells were fixed in 4% p-formaldehyde ( PFA ) in PHEM buffer ( 60 mM PIPES , 10 mM EGTA , 25 mM HEPES , and 2 mM MgCl2 , final pH 6 . 8 ) for 10 min at room temperature . Post fixation , cells were incubated with blocking solution ( 0 . 2% saponin + 5% FBS in PHEM buffer ) at room temperature for 30 min , followed by three washes with 1X PBS . After this blocking step , cells were incubated with primary antibodies in staining solution ( PHEM buffer + 0 . 2% saponin ) for 1 hr at room temperature , washed thrice with 1X PBS , and further incubated for 30 min with Alexa fluorophore-conjugated secondary antibodies made in staining solution . Cells were washed thrice with 1X PBS and mounted in Fluoromount G ( Southern Biotech ) . Single-plane confocal images were acquired using a 710 Confocal Laser Scanning Microscope ( ZEISS ) equipped with a Plan Apochromat 63×/1 . 4 NA oil immersion objective and high-resolution microscopy monochrome cooled camera AxioCamMRm Rev . 3 FireWire ( D ) ( 1 . 4 megapixels , pixel size 6 . 45 μm × 6 . 45 μm ) . For image acquisition , ZEN Pro 2011 ( ZEISS ) software was used . All images were captured to ensure that little or no pixel saturation is observed . The representative confocal images presented in figures were imported into Adobe Photoshop CS and formatted to 300 dpi resolution . The whole image adjustment of brightness was done using curves function . For all the colocalization analysis , at least 30 cells for each treatment per experiment were used for three independent experiments . Pearson’s Correlation Coefficient ( PCC ) was determined using the JACoP plugin of ImageJ where the threshold was set using maximum entropy . In order to trace the endocytic route , HeLa cells were incubated with Alexa-Fluor 647-conjugated dextran ( Molecular Probes ) for 16–18 hr . The cells were washed once with 1X PBS and infected with GFP-expressing Salmonella ( at an MOI 50:1 ) and further incubated in a dextran-free medium for the rest of the experiment . Live-cell imaging was initiated at indicated time-points . For live-cell imaging experiments , cells were seeded on glass-bottom tissue culture treated cell imaging dish ( Eppendorf ) and infected with either DsRed- or GFP- expressing Salmonella strains ( at an MOI 50:1 ) as described above . Post-infection , imaging dish was loaded into a sealed live-cell imaging chamber ( 37°C and 5% CO2 ) for imaging in DMEM . Time-lapse confocal images were acquired at specified time-points using an LSM 710 confocal microscope with a LCI Plan Neofluar objective 63×/1 . 3 multi-immersion correction and equipped with a high-resolution microscopy monochrome cooled camera AxioCamMRm Rev . 3 FireWire ( D ) . Image acquisition and adjustments to brightness and contrast was performed by using ZEN Pro 2011 software . Sample processing and TEM was performed at the Harvard Medical School EM Facility ( Boston , USA ) . Briefly , control shRNA or Vps41 shRNA transduced HeLa cells were infected with S . typhimurium SL1344 for 10 hr . Post-infection , cells were fixed in routine fixative ( 2 . 5% glutaraldehyde/1 . 25% PFA in 0 . 1 M sodium cacodylate buffer , pH 7 . 4 ) for at least 1 hr at room temperature and washed in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) . The cells were then post fixed for 30 min in 1% osmium tetroxide/1 . 5% potassium ferrocyanide , washed in water three times , and incubated in 1% aqueous uranyl acetate for 30 min , followed by two washes in water and subsequent dehydration in grades of alcohol ( 5 min each: 50 , 70 , 95 , 2× 100% ) . Cells were removed from the dish in propylene oxide , pelleted at 3000 rpm for 3 min , and infiltrated overnight in a 1:1 mixture of propylene oxide and TAAB Epon ( Marivac Canada ) . The samples subsequently embedded in TAAB Epon and polymerized at 60°C for 48 hr . Ultrathin sections were cut on a Reichert Ultracut-S microtome , picked up onto copper grids stained with lead citrate , and examined in a JEOL 1200EX transmission electron microscope . Images were recorded with an AMT 2k charge-coupled device camera . Sample fixation for immunogold EM was carried out as described previously [26] , and double immunogold labeling and imaging was performed at the Harvard Medical School EM Facility ( Boston , USA ) . For preparation of cryosections , control siRNA- and Vps41 siRNA-treated HeLa cells were infected with S . typhimurium as described above . After 2 hr p . i . , cells were transfected with HA-Rab7 expressing construct and 10 hr p . i . cells were fixed with 4% PFA + 0 . 1% glutaraldehyde ( Glu ) prepared in 0 . 1 M sodium phosphate buffer , pH 7 . 4 . After 2 hr fixation at room temperature , the cell pellet was washed once with PBS and then placed in PBS containing 0 . 2 M glycine for 15 min to quench free aldehyde groups . Before freezing in liquid nitrogen , the cell pellets were cryoprotected by incubating in three drops of 2 . 3 M sucrose in PBS for 15 min . Frozen samples were sectioned at -120°C , and the sections were transferred to formvar/carbon-coated copper grids . Grids were floated on PBS until the immunogold labeling was performed . The double immunogold labeling was performed at room temperature on a piece of parafilm . All the primary antibodies and Protein A immunogold were diluted in 1% Bovine Serum Albumin ( BSA ) in PBS . In brief , grids were floated on drops of 1% BSA for 10 min to block for unspecific labeling , transferred to 5 μl drops of rat anti-HA , and incubated for 30 min . The grids were then washed in four drops of PBS for a total of 15 min , transferred to 5 μl drops of rabbit anti-rat for 30 min , and washed again in four drops of PBS for 15 min , followed by 15 nm Protein A immunogold for 20 min ( 5 μl drops ) . After the 15 nm Protein A immunogold incubation , grids were washed in four drops of PBS , fixed for 2 min with 0 . 5% Glu followed by four drops of PBS containing 0 . 2 M glycine for 15 min to quench free aldehyde groups . The labeling process was repeated with rabbit anti-LAMP1 followed by 10 nm Protein A immunogold for 20 min in 5 μl drops . Finally , the grids were washed in four drops of PBS and six drops of double-distilled water . Contrasting/embedding of the labeled grids was performed on ice in 0 . 3% uranyl acetate in 2% methyl cellulose for 10 min . Grids were picked up with metal loops , and the excess liquids were removed by blotting with a filter paper and were examined in an electron microscope ( 1200EX; JEOL ) . Images were recorded with an AMT 2k CCD camera . For lysates , cells were lysed in ice-cold lysis buffer ( 25 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 and protease inhibitor cocktail ) . For co-IP experiments , HEK293T cells treated with control or gene-specific siRNA and transfected with indicated plasmids were lysed in ice-cold TAP lysis buffer ( 20 mM Tris , pH 8 . 0 , 150 mM NaCl , 0 . 5% NP-40 , 1 mM MgCl2 , 1 mM Na3VO4 , 1 mM NaF , 1 mM PMSF , and protease inhibitor cocktail; Sigma-Aldrich ) . The lysates were incubated with indicated antibody-conjugated agarose beads at 4°C rotation for 3 hr , followed by four washes in TAP wash buffer ( 20 mM Tris , pH 8 . 0 , 150 mM NaCl , 0 . 1% NP-40 , 1 mM MgCl2 , 1 mM Na3VO4 , 1 mM NaF , and 1 mM PMSF ) . The samples were then loaded on SDS-PAGE for further analysis . Protein samples separated on SDS-PAGE were transferred onto polyvinylidene fluoride ( PVDF ) membranes ( Bio-Rad Laboratories ) . Membranes were blocked overnight at 4°C in blocking solution ( 10% skim milk in 0 . 05% PBS-Tween 20 ) . Indicated primary and secondary antibodies were prepared in 0 . 05% PBS-Tween 20 . The membranes were washed for 10 min thrice with 0 . 05% PBS-Tween 20 or 0 . 3% PBS-Tween 20 after 1 hr incubation with primary antibody and 1 hr incubation with secondary antibody , respectively . The blots were developed using a chemiluminescence-based method ( Pierce ) . To enumerate intracellular Salmonella growth , gentamicin protection assay was performed . Briefly , cells were infected with designated Salmonella strain for different time periods using the protocol described above . At the end of every time point p . i . cells were gently washed with PBS followed by lysis using PBS containing 0 . 1% Triton X-100 and 1% SDS for 5 min at room temperature . The resulting lysates were serially diluted and plated onto LB agar plates containing streptomycin . To assess cytosolic Salmonella replication in HeLa cells upon Vps41 depletion , CHQ resistance assay was performed as described previously [56] . Briefly , control siRNA- or Vps41 siRNA-treated HeLa cells were seeded in 24-well plates and infected with S . typhimurium as described above . To evaluate cytosolic replication of Salmonella , 1 hr prior to 7 hr p . i . time point , two wells were treated with CHQ ( 150 μM ) and gentamicin ( 5 μg/ml ) for 1 hr ( CHQ-resistant bacteria ) and another two wells were incubated with gentamicin ( 5 μg/ml ) only ( total bacteria ) . At the end of 7 hr p . i . time point , duplicate gentamicin treated ( total CFU ) and duplicate CHQ + gentamicin-treated cells ( cytosolic CFU ) were solubilized and serial dilutions were plated on LB agar for CFU enumeration . Six weeks old C57BL/6 male mice were obtained from the CSIR-Institute of Microbial Technology ( IMTECH ) animal house facility and injected intravenously ( i . v . ) with 12 . 5 mg/Kg ( of body weight ) of either control ( CCTCTTACCTCAGTTACAATTTATA ) or mouse VPS41-specific ( CCATAGCGCAGCCTGAGAGTCAT ) vivo-morpholinos ( purchased from Gene Tools , LLC ) for two consecutive days at an interval of 24 hr , followed by Salmonella infection the third day . For Salmonella infection , stationary phase culture of S . typhimurium strain SL1344 was diluted to a CFU of ~1 . 3X103 in 100 μl of 1X PBS and injected i . v . The infectious dose was quantified by plating plating dilution series on LB agar plates containing streptomycin . Mice were sacrificed after 3 days and dilution series of spleen and liver lysates ( prepared in 0 . 05% sodium deoxycholate in 1X PBS ) were plated on LB agar plates containing streptomycin . This study was carried out in strict accordance to the guidelines issued by the Committee for the Purpose of Supervision of Experiments on Animals ( No . 55/1999/CPCSEA ) under the Prevention of Cruelty to Animals Act 1960 and amendments introduced in 1982 by Ministry of Environment and Forest , Government of India . All protocols involving mice experiments were approved by the Institutional Animal Ethics Committee ( IAEC ) of Council of Scientific and Industrial Research-Institute of Microbial Technology ( Approval no . IAEC/16/12 and IAEC/17/13 ) . Roughly 50 million HeLa cells infected with S . typhimurium SL1344 strain were used for subcellular fractionation of SCVs . At 3 hr and 8 hr p . i . , cells were washed thrice with ice-cold PBS and scrapped into a 15 ml centrifuge tube using a rubber cell scrapper . The cells were centrifuged at 1000 rpm for 7 min and the cell pellets were suspended in ice-cold homogenization buffer ( 250 mM sucrose , 20 mM HEPES ( pH 7 . 2 ) , 0 . 5 mM EGTA and 5 μg/ml Cytochalasin D ) containing protease inhibitor cocktail ( Sigma-Aldrich ) and transferred to a Dounce Homogenizer with a tight fitting pestle on ice to break the cells . Approximately 30 strokes were applied until almost 90% of the cells were broken without breaking the nuclei . The intact cells and nuclei were pelleted in microcentrifuge tube at 400 x g for 3 min . The resulting supernatant was collected in a fresh microcentrifuge tube to yield the post nuclear supernatant ( PNS ) . The PNS was brought to a final concentration of 39% sucrose and layered on to 2 ml 55% sucrose which was in turn layered onto 65% sucrose cushion in a 13 . 2 ml open top Beckman ultracentrifuge tube followed by addition of 2 ml 32 . 5% and 2 ml 10% sucrose solutions . All sucrose solutions ( w/v ) were prepared in 20 mM HEPES ( pH 7 . 2 ) and 0 . 5 mM EGTA . The PNS layered on sucrose gradient was then subjected for ultracentrifugation in a swinging bucket rotor for 1 hr at 100000 x g at 4°C . The fractions of 1 ml each were collected from top to bottom . Pooled fractions 8–10 were adjusted very slowly to a final sucrose concentration of 11% with homogenization buffer without sucrose and layered on 15% Ficoll cushion ( 5% sucrose , 0 . 5 mM EGTA and 20 mM HEPES pH 7 . 2 ) . The samples in open top Beckman ultracentrifuge tube were spun at 18000 x g for 30 min in a Beckman SW 41 Ti rotor at 4°C . The supernatant was discarded and pellet was resuspended in 11 ml homogenization buffer . The samples were spun again at 18000 x g for 20 min in a Beckman SW 41 Ti rotor at 4°C and the resulting pellet was labeled as “SCV” fraction . The pelleted SCV fractions were resuspended in 20 μl of 4X SDS-sample buffer , boiled for 10 min and analyzed by SDS-PAGE and immunoblotting . A previously published three-step approach , lysis of infected host cells followed by intracellular compartment enrichment and affinity-IP , was used to enrich and determine the presence of HOPS subunits in SMMs [45] . Briefly , 16 hr prior to infection , 5 million HeLa cells were seeded in a 10-cm tissue culture dish and four 10-cm dishes were used per IP . For infection , S . typhimurium SL1344 sseF harboring a low-copy expression vector with a C-terminal HA-tagged SseF and its cognate chaperone sscB ( sseF/SseF-HA ) was used , and cells infection for a period of 8 hr was carried out as described above . Post-infection , cells were washed thrice with ice-cold PBS and scrapped into a 15 ml centrifuge tube using a rubber cell scrapper , and centrifuged at 1000 x g for 7 min . The resulting cells pellet was suspended in ice-cold homogenization buffer ( 250 mM sucrose , 20 mM HEPES , 0 . 5 mM EGTA , pH 7 . 4 ) , centrifuged at 1000 x g for 10 min , and resuspended in 1 ml of 4°C pre-cooled homogenization buffer containing protease inhibitor cocktail ( Sigma-Aldrich ) . The cells were mechanically disrupted by adding 100 μl of 0 . 5 mm glass beads ( Sigma-Aldrich ) using a vortexer ( three 1 min strokes ) with 5 min of intermediate cooling on ice . The lysate was centrifuged at 100 x g for 10 min at 4°C , and the resulting pellet ( labeled as “GEMN pellet” ) was washed twice with ice-cold homogenization buffer with protease inhibitor mixture . The final GEMN pellet was resuspended in 500 μl of homogenization buffer supplemented with 1 . 5 mM MgCl2 and treated with DNase I ( 50 μg/ml ) for 30 min at 37°C . The protein concentration in the GEMN protein fraction was determined using Bradford reagent ( Bio-rad ) . For affinity-IP , 500 μg of GEMN proteins adjusted to a final volume of 500 μl in solubilization buffer ( 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 1% NP-40 ) were added to 20 μl of pre-blocked ( in 1% BSA made in PBS for 30 min ) anti-HA antibody-conjugated agarose beads or anti-myc antibody-conjugated agarose beads ( Sigma-Aldrich ) as a control , and were allowed to mix on rotary shaker at 4°C for 4 hr . At the end of the incubation period , beads were washed five times with 0 . 1% NP-40 made in PBS to remove non-specific proteins . Finally , the beads were resuspended in 20 μl of 4X SDS-sample buffer , boiled for 10 min and analyzed by SDS-PAGE and immunoblotting . For protein expression and purification , bacterial expression vectors encoding for GST or GST tagged-SifA were transformed into E . coli BL21 strain . Primary cultures of a transformed single colony were set up for 12 hr at 37°C in LB broth containing plasmid vector antibiotic . Secondary cultures were set up using 1% primary inoculums and subjected to incubation at 37°C to an absorbance of 0 . 6 at 600 nm and then protein production was induced using 0 . 5 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) for 5 hr at 30°C . After the incubation period , bacterial cultures were centrifuged at 4 , 000 rpm for 15 min , washed once with 1X PBS , and resuspended in ice-cold buffer ( 20 mM HEPES and 150 mM NaCl , pH 7 . 4 ) containing protease inhibitor tablet ( Roche ) and 1 mM PMSF . Cell lysis was performed by sonication , followed by centrifugation at 12 , 000 rpm for 15 min at 4°C . The supernatants were incubated with glutathione resin ( Gbiosciences ) on rotation for 2 hr at 4°C to allow binding of GST or GST tagged-SifA , followed by 10 washes with wash buffer ( 20 mM HEPES , 300 mM NaCl , and 0 . 5% Triton X-100 , pH 7 . 4 ) . For use in the pulldown assays , protein-bound glutathione resins were blocked with 5% BSA in PBS for 2 hr at 4°C . For pulldown assays , transfected HEK 293T cells were lysed in ice-cold TAP lysis buffer , and lysates were incubated with protein-bound glutathione resins at 4°C for 3 hr with rotation . Samples were washed four times with TAP wash buffer , and elution was performed by boiling the samples in 1X SDS-PAGE loading buffer and loaded onto SDS-PAGE for analysis . For the yeast two-hybrid assay , plasmids encoding GAL4-activation domain ( AD ) and GAL4-DNA binding domain ( BD ) fusion encoding constructs were co-transformed in S . cerevisiae AH109 strain , streaked on SD plates lacking leucine and tryptophan ( SD-L/-W ) and allowed to grow at 30°C for 3 days . The co-transformants were replated on non-selective medium ( SD-L/-W ) and selective medium ( SD-leucine/-tryptophan/-histidine; SD-L/-W/-H ) to assess interaction . For measuring yeast growth rate , primary cultures were seeded in SD-L/-W broth ( Clontech ) from single colonies of S . cerevisiae AH109 strain co-transformed with indicated plasmids , and grown overnight at 30°C to saturation . The resulting cultures were diluted to approximately 0 . 1 OD ( at 600 nm ) in SD-L/-W/-H broth ( Clontech ) and culture growth was monitored every 4 hr for 48 hr . For performing the yeast three-hybrid assay , the S . cerevisiae Gold strain ( Clontech ) was made sensitive to methionine ( Met ) by streaking the yeast on an SD/-Met media at least two times before transforming with the desired plasmids . After co-transformation , yeast cells were replated on SD-L/-W ( nonselective; selects only for the presence of plasmid ) or SD-L/-W/-H/-M ( selective; requires interaction of bait and prey proteins through the linker protein for growth ) . The acidotropic dye LysoTracker Red DND-99 ( Thermo Fisher Scientific ) was diluted in Opti-MEM without phenol red ( Thermo Fisher Scientific ) . To control siRNA- or Vps41 siRNA-treated HeLa cells cultures 100 nM LysoTracker Red was added and uptake for 1 hr was performed . At the end of the internalization period , cells were washed and then resuspended in fresh pre-warmed medium , and red lysosomal fluorescence of 10 , 000 cells per sample was determined by flow cytometry ( BD Accuri ) . FlowJo v10 software was used to analyze all of the data from flow cytometric experiments . For visualization of LysoTracker Red-uptake signal by confocal microscopy , at the end of the dye uptake cells were fixed in 4% PFA in PHEM buffer at room temperature for 10 min . Post-fixation , cells were washed and mounted on glass slides and analyzed . Statistical analyses were performed with Prism 6 software ( GraphPad ) . Data are presented as mean ± standard deviation ( S . D . ) unless otherwise indicated . P-values were calculated using two-tailed unpaired Student’s t test , and differences were considered significant when P < 0 . 05 . The sample sizes are specified in the figure legends for all of the quantitative data . | Intracellular pathogens have devised various strategies to subvert the host membrane trafficking pathways for their growth and survival inside the host cells . Salmonella is one such successful intracellular pathogen that redirects membrane and nutrients from the host endocytic compartments to its replicative niche known as the Salmonella-containing vacuole ( SCV ) via establishing an interconnected network of tubules ( Salmonella-induced filaments or SIFs ) that form a continuum with the SCVs . How Salmonella ensures a constant supply of endocytic cargo required for its survival and growth remained unexplored . Our work uncovers a strategy evolved by Salmonella wherein it secretes a bacterial effector into the host cytosol that recruits component of host vesicle fusion machinery-HOPS complex to SCVs and SIFs . HOPS complex promotes docking of the late endocytic compartments at the SCV membrane , prior to their fusion . Thus , depletion of HOPS subunits both in cultured cell lines as well as a mouse model inhibits Salmonella replication , likely due to reduced access to host membranes and nutrients by the vacuolar bacteria . These findings provide mechanistic insights into how this pathogen reroutes the host’s endocytic transport towards its vacuole , ensuring its own intracellular survival and replication . | [
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... | 2017 | Salmonella exploits the host endolysosomal tethering factor HOPS complex to promote its intravacuolar replication |
Neural populations encode information about their stimulus in a collective fashion , by joint activity patterns of spiking and silence . A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input . For large populations , direct sampling of these distributions is impossible , and so we must rely on constructing appropriate models . We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli , dependencies between cells play an important encoding role . We introduce the stimulus-dependent maximum entropy ( SDME ) model—a minimal extension of the canonical linear-nonlinear model of a single neuron , to a pairwise-coupled neural population . We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus . We show how the SDME model , in conjunction with static maximum entropy models of population vocabulary , can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population .
Neurons represent and transmit information using temporal sequences of short stereotyped bursts of electrical activity , or spikes [1] . Much of what we know about this encoding has been learned by studying the mapping between stimuli and responses at the level of single neurons , and building detailed models of what stimulus features drive a single neuron to spike [2]–[4] . In most of the nervous system , however , information is represented by joint activity patterns of spiking and silence over populations of cells . In a sensory context , these patterns can be thought of as codewords that convey information about external stimuli to the central nervous system . One of the challenges of neuroscience is to understand the neural codebook—a map from the stimuli to the neural codewords—a task made difficult by the fact that neurons respond to the stimulus neither deterministically nor independently . The structure of correlations among the neurons determines the organization of the code , that is , how different stimuli are represented by the population activity [5]–[8] . These correlations also determine what the brain , having no access to the stimulus apart from the spikes coming from the sensory periphery , can learn about the outside world [9]–[11] . The source of these correlations , which arise either from the correlated external stimuli to the neurons , from “shared” local input from other neurons , or from “private” independent noise , has been heavily debated [12]–[15] . In many neural systems , the correlation between pairs of ( even nearby or functionally similar ) neurons was found to be weak [16]–[18] . Similarly , the redundancy between pairs in terms of the information they convey about their stimuli was also typically weak [19]–[21] . The low correlations and redundancies between pairs of neurons therefore led to the suggestion that neurons in larger populations might encode information independently [22] , which was echoed by theoretical ideas of maximally efficient neural codes [23]–[25] . Recent studies of the neural code in large populations have , however , revealed that while the typical pairwise correlations may be weak , larger populations of neurons can nevertheless be strongly correlated as a whole [18] , [26]–[33] . Maximum entropy models of neural populations have shown that such strong network correlations can be the result of collective effects of pairwise dependencies between cells , and , in some cases , of sparse high-order dependencies [18] , [34]–[36] . Most of these studies have characterized the strength of network effects and spiking synchrony at the level of the total vocabulary of the population , i . e . the distribution of codewords averaged over all the stimuli . It is not immediately clear how these findings affect stimulus encoding , where one needs to distinguish the impact of correlated stimuli that the cells receive ( “stimulus correlations” ) , from the impact of co-variance of the cells conditional on the stimulus ( “noise correlations” ) . For small populations of neurons , it has been shown that taking into account correlations for decoding or reconstructing the stimulus can be beneficial compared to the case where correlations are neglected ( e . g . [35] , [37]–[40] ) . Similarly , generalized linear models highlighted the importance of dependencies between cells in accounting for correlations between pairs and triplets of retinal ganglion cell responses [41] . Here we present a new encoding model that allows us to study in fine detail the codebook of a large neural population . We define the codewords to be the joint activity patterns of the population in time windows whose duration reflects the typical width of the cross-correlation of spiking between pairs of neurons . Importantly , this model gives a joint probability distribution over the activity patterns of the whole population for a given stimulus , while capturing both the stimulus and noise correlations . This new model belongs to a class of maximum entropy models with strong links to statistical physics [27] , [42]–[53] and is directly related to maximum entropy models of neural vocabulary [18] , [27]–[32] , allowing us to estimate the entropy and its derivative quantities for the neural code . In sum , the maximum entropy framework enables us to progress towards our goal of focusing attention on the level of joint patterns of activity , rather than capturing low-level statistics ( e . g . , the individual firing rates ) of the neural code alone . We start by showing that linear-nonlinear ( LN ) models of retinal ganglion cells responding to spatially unstructured stimuli capture a significant part of the single neuron response , but still miss much of the detail; in particular , we show that they fail to capture the correlation structure of firing among the cells . We next present our new stimulus-dependent maximum entropy ( SDME ) model , which is a hybrid between linear-nonlinear models for single cells and the pairwise maximum entropy models . Applied to groups of neurons recorded simultaneously , we find that SDME models outperform the LN models for the stimulus-response mapping of single cells and , crucially , give a significantly better account of the distribution of codewords in the neural population .
Using repeated presentations of the same movie , we estimated the average response of each of the cells across repeats , , or the peri-stimulus time histogram ( PSTH ) . Following Refs . [4] , [55] , we fitted a linear-nonlinear model for each of the cells in the experiment , so that the resulting model for the population as a whole is a set of uncoupled , conditionally independent LN neurons that we denote together as a ‘S1’ model ( the reason for this notation will be explained later ) . The predicted rate of every neuron is then , where is a linear filter matched for the -th cell , is its point-wise nonlinear function , and is the stimulus fragment from time until ( here we used , making a vector of light intensities with 40 components ) . Linear filters were reconstructed using reverse correlation ( spike-triggered average ) , and nonlinearities were obtained by histograming into adaptively-sized bins and obtaining by inverting using Bayes' rule . These LN models captured most of structure of the PSTH , yet as the example cell in Fig . 2a shows , they often misestimated the exact firing rates of the neuron , or sometimes even missed parts of the neural response altogether . For the Gaussian FFF , the normalized ( Pearson ) correlation between the measured and predicted PSTH , , was ( mean std across 100 cells ) . The performance gap of the canonical LN models in predicting single neuron responses suggests that either the single-neuron models need to be improved to account for the observed behavior , or that interactions between neurons play an important encoding role and need to be included . Clearly , the firing rate prediction performance can be improved for single neurons by models with higher-dimensional stimulus sensitivity ( e . g . [55] , [56] ) or dynamical aspects of spiking behavior ( e . g . [57] , [58] ) . However , previous work ( and results below ) demonstrated that even conditionally-independent models which by construction perfectly reproduce the firing rate behavior of single cells , often fail to capture the measured correlation structure of firing between pairs of cells , as well as higher-order statistical structure [18] . We therefore sought a model of the neural code that would be able to reproduce the correlation structure of population codes . We asked whether a model that combined the LN ( receptive-field based ) aspect of single cells with the interactions between cells , could give a better account of the neural stimulus-response mapping . Importantly , the new model should capture not only the firing rate of single cells but also accurately predict the full distribution of the joint activity patterns across the whole population . Because the joint distributions of activity are high-dimensional ( e . g . , the distribution over codewords across the duration of the experiment , , has components ) , this is a very demanding benchmark for any model . We propose the simplest extension to the conditionally-independent set of LN models for each cell in the recorded population , by including pairwise couplings between cells , so that the spiking of cell can increase or decrease the probability of spiking for cell [59] , [60] . Importantly , in contrast to previous models , we introduce this coupling so that the resulting model is a maximum-entropy model for , the conditional distribution over population activity patterns given the stimulus . We recall that the maximum entropy models give the most parsimonious probabilistic description of the joint activity patterns , which perfectly reproduces a chosen set of measured statistics over these patterns , without making any additional assumptions [61] . Specifically , we construct a model that relies only on the measured overall correlations between pairs of neurons , which can be reliably estimated from experimental data ( see Methods ) . We find that ( i ) the pairwise correlations between cells in response to the Gaussian FFF movie are typically weak but significantly different from zero ( Fig . 1c , consistent with previous reports [18] , [27] , [32] ) ; ( ii ) the correlation in neural activities shows a fast decay with distance despite the infinite correlation length of the stimulus , but the decay does not reach zero correlation even at relatively large distances ( Fig . 1d ) . This salient structure , along with any other potential statistical correlation at the pairwise order , is characterized by the covariance matrix of activities , , where the averages are taken across time and repeats . We start by introducing the least structured ( maximum entropy ) distribution of the population responses to stimuli , by treating each time point along the stimulus separately; since every moment of time maps uniquely into one stimulus , we start by building the model of the response given time . We thus find that reproduces exactly the observed average firing rate for each time bin in the stimulus and for each neuron , , as well as the overall covariance matrix between all pairs of cells ( c . f . [62] ) . Thus , we seek that maximizes : ( 1 ) where the subscript to brackets denotes whether the averaging is done over the maximum entropy distribution ( ) , or over the recorded data; Lagrange multipliers ensure that the distributions are normalized . This is an optimization problem for parameters and , which has a unique solution since the entropy is convex . The functional form of the solution to this optimization problem is well-known and in our case it can be written as ( 2 ) where the individual time-dependent parameters for each of the cells , , and the stimulus-independent pairwise interaction terms , are set to match the measured firing rates and the pairwise correlations ; is a normalization factor or partition function for each time bin , given by . The pairwise time-dependent maximum entropy ( pairwise TDME or T2 ) model in Eq . ( 2 ) is equivalent to an Ising model from physics , where the single-cell parameters are time-dependent local fields acting on each of the neurons ( spins ) , and static ( stimulus-independent ) infinite-range interaction terms couple each pair of spins . In the limit where interactions go to zero , , the model in Eq . ( 2 ) becomes the full conditionally-independent model , itself a first-order time-dependent maximum entropy model that reproduces exactly the firing rate of every neuron , : ( 3 ) In this case the probability distribution factorizes , and the solution for and becomes trivially computable from the firing rates , . For time bins that are short enough to contain 0 or 1 spike ( as we have assumed throughout ) , is given by . Consistent with our previous notation , we denote this full conditionally-independent model as T1 . Time-dependent maximum entropy models are powerful , since they make no assumption about how the stimulus drives the response; they often serve as useful benchmarks for other models ( especially the T1 model ) . On the other hand , these models require repeated stimulus presentations to fit , involve a number of parameters that grows linearly with the duration of the stimulus , do not generalize to new stimuli , and do not provide an explicit map from the stimuli to the responses . We therefore present a more particular form of the model of Eq . ( 2 ) that , ( i ) , would give an explicit description of stimulus-dependent distribution of population patterns; ( ii ) , would generalize to new stimuli; ( iii ) , could be directly compared to the uncoupled LN models; and ( iv ) , would not require repeats of the same stimulus to fit . Specifically , rather than having an arbitrary time-dependent parameter for every neuron for each time bin , , we assume that this dependence takes place through the stimulus projection alone , i . e . . This is analogous to an LN model , where the neural firing depends on the value of the stimulus projection onto the linear filter . This choice is made for simplicity; this model can be generalized to , e . g . , neurons that depend on two linear projections of the stimulus , by making depend jointly on , although such models would be progressively more difficult to infer from data . Concretely , we estimated the linear filter for each cell using reverse correlation , and convolved the filter with the stimulus sequence , , to get the “generator signal” . We then looked for the maximum entropy probability distribution , by requiring that the average firing rate of every cell given the generator signal is the same in the data and under the model , i . e . ( see Methods ) ; as before , we also required the model to reproduce the overall covariance between all pairs of cells , . This yields a pairwise stimulus-dependent maximum entropy ( pairwise SDME or S2 ) model , which takes the following form: ( 4 ) The parameters of this model are: couplings , parameters , and a linear filter for each cell; these parameters define the energy function of the model . We used a Monte Carlo based gradient descent learning procedure to find the model parameters numerically ( see Methods; note that the problem is still convex with a single solution for the parameter values ) . By construction , the S2 model exactly reproduces the covariance of activities , , between all pairs of cells , and also the LN model properties of every cell: an arbitrary nonlinear function can be encoded by properly choosing how parameters depend on the linear projections of the stimulus , . We can construct a maximum entropy model with ( no constraints on the pairwise correlations ) . The result is a set of uncoupled ( conditionally independent ) LN models: ( 5 ) Fig . 3 shows all the models in a systematic way: the pairwise time-dependent maximum entropy ( T2 ) model of Eq . ( 2 ) is an extension of conditionally independent ( T1 ) model that additionally reproduces the measured pairwise correlations between cells . In a directly analogous way , the pairwise stimulus-dependent maximum entropy ( S2 ) model of Eq . ( 4 ) is an extension to the set of uncoupled LN models ( S1 ) , Eq . ( 5 ) , that additionally reproduces the measured pairwise correlations between cells . Because ( Eq . 4 ) agrees with ( Eq . 5 ) exactly in all constrained single-neuron statistics , any improvement in prediction of the S2 model , be it in the firing rate or the codeword distributions , can be directly ascribed to the effect of the interaction terms , . An alternative approach to describing the joint response of large populations of neurons to external stimuli has been presented in Ref . [41] . The Generalized Linear Model ( GLM ) gives a generative model from which one can sample simulated responses to new stimuli , relying on activity history and temporal dependencies between cells , but assuming conditional independence within any given time bin . We compare the advantages of the two approaches in the Discussion below , but briefly emphasize here that a key difference is that GLM does not present an explicit probability distribution over codewords ( that are defined for temporal bins significantly longer than those of the GLMs ) , which is central for the analysis of the neural code we present below . To assess the accuracy of different stimulus-dependent models , and , in particular , of the contribution of the interactions between cells , we fitted and quantified the performance of the uncoupled LN models ( S1 ) and the pairwise SDME model ( S2 ) . At the level of single neurons , we found that the S2 model predicted the firing rates better than the S1 model ( see e . g . Fig . 2a ) , with the normalized correlation coefficient between the true and predicted firing rate , reaching ( mean std across 100 cells ) , as shown in Fig . 2b . The differences between the S2 and the S1 models become more striking at the level of the activity patterns of the whole population . Figs . 4a , b show the complex structure of the population activity patterns across all 626 repeats at a particular moment in time . During times when the population is active , it generates a wide diversity of patterns in response to the same stimulus; even with hundreds of repeats , these distributions cannot be empirically sampled . Nevertheless , the large number of repeats suffices to identify and estimate reliable low-order marginals of these distributions , in particular , the correlations between the pairs of neurons at various points in time . The wide range of magnitudes of these reliably estimated correlations shows that a number of neuronal pairs are far from conditionally independent . As shown in Fig . 4c , the S2 model captures a significant fraction of this correlation structure on a timebin-by-timebin basis ( on test data ) ; clearly , the S1 model fails at this task . We found that S2 is orders of magnitude better in predicting the population neural responses to stimuli . This is quantified in Fig . 4d , which compares S1 and S2 through the log-likelihood ratio , , for the population activity patterns under the two models . These differences are large in particular for those stimuli that elicit a strong response , that is , precisely where the response consists of synchronous spiking and the structure of the codewords can be nontrivial . Fig . 5 summarizes these results by showing the average log-likelihood ratio over all testing repeats , emphasizing that the difference between the models becomes particularly apparent for groups of more than 20 cells . We next examined how well various models of the neural codebook , , explain the total vocabulary , that is , the distribution of neural codewords observed across the whole duration of the experiment , . Despite the nominally large space of possible codewords—much larger than the total number of samples in the experiment ( ) —the sparsity of spikes and the correlations between neurons restrict the vocabulary to a much smaller set of patterns . Some of these occur many times during our stimulus presentation , allowing us to estimate their empirical probability , , directly from the experiment , and compare it to the model prediction [35] . The most prominent example of such frequently observed codewords is the silent pattern , , which is seen of the time . Fig . 6 shows the likelihood ratio of the model probability and empirical probability for various codewords observed in the test part of the experiment , as a function of the rate at which these codewords appear . Here we used an additional model for comparison , i . e . , the full conditionally-independent model ( T1 ) , where every cell is described in terms of time-dependent firing rate . The S2 model in Fig . 6a strongly outperforms the S1 model in Fig . 6b , and has a slightly better performance than the T1 model ( Fig . 6c ) , despite the fact that the latter is determined by parameters , the firing rates of every cell in every time bin . Quantitatively , the per-codeword log-likelihood of the test data under S1 model is 5 . 30 , under T1 model 4 . 34 , under S2 model 4 . 12 , under empirically sampled distribution on the training set 4 . 02 , while the lower bound on the log-likelihood ( obtained when the “model” are the true empirical frequencies on the test set ) is 2 . 98 ( see Methods ) . On average , S2 predicts the probabilities of the patterns of activity with minimal bias , and with a standard deviation of of about 1; the S1 model in comparison is biased and has a spread that is more than twice as large . Even more striking is the fact that S1 assigns very low probabilities to some codewords such that they were never generated during our Monte Carlo sampling ( and are therefore not even shown in scatterplots of Fig . 6 ) , although they were frequently observed in the experiment . This discrepancy is quantified by enumerating the most probable patterns in the data and in the model ( by sampling , see Methods ) , and measuring the size of the intersection of the two sets of patterns . In other words , we ask if the model is even able to access all the patterns that one is likely to record in the experiment . As shown in the bottom of Fig . 6 , S2 does well on this task , with 419 codewords in the intersection of the most likely patterns in the data and the model . This is a much better performance than the S1 model , and a little better than for the T1 model ( which has many more parameters ) . We emphasize that all these comparisons were done on test data only , so that the models had to generalize over the large diversity of patterns where some of the patterns seen in the training set might never occur on the testing set and vice versa ( see Fig . 4a , b ) . The S2 model was constructed to capture exactly the total pairwise correlation in neuronal spiking , . With repeated stimulus , this total correlation can be broken down into the signal and noise components . The signal correlations , , are inferred by applying the same formula as for the total correlation , but on the spiking raster where the repeated trial indices have been randomly and independently permuted for each time bin . This removes any correlation due to interactions between spikes on simultaneously recorded trials , and only leaves the correlations induced by the response being locked to the stimulus . The noise correlation , , is then defined as the difference between the total and the signal components , . We calculated the noise correlations between all pairs in our neuron dataset . By their definition , the conditionally independent models cannot reproduce , which are always zero for those models . To assess the performance of the S2 model , we drew samples from our model distribution using a Monte Carlo simulation and compared the noise correlations in the simulated rasters to the true noise correlations . The model prediction is tightly correlated with the measured values , as shown in Fig . 7 . We observe a systematic deviation of , most likely because the assumed dependence on the stimulus through one linear filter per neuron is insufficient to capture the complete dependence on stimulus , thereby underestimating the full structure of stimulus correlation and inducing an excess in the noise correlation . Despite this , the degree of correspondence in noise correlations observed in Fig . 7 is telling us that the S2 model has clearly captured a large amount of noise covariance structure in neural firing at the network level . How should we interpret the inferred parameters of the S2 model ? LN models have a clear mechanistic interpretation in terms of the cell's receptive field and the nonlinear spiking mechanism . Here , similarly , the stimulus dependent part of the model for each cell , , is a nonlinear function of a filtered version of the stimulus ; in the absence of neuron-to-neuron couplings , the nonlinearity of every neuron would correspond to , where , according to Eq . ( 5 ) . The dependence of on the stimulus projection is similar across the recorded cells as shown in Fig . 8a; as expected , higher overlaps with the linear filter induce higher probability of spiking . The pairwise interaction terms in the S2 model , , are symmetric , static , and stimulus independent by construction . As such , they represent only functional and not physical ( i . e . synaptic ) connections between the cells . Fig . 8b shows the pairwise interaction map for 100 cells; the histogram of their values ( in Fig . 8c ) reflects that they can be of both signs , but the distribution has a stronger positive tail , i . e . a number of cell pairs tend to spike together or be silent together with a probability that is higher than expected from their respective LN models . We can compare these interactions to the interactions of a static ( non-stimulus-dependent ) pairwise maximum entropy model for the population vocabulary [18] , [28]: ( 6 ) In this model for the total distribution of codewords , there is no stimulus dependence , and the parameters and are chosen so that the distribution is as random as possible , while reproducing exactly the measured mean firing rate of every neuron , and every pairwise correlation , , across the whole duration of the experiment . Interestingly , we find that the pairwise interaction terms in the S2 model of Eq . ( 4 ) are closely related to the interactions in the static pairwise maximum entropy model of Eq . ( 6 ) : S2 interactions , , tend to be smaller in magnitude , but have an equal sign and relative ordering , as the static ME interactions , . Some degree of correspondence is expected: an interaction between neurons and in the static ME model captures the combined effect of the stimulus and noise correlations , while in the corresponding S2 interaction , ( most of ) the stimulus correlation has been factored out into the correlated dynamics of the inputs to the neurons and , i . e . and . The surprisingly high degree of correspondence , however , indicates that even the interactions learned from static maximum entropy models can account for , up to a scaling factor , the pairwise neuron dependencies that are not due to the correlated stimulus inputs . Figs . 4a , b show the richness of activity patterns produced in response to repeats of the same stimulus . While these patterns must encode the same information , it is not clear how this could be established by looking at the patterns alone ( without prior knowledge that they were generated in response to the same stimulus ) , because of the high dimensionality of the pattern space . Is there a way to simplify this response space ? We suggest one such approach here , motivated by the analogy to Ising models in statistical physics and the related similarities with the Hopfield networks [27] , [32] , [62] , [63] . At every instant in time , the probability of any activity pattern in the S2 model is fully specified by the distribution with an exponential form given by Eq . ( 4 ) . In analogy to statistical physics , the exponent is the ( negative ) energy of the state . This energy function defines an instantaneous “energy landscape” over the space of all possible activity patterns . Minima in this landscape can be viewed as metastable patterns or attractors , and all activity patterns can be assigned to their respective attractors by descending on the energy landscape until the closest local minimum is reached , much like in the Hopfield network . In this way , the space of patterns is partitioned , at each point in time , into a number of domains centered on the metastable states . How useful is this representation of the response space ? Using the S2 model fit on training repeats , we examined neural responses in every time bin across all testing repeats . We assigned each response pattern from testing data to its corresponding metastable state . Fig . 9a shows , as a function of time , all identified metastable states , their energies ( i . e . the negative log probability of that state ) , and the number of repeats on which a pattern belonging to that state was emitted . This analysis still paints a rich , but already much simplified picture of the retinal responses , where many patterns are grouped into a small number of clusters centered on the metastable states . Interestingly , these assignments generalize very well: in Fig . 9b we independently identify the metastable states on testing and training sets for each time bin , assign all patterns seen in the experiment to these states , and count and compare how many times each state appears on testing and training repeats . Virtually all ( ) metastable states appearing in training repeats are found on testing repeats and vice versa , and this intersection is vastly larger than the intersection of the activity patterns themselves , a lot of which can appear only once in all 626 repeats . Moreover , the frequency with which patterns belonging to a particular metastable state occur is reproducible between the training and test data , suggesting that the partitioning of the high-dimensional activity space into clusters defined by the energy function of the S2 model is a productive dimensionality reduction method in this context . The S2 model is an approximation to the neural codebook , , while the static ME model describes the population vocabulary , . With these two distributions in hand , we can explore how the population jointly encodes the information about the stimulus into neural codewords—the joint activity patterns of spiking and silence . We make use of the fact that we can estimate the entropy of the maximum entropy distributions using a procedure of heat capacity integration , as explained in Refs . [27] , [32] ( see Methods ) . The information ( in bits ) that the codewords carry about the stimulus is then ( 7 ) that is , the information can be written as a difference of the entropy of the neural vocabulary , and the noise entropy ( the average of the entropy of the codebook ) , where the entropy is . Because of the maximum entropy property of our model for , the entropy of our static pairwise model in Eq . ( 6 ) is an upper bound on the transmitted information; expressed as an entropy rate , this amounts to . The brain does not have direct access to the stimulus , but only receives codewords , drawn from , by the retina . It is therefore useful to estimate for every moment in time , the surprise about the output of the retina , and thus about the stimulus , which is given by . We , as experimenters—but not the brain—have access to stimulus repeats and thus to , so we can compute the average value of surprise ( per unit time ) at every instant in the stimulus: ( 8 ) This quantity can be expressed using the entropies and the learned parameters of our maximum entropy models , and is plotted as a function of time in Fig . 10 . Since averaging across time is equal to averaging over the stimulus ensemble , we see from Eq . ( 8 ) that would have to be identically equal to under the condition that ( marginalization ) . Since we build models for ( static ME ) and ( S2 ) from data independently , they need not obey the marginalization condition exactly , but they will do so if they provide a good account of the data . Indeed , by using the static ME and S2 distributions in Eq . ( 8 ) for surprise , we find that , very close to the entropy rate of the total vocabulary and within the estimated error bars of the entropy , which are 1% . To estimate the information transmission , we have to subtract the noise entropy rate from the output entropy rate , as dictated by Eq . ( 7 ) . The entropy of the S2 model is an upper bound on the noise entropy; since this is not a lower bound , we cannot put a strict bound on the information transmission , but can nevertheless estimate it . Fig . 10 shows the “instantaneous information” [64] , , as a function of time; from Eq . ( 7 ) , the mutual information rate is a time average of this quantity , . We find . This quantity can be compared to the total entropy rate of the stimulus itself ( which must be higher than ) , which in our case is ( see Methods ) . While our estimates seem to indicate that a lot of vocabulary bandwidth ( 730 bit/s ) is “lost” to noise ( 600 bit/s ) , the last comparison shows that the Gaussian FFF stimulus source itself is not very rich , so that the estimated information transmission takes up more than half of the actual entropy rate of the source . Lastly , we asked how important is the inclusion of pairwise interactions , , into the S2 model , compared to the S1 model , when accounting for information transmission . We therefore estimated the noise entropy rate for the S1 model , , which was found to be , considerably higher than the noise entropy of the S2 model . Crucially , this noise entropy rate is larger than the total entropy rate estimated above , which is impossible for consistent models of the neural codebook and the vocabulary ( since it would lead to negative information rates ) . This failure is a quantitative demonstration of the inability of the uncoupled LN models to reproduce the statistics of the population vocabulary , as shown in Fig . 6b , despite a seemingly small performance difference on the level of single cell PSTH prediction .
We presented a modeling framework for stimulus encoding by large populations of neurons , which combines an individual neuronal receptive field model , with the ability to include pairwise interactions between neurons . The result is a stimulus-dependent pairwise maximum entropy ( S2 ) model , which is the most parsimonious model of the population response to the stimulus that reproduces the linear-nonlinear ( LN ) aspect of single cells , as well as the pairwise correlation structure between neurons . In two limiting cases , the S2 model reduces to known models: if the single cell parameters are static , S2 becomes the static pairwise maximum entropy model of the population vocabulary; if the couplings are 0 , S2 reduces to S1 , the set of uncoupled LN models . We applied this modeling framework to the salamander retina presented with Gaussian white noise stimuli , and found that the interactions between neurons play an important role in determining the detailed patterns of population response . In particular , the S2 model gave better prediction of PSTH of single cells , yielded orders-of-magnitude improvement in describing the population patterns , and captured significant aspects of noise correlations . The deviations between the S2 and the S1 model became significant for cells , and tended to occur at “interesting” times in the stimulus , precisely when the neural population was not silent . The S2 model allowed us to improve over LN models for salamander retinal ganglion cells in terms of the PSTH prediction of single cells . But , more importantly , it gave a huge improvement in terms of describing and predicting the population activity patterns , or codewords . Interestingly , for parasol cells in the macaque retina under flickering checkerboard stimulation , the generalized linear model did not yield firing rate improvement relative to uncoupled LN models ( but did improve the prediction of higher order statistics of neural activity ) [41] . In both cases , however , the improvements reflect the role of dependencies among cells in encoding the stimulus , and their effect becomes apparent when we ask questions about information transmission by a neural population . Maximum entropy models can only put upper bounds on the total entropy and the noise entropy of the neural code ( and this statement remains true even if successive codewords are not independent ) , and as such cannot set a strict bound , but only give an estimate , for the information transmission . Nevertheless , ignoring the inter-neuron dependencies by using the S1 model would predict the total population responses so badly that the estimated noise entropy would be higher than the upper bound on the total entropy , which is a clear impossibility . In contrast , S2 model gives noise entropy rates that are consistent with the estimate from the static maximum entropy model , and transmission rates that amount to about 60% of the source entropy rate ( comparable to estimates of coding efficiency in single neurons , e . g . , Ref . [65] ) . An alternative approach to describing the joint response of large populations of neurons to external stimuli has been presented in Ref [41] . The Generalized Linear Model ( GLM ) gives a generative model from which one can sample simulated responses to new stimuli , relying on activity history and temporal dependencies between cells . The crucial assumption of the GLM is that the responses of the neurons are conditionally independent given the stimulus and the spiking history; to satisfy this assumption , the discretization of time has to be as fine grained as possible , but certainly well below the discretization of or typically used for maximum entropy models in our retinal preparation . This conditional independence , guaranteed by very short time bins , allows tractable inference procedures to be devised for fitting the GLMs from data . On the other hand , it makes—by its very definition—successive activity patterns dependent on each other , because that is the only way to introduce interactions between the spikes . In contrast , maximum entropy models pick the time bin to be short enough such that multiple spikes are rarely observed in the same time bin , but long enough so that most of the strong spike-spike interactions ( as well as fine temporal detail , such as spike-timing jitter ) occur within a single bin . This allows us to view activity patterns in successive time bins as codewords ( although some statistical dependence between them remains: in the SDME models this is probably due to multiple timescales on which the neurons respond to stimuli; and in the static ME model [31] due to , in part , stimulus correlation ) . If we were to make the time scale in maximum entropy models much shorter , e . g . by an order of magnitude or more , we could make the conditional independence assumption of the responses given the stimuli and previous spiking . This would lead us to GLM-like models in the maximum entropy framework , e . g . , to dynamic/nonequilibrium generalizations of Ising models [48]; in this case , however , we would again lose the interpretation where the instantaneous state of the retina is represented well by a single codeword . For this reason , GLM and SDME are complementary approaches: the first allows for a temporally-detailed probabilistic description of a spiking process , while the second gives an explicit expression for the probability distribution over codewords in longer temporal bins . To our knowledge , there is no easy way to derive one model from the other: while one can fit the GLM with a very small time bins , use it to generate rasters and re-discretize those into time bins of longer duration to get a codeword representation , building a probabilistic model for the codewords from the GLM-derived rasters is as difficult as building it for original data . While a more detailed comparison of these models is beyond the scope of the current work , it is interesting to note that these approaches are different and complementary also in terms of the potential interpretation of their parameters: GLM couplings between neurons have an intuitive interpretation in terms of causal dependency between cells , whereas the SDME ones suggest a prior on the coding vocabulary of the population ( see below ) . Finally , from a modeling viewpoint , GLM lends itself to a clean and tractable maximum likelihood inference framework with regularization , whereas the SDME offers the tools and insights of statistical physics [27] , [42]–[53] ( including , e . g . , advanced Monte Carlo schemes for entropy estimation [66] and the partitioning of the space of codewords in terms of metastable states briefly discussed in this paper ) . Tkačik and colleagues [62] have suggested that one can interpret in an SDME model as a prior over the activity patterns that the population would use to optimally encode the stimulus . For low noise level they argued that the prior should be “weak” ( and could help decorrelate the responses ) because the population could faithfully encode the stimulus , whereas in the noisy regime , the prior should match the statistics of the sensory world and thus counteract the effects of noise . Berkes and colleagues [67] suggested a similar reason for the relationship between ongoing and induced activity patterns in the visual cortex . Our results show that interactions are necessary for capturing the network encoding , and implicitly reflect the existence of such a prior . The recovered interactions are strongly correlated with the interaction parameters of a static , stimulus independent model over the distribution of patterns , making it possible for the brain ( which only has access to the spikes , not the stimulus ) to learn these values . Whether the interactions are matched to the statistics of the visual inputs as suggested in Ref [62] will be the focus of future work . The maximum entropy models presented here can be immediately applied to other brain areas where one can get stable recordings of many neurons over a few tens of minutes [35] , [68] . SDME could be applied to spatially structured stimuli , for instance , to capture the response to the flickering checkerboards: obtaining good estimates of the spatio-temporal receptive fields is standard procedure , identical to that in LN or GLM-type models , while fitting the parameters of the SDME is equally tractable on full field flicker ( as presented here ) or movie with spatial structure . In practice , a different tradeoff would be chosen in experimental design , by making stimulus segment longer to sample the linear filters better from many different stimuli , and decreasing the number of repeats . As we noted above , for fitting the model , one could also eliminate repeated structure altogether , yet repeated presentations of the same stimuli would still be needed to assess the model quality in terms of the PSTH . The current design of the experiment focused on a very large number of repeats of the same stimulus , to allow for as accurate estimate of the PSTH and correlations of individual cells , while future experiments could allow for evaluation of the model on novel repeated stimuli . Given the results we have presented here and those of [41] , we expect that the SDME models would significantly outperform the LN models on novel stimuli as well . Other potential extensions of the pairwise SDME model would include temporal dependencies as in Refs [31] , [49] or a SDME model where the pairwise interactions are also stimulus dependent . While it is not immediately clear how such dependency would be expressed for the ( unlike the linear filter description of the single cell parameters , 's ) , such a model would be instrumental for analysis of population adaptation or learning . Another extension would be to include the dependence of on multiple stimulus projections , or to include high-order interaction terms between spikes , which are likely to play an important role for large populations responding to natural stimuli [34] , [35] . Finally , we also expect that sampling from larger populations , as future experiments will allow , would enable us to give a full characterization of the interaction maps between cells of different classes , which would most likely reflect independence between classes with strong correlations between the cells of the same class , or even stronger correlations at the population level including across different classes; the two alternatives represent an exciting ( and still mostly unanswered ) question . We expect that increasingly detailed statistical models of neural codes , and the efforts to infer such models from experimental data , will allow us to focus our attention on population-level statistics and on finding principled information-theoretic measures for quantifying the code , like the surprise and instantaneous information suggested here .
Experiments were performed on the adult tiger salamander , Ambystoma tigrinum . All experiments were in accordance with Ben-Gurion University of the Negev and government regulations . Extracted retinas were placed with the ganglion cell layer facing a multielectrode array with 252 electrodes ( Ayanda Biosystems , Switzerland ) , and superfused with oxygenated Ringer medium at room temperature . Extracellularly recorded signals were amplified ( MultiChannel Systems , Germany ) and digitized at 10 kHz , and spike-sorted using custom software written in MATLAB . Stimuli were projected onto the retina from a CRT video monitor ( ViewSonic G90fB ) at a frame rate of 60 Hz; each movie frame was presented twice , using standard optics . Full Field Flicker ( FFF ) stimuli were generated by independently sampling spatially uniform gray levels ( with a resolution of 8 bits ) from a Gaussian distribution , with mean luminance of 147 lux and the standard deviation of 33 lux . These data allow us to estimate the entropy rate of the source ( as used in the main text ) , by multiplying the entropy of the luminance distribution with the refresh rate . To estimate the cells' receptive fields , checkerboard stimulus was generated by selecting each checker ( on the retina ) randomly every 33 ms to be either black or white . To identify the RF centers , a two-dimensional Gaussian was fitted to the spatial profile of the response . The movies were gamma corrected for the computer monitor . In all cases the visual stimulus entirely covered the retinal patch that was used for the experiment . The firing rates of the cells and the overall covariance of the spiking activity are the key statistics for inferring the models we present , so the reliability of our estimates for these quantities is a key systematic issue . Previous work has shown that 10–20 minute recordings give very reliable estimates [35] , [68] , and that train data of similar size allows for reliable estimates of pairwise-maximum-entropy-based models for populations of this size [68] . The error on instantaneous firing rate was estimated by splitting 626 repeats into two random halves 50 times , and estimating firing rate for each neuron . The relative error in the firing rate , , estimated as ( relative ) std over random splits of data , scales tightly with the mean firing rate with the power , such that at instantaneous rates of about the error is , at the error is , and at the error is . For correlations , we assess their significance by comparing the distribution of real correlation coefficients to the ( null ) distribution where the spikes for each neuron have been randomized in time . The null distribution is evaluated over one half of the repeats , because this is the data size used for training; the mean of the distribution is , and the std , making 95% of observed correlations larger than this spread due to sampling . More in detail , the relative error on correlations was assessed by splitting data 50 times randomly into two halves , and seeing that the relative error scales with the value of the correlations , so that the typical error at significance threshold was about 60% , for ( 80% of all correlations ) it was 18% , for ( 23% of all correlations ) it was 4% , and for it was less than 2% . The average error on significant correlations is slightly below 10% . To quantify the stability of the recordings across time , we computed for each cell the approximate drift in the firing rate , by linearly regressing the average firing rate in each repeat against the repeat index . For about half of the cells the relative change in the firing rate across the whole duration of the experiment was below 25% ( average 14% ) , while for 80% of the cells the drift was below 50% ( average 24% ) . To deal with the remaining non-stationarity , we selected as our training data all odd numbered repeats , and for our test data all even numbered repeats , so that the models were trained and tested across the non-stationary behavior . The LN model for each neuron consists of the linear filter , and the nonlinear function , which is defined pointwise on a set of binned values for the generator signal , . We used binning into bins such that initially each bin contains roughly the same number of values for , but subsequently the binning is adaptively adjusted ( separately for each neuron ) to be denser at higher values of , where the firing rates are higher . We fitted LN models with varying number of bins , and have chosen when the performance of the LN models appeared to saturate [69] . To find the parameters of the stimulus-dependent maximum entropy model ( ) , we retained the binning of the generator signal used for LN model construction . Given trial values for the SDME parameters , we estimated the chosen expectation values ( covariance matrix of neural activity , and the firing rate conditional on , ) by Monte Carlo sampling from the trial distribution in Eq . ( 4 ) ; the learning step of the algorithm is computed by comparing the expectation values in the trial distribution and the empirical distribution ( computed over the training half of the stimulus repeats ) . In detail , we used a gradient ascent algorithm , applying a combination of Gibbs sampling and importance sampling in order to efficiently estimate the gradient , by using optimizations similar to those described in Ref . [70] . Sampling was carried out in parallel on a 16 node cluster with two 2 . 66 GHz Intel Quad-Core Xeon processors and 16 GB of memory per node . The calculation was terminated when the average error in firing rates and coincident firing rates reached below 1% and 5% respectively , which is within the experimental error . To compute the single neuron PSTH and compare the distributions of codewords from the model to the empirical distribution , we used Metropolis Monte Carlo sampling to draw codewords from the model distributions; we drew 5000 independent samples ( to draw uncorrelated configurations , a sample was recorded only after 100 “spin-flip” trials ) for every timepoint , for a total of samples; the same procedure was used also to draw from the conditionally independent ( T1 , S1 ) models . To estimate the entropies of high dimensional SDME distributions , we used the “heat capacity integration” method , detailed in Ref [32] . Briefly , a maximum entropy model ( where is the Hamiltonian function determined by the choice of constrained operators and the conjugated parameters ) is extended by introducing a new parameter , much like the temperature in physics , so that . The entropy of the distribution is given by , where the heat capacity , and the variance in energy can be estimated at each by Monte Carlo sampling . In practice , we run a separate Monte Carlo sampling for a finely discretized interval of temperatures , , estimate for each temperature , and numerically integrate to get the entropy . We have previously shown that this procedure yields robust entropy estimates even for large numbers of neurons [27] , [32] . To evaluate the performance of the models on the testing data , we computed ( i ) the average per-codeword log-likelihood ( reported in the Results section ) , and ( ii ) the GoF ( goodness-of-fit ) figure , reported in Fig . 6 . Regarding ( i ) , for model the log-likelihood is , where the average is over all testing repeats and all times . For models S1 , S2 , the evaluation is straightforward . For T1 model , there is a problem whenever the firing rate of a neuron in the training set is 0 , which leads to undefined log likelihoods . To address this , we add a small regularizer to the estimated firing rates that define the T1 model , and choose to maximize the log-likelihood of T1 on the test set , thus giving maximal possible advantage to the T1 . We also created two models by empirically sampling the frequencies of codewords on training ( testing ) data . Sampling the frequencies on testing data and evaluating on testing data gives the absolute lower bound to the log likelihood . When the frequencies are sampled on training data , we again face a possible problem for codewords whose empirical probability is 0 , but which occur in test data . We introduce a pseudocount regularizer to give these codewords non-zero probability , and set the regularizer to maximize the log-likelihood on testing data , again maximally favoring this model . Regarding ( ii ) , we compute GoF ( goodness-of-fit ) figure as std , where . is the empirical probability of a codeword on the test set , is its model probability , is the expected error on , computed from the multinomial variance for every codeword given its empirical probability , and the std is taken over all non-silent patterns of the test set plotted in Fig . 6 , top row . | In the sensory periphery , stimuli are represented by patterns of spikes and silences across a population of sensory neurons . Because the neurons form an interconnected network , the code cannot be understood by looking at single cells alone . Recent recordings in the retina have enabled us to study populations of a hundred or more neurons that carry the visual information into the brain , and thus build probabilistic models of the neural code . Here we present a minimal ( maximum entropy ) yet powerful extension of well-known linear/nonlinear models for independent neurons , to an interacting population . This model reproduces the behavior of single cells as well as the structure of correlations in neural spiking . Our model predicts much better the complete set of patterns of spiking and silence across a population of cells , allowing us to explore the properties of the stimulus-response mapping , and estimate the information transmission , in bits per second , that the population carries about the stimulus . Our results show that to understand the code , we need to shift our focus from reproducing single-cell properties ( such as firing rates ) towards understanding the total “vocabulary” of patterns emitted by the population , and that network correlations play a central role in shaping the code of large neural populations . | [
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] | 2013 | Stimulus-dependent Maximum Entropy Models of Neural Population Codes |
Parasitic helminths such as schistosomes , as well as filarial and soil-transmitted nematodes , are estimated to infect at least a billion people worldwide , with devastating impacts on human health and economic development . Diagnosis and monitoring of infection dynamics and efficacy of treatment depend almost entirely on methods that are inaccurate , labor-intensive , and unreliable . These shortcomings are amplified and take on added significance in mass drug administration programs , where measures of effectiveness depend on accurate monitoring of treatment success ( or failure ) , changes in disease transmission rates , and emergence of possible drug resistance . Here , we adapt isothermal molecular assays such as loop-mediated isothermal amplification ( LAMP ) to a simple , hand-held , custom-made field-ready microfluidic device that allows sensitive and specific detection of schistosome cell-free nucleic acids in serum and plasma ( separated with a point-of-care plasma separator ) from Schistosoma mansoni-infected mice . Cell-free S . mansoni DNA was detected with our device without prior extraction from blood . Our chip exhibits high sensitivity ( ~2x10−17 g/μL ) , with a positive signal for S . mansoni DNA detectable as early as one week post infection , several weeks before parasite egg production commences . These results indicate that incorporation of isothermal amplification strategies with our chips could represent a strategy for rapid , simple , low-cost diagnosis of both pre-patent and chronic schistosome infections as well as potential monitoring of treatment efficacy .
Schistosomes and other parasitic helminths are estimated to infect at least a billion people worldwide , as well as domestic and farm animals , with substantial impacts on human health and economic development . Accurate , reliable , and inexpensive diagnostic methods are key for monitoring infection dynamics and treatment efficacy , but technological gaps in current detection methods impose significant limitations on epidemiological analysis and control strategies [1 , 2] . Current parasitological and serological methods for diagnosis of schistosome and other helminth infections have major limitations . For example , the Kato-Katz technique to quantify schistosome eggs is unreliable ( ie , results from the same patient can vary from one day to the next ) and significantly underestimates infection levels [3–5] . It also requires a fully-patent infection and so cannot detect early infections with immature worms . Antigen- and antibody-based serological tests are available , but often lack sensitivity and specificity [2 , 6 , 7] . These limitations are amplified and take on added significance when monitoring mass drug administration programs , where measures of effectiveness depend on the ability to reliably monitor treatment success ( or failure ) , changes in disease transmission rates , and emergence of drug resistance . Indeed , the recent call by the World Health Organization for complete elimination of schistosomiasis transmission [8–10] stressed the need for sensitive and specific point-of-contact ( POC ) diagnostic tools , particularly in low-transmission settings [11] . Nucleic acid-based methods for detection of free parasite DNA in host blood , urine , feces , and tissues hold the promise of dramatic increases in diagnostic sensitivity and specificity for helminth infections . Technological advances and more widely available genomic data have prompted several groups to explore such approaches for a variety of parasitic infections in definitive and intermediate hosts [12–14] . Recent reports have shown that loop-mediated isothermal amplification ( LAMP ) , a highly sensitive amplification technique that does not require thermal cycling ( and its associated costs and equipment ) , can detect parasite DNA in a variety of platyhelminth- and nematode-infected hosts [12] , including those with schistosome [15–19] infections . Like PCR , isothermal amplification methods such as LAMP are readily adaptable to real-time protocols , and can be used in conjunction with reverse transcriptase and other modifications to amplify sequences from RNA and microRNAs [20] . However , despite the clear advantages of molecular detection , deployment of these methods in POC assays has been constrained by the logistical issues and costs of the infrastructure associated with these technologies . Here , we used animal models to adapt LAMP protocols that detect cell-free Schistosoma mansoni DNA in host blood to a simple , inexpensive , disposable , custom-made , field-ready microfluidic cassette for molecular diagnostics [21–23] . The cassette ( hereafter called a chip ) features an array of reaction chambers for isothermal amplification of nucleic acids using LAMP or reverse transcription ( RT ) -LAMP , and potentially other isothermal amplification methodologies . Unique to our chip is the inclusion of a nucleic acid isolation membrane within each amplification reaction chamber ( supporting information S1A Fig ) [22] . Nucleic acids captured on these isolation membranes serve directly as templates for amplification without a separate elution step , thus simplifying flow control . Interfacing our chip with our custom-made plasma separator [24] that combines sedimentation and filtration to separate plasma from whole blood eliminates the need for any infrastructure . Several relatively low-cost options are available for monitoring the amplification reaction , either in real time using a simple fluorescence reader , an inexpensive USB-based microscope that can be connected to a laptop computer , or , most conveniently , with a cell/smart phone camera . At their end points , reactions may be assessed by eye directly , with a reaction-diffusion column [25] , or with a lateral flow strip . Thermal control for the reactions is provided either by a thin film heater ( powered by a DC power source or batteries ) or by an integrated , water-triggered , exothermic chemical reaction ( without the use of any electrical power ) [26] . This lab-on-a chip device has detected as few as 10 copies of HIV in saliva [22] and pathogenic E . coli in urine [26] , and has been used for rapid genotyping of Plasmodium-transmitting mosquitoes [27] . In this report , we show that our device can be used for molecular detection of cell-free parasite DNA from the blood fluke S . mansoni in blood from infected animal hosts . We furthermore show that the chip can detect S . mansoni DNA in serum taken from mice as early as one week post infection , several weeks prior to the onset of parasite egg production . This work serves as proof of principle for this technology that enables sensitive detection of parasite infections using a simple , inexpensive , disposable device that can function in environments lacking even the most basic infrastructural support .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health . Animal handling and experimental procedures were undertaken in compliance with the University of Pennsylvania's Institutional Animal Care and Use Committee ( IACUC ) guidelines ( Animal Welfare Assurance Number: A3079-01 ) . The IACUC approved these studies under protocol number 805244 . The human plasma samples used for diluting parasite genomic DNA came from de-identified , EDTA-anticoagulated , whole blood samples previously collected from healthy adult donors at the Hospital of the University of Pennsylvania . There were no identifiable human subjects used in these experiments , and , following review by the University of Pennsylvania Institutional Review Board ( IRB ) , the use of this blood was designated as not meeting the criteria for human subject research , and was therefore exempted from IRB oversight ( protocol #814752 ) . The nucleic acid amplification cassette is shown in S1A Fig . The 46 mm x 36 mm x 3 . 50 mm cassette consists of three layers: a top cover made of 250 μm thick , poly [methyl methacrylate] ( PMMA ) film; a 3 mm thick , PMMA cassette body; and a 250 μm ( 0 . 01 inch ) thick , PCR Sealers ( Finnzymes ) tape bottom . Both the top and bottom cover films were cut with a CO2 laser ( Universal Laser Systems ) . The cassette body was milled with a precision , computer-controlled milling machine ( HAAS Automation Inc . ) to form three separate reactors ( more are possible , if required ) , silica “glass fiber” membrane porous disc ( “membrane” ) supports , and access conduits . The top PMMA film was solvent-bonded to the cassette body with acetonitrile at room temperature . Residual solvent was removed by overnight heating at 50°C . Each reaction chamber is connected to an inlet port and an exit port with 500 μm-wide , 200 μm-deep conduits . The reactor is 5 . 2 mm in length , 1 . 0 mm in width , and 3 . 0 mm in depth with a total volume of 16 μl . Each reactor is equipped with a flow-through Qiagen silica membrane ( DNeasy Blood & Tissue Kit ) at its entry port . In our experiments , we used two different types of samples: calibrated samples with known quantities of target molecules; and samples obtained from infected animals . Infected-animal samples were tested directly ( whole blood ) and after separating plasma or serum from the whole blood . Calibrated samples consisted of S . mansoni genomic DNA ( gDNA ) spiked in human plasma . S . mansoni gDNA was obtained from the Schistosomiasis Resource Center , housed at the Biomedical Research Institute , for distribution by BEI Resources , NIAID , NIH . Samples from infected hosts were obtained from S . mansoni-infected mice from two sources . Female Swiss Webster mice , infected with ~200 S . mansoni cercariae ( NMRI strain ) , were provided by the Schistosome Resource Center ( S . mansoni , Strain NMRI-exposed Swiss Webster mice , NR-21963 ) . Other mice ( also Swiss Webster ) were infected in-house at the University of Pennsylvania by subcutaneous injection of specified numbers of cercariae ( typically 100–200 cercariae per mouse ) . Adult worms were perfused in saline from infected mice at 7–8 weeks post infection and counted . Infected and uninfected , negative control mice were bled either via the tail vein , the sub-mandibular and facial veins , or via cardiac puncture following euthanasia . Serum was isolated from fresh whole blood either: ( i ) by centrifugation following clotting or ( ii ) by pipetting following clotting , but without centrifugation . To obtain crude serum without centrifugation ( ii ) , we allowed the whole blood to clot at room temperature for approximately 30 minutes , aspirated the liquid component , and stored that material at -80°C until use . Freezing would not be necessary , however , when the test is conducted in proximity with the supernatant collection . Plasma was separated from whole blood with our custom-made POC separator that combines sedimentation and filtration and does not require any instrumentation [24] . In addition to primers and template , LAMP reaction mixtures ( 15 μL ) contained 9 μL OptiGene Isothermal Master Mix ISO-100 ( OptiGene , UK ) and 0 . 5 μL EvaGreen dye ( Biotium , Hayward , CA ) . Amplification was monitored using the BioRad Real Time PCR system at 63°C . Fluorescence data were collected at 2-min intervals over 90 min ( 45 cycles of 2 min each ) . The LAMP assay for detection of schistosome infection in mouse plasma and serum was adapted from that used to detect the S . mansoni Sm1-7 tandem repeat sequence in S . mansoni genomic DNA and in intermediate snail hosts [16] . This highly repetitive 121 bp sequence has been estimated to comprise more than 10% of the S . mansoni genome [28] . This sequence has previously been targeted by others using PCR [29] for the amplification of S . mansoni DNA in infected host serum . The assay's sensitivity in terms of genome equivalents was determined based on a value of 0 . 4 pg DNA per S . mansoni haploid genome , calculated from the genome size of 364 . 5 megabases [30] . We based our LAMP primers on those reported previously to yield successful amplification of Sm1-7 ( 16 ) , and only four primers were used because of the small size of the repeating target sequence ( 121 bp ) . The four LAMP primers ( and their concentrations ) are: Sm1-7-F3: 5'-GATCTGAATCCGACCAACCG-3' ( 0 . 2 μM ) ; Sm1-7-B3: 5'-AACGCCCACGCTCTCGCA-3' ( 0 . 2 μM ) ; Sm1-7-FIP: 5'-AAATCCGTCCAGTGGTTTTTTTGAAAATCGTTGTATCTCCG-3' ( 1 . 6 μM ) ; Sm1-7-BIP: 5'-CCGAAACCACTGGACGGATTTTTATTTTTAATCTAAAACAAACATC-3' ( 1 . 6 μM ) For detection of schistosome DNA in mouse serum and plasma , 20 μL serum/plasma was mixed with 20 μL lysis buffer ( DNeasy Blood & Tissue Kit , Qiagen ) and 20 μL ethanol , and injected into one of the amplification reactors . Nucleic acids bind to the Qiagen silica membrane in the presence of high chaotropic ( denaturing ) salts and low pH . Following sample injection , 50 μL of DNeasy wash buffer 1 ( AW1 ) , containing chaotropic salt and ethanol , was pipetted into the chip to remove any remaining amplification inhibitors . The silica membrane was washed with 50 μL of DNeasy wash buffer 2 containing 70% ethanol ( AW2 ) , followed by air drying for 30 s . After binding of nucleic acids to the filter and washing , 22 μL of LAMP master mix as described for the benchtop assay , which contains all the reagents necessary for LAMP , was injected into each reaction chamber through the inlet port and membrane . Subsequently , the inlet and outlet ports were sealed using transparent tape ( 3M , Scotch brand cellophane tape , St . Paul , MN ) to minimize evaporation during the amplification process . The chip was placed in a portable custom-made device that houses a heating system and USB-based microscope ( S1 Fig ) for fluorescence excitation and emission imaging . The microscope was connected to a laptop computer and the images were processed with custom MATLAB code to remove background noise and uneven illumination effects . A normalized and averaged fluorescence intensity signal for each reactor was extracted from each processed image , and from these , a typical “real-time” fluorescence amplification curve was derived . The amplicons never leave the sealed chip , decreasing the risk of cross-contamination . Additionally , the chip contains negative controls to alert against potential false positives .
The Sm1-7 LAMP primers have been tested by others and exhibit high sensitivity ( 0 . 1 fg of S . mansoni genomic DNA ) and specificity [16] . These primers have also been used to detect S . mansoni infection in intermediate host snails [16 , 17] . To examine the performance of the Sm1-7 LAMP primers with OptiGene Isothermal Master Mix , we tested the primer set against S . mansoni genomic DNA . Fig 1A depicts the real time amplification curves obtained with a bench top assay at various target DNA concentrations . Even prior to optimization , we could detect as little as 0 . 5 fg S . mansoni genomic DNA ( with a threshold time of about 40 min ) . Fig 1B depicts the threshold time ( n = 3 ) as a function of target concentration . As expected , the threshold time varies linearly with the log of the target concentration . To determine reaction specificity , we obtained the melting curves ( Fig 1C ) . When the amplification products are present , all the curves feature sharp peaks centered at the melting temperature ( Tm ) of 84°C , consistent with the calculated value ( using the Nearest-Neighbor module of OligoCalc [31] ) . In the absence of a target ( no-template control , designated as "Negative" ) , there is a broad , low peak at a lower melting temperature ( ~62°C ) , presumably due to the amplification of primer-dimers . These primer-dimers do not significantly impair our detection of the target pathogen DNA . To examine the effectiveness of our microfluidic device for detecting schistosome infection , we spiked S . mansoni genomic DNA ( gDNA ) into human plasma at different concentrations . In these experiments ( Fig 2 ) , we detected and quantified the emission intensity in real time with a portable , inexpensive USB-based mini-microscope ( S1B Fig ) . Fig 2A shows the florescent emission from three amplification reactors at various times detected with the camera ( S1 Video ) . The reactors were loaded with 50 , 5 , and 0 ( negative control ) fg S . mansoni gDNA in plasma . Similar images were captured with a smartphone camera ( S2 Fig ) , demonstrating that the USB microscope can be replaced with a smartphone . The emission intensity was digitized and quantified with a custom-written Matlab code . Fig 2B depicts the real-time fluorescence emission intensities ( arbitrary units ) detected from four reactors as functions of time . The samples consist of 50 , 5 , 0 . 5 , and 0 ( negative control ) fg of S . mansoni gDNA spiked into 20 μL plasma . The fluorescence intensity of the negative ( no target ) control remains nearly level throughout the entire detection time , indicating negligible , if any , formation of primer-dimers or other non-specific products and the absence of any significant contamination . When S . mansoni gDNA is present , the signal intensity increases from the baseline to the saturation level . The higher the target concentration , the earlier the intensity curve increases above its baseline value . Fig 2C depicts the threshold time Tt ( n = 3 ) as a function of the amount of S . mansoni gDNA . The threshold time Tt is defined as the reaction time until the fluorescent signal increases above the baseline level to ~50% of the saturation level . The figure clearly indicates that LAMP amplification of the highly repetitive Sm1-7 sequence allows the detection of far less than a single haploid S . mansoni genome equivalent ( ~400 fg ) using relatively unsophisticated equipment . The threshold time in the chip experiments was somewhat higher than in the benchtop experiments , perhaps reflecting absorption of enzymes to the amplification reactor’s surface . Indeed , when we coated the reactor’s surface with bovine serum albumin ( BSA ) to reduce non-specific binding ( S3 Fig ) , we reproduced the threshold times of the benchtop experiments ( Fig 1 ) . Despite this effect , the chip matched the benchtop sensitivity . To simplify laboratory procedures , typical benchtop assays for Sm 1–7 do not include purification , concentration , and elution steps , significantly limiting the volume of the sample that can be tested . In a typical benchtop test protocol for cell-free DNA in blood [32 , 33] , 1 μL sample is added to the reaction mix , presumably to sufficiently dilute amplification inhibitors . For the purpose of this paper , we define protocols that add 1 μl sample to the reaction mix without prior isolation and elution as "standard" procedures . By necessity , one sacrifices test sensitivity when such a small sample volume is used . In contrast , in addition to being able to carry out our test with a simple , inexpensive , portable , battery-powered , custom-made processor ( S1 Fig ) , our chip also offers the advantage of decoupling the sample and reaction volumes . To increase detection sensitivity , we can filter through the isolation membrane sample volumes much greater than the reaction chamber’s volume . To demonstrate this concentration function of our chip , we spiked S . mansoni gDNA into human plasma to simulate samples with a low target concentration ( 0 . 005 fg/μL ) . Cell-free DNA at this concentration cannot be detected when one uses a small volume such as employed in a vial-based test [32 , 33] , but it is readily detectable in our chip simply by increasing the sample volume . Fig 2D depicts amplification curves of a sample with a target concentration of 0 . 005 fg/μL S . mansoni gDNA . A sample volume of 20 μL is undetectable ( false negative ) while sample volumes of 100 and 200 μL provide robust positive signal . Clearly , a benchtop test that utilizes a 1 μL sample will fail . To further compare our chip ( Fig 3A ) with a standard benchtop test ( Fig 3B ) , we used serum from two mice seven weeks after infection with S . mansoni cercariae , at which time the parasites have developed into mature adults that produce eggs . In the benchtop test , following recommended protocols for detection of cell-free DNA in blood [32 , 33] , we used 1 μL of serum in the reaction mix . In contrast , we used 20 μL of serum on our chip . The chip detected a signal from both schistosome-infected murine samples while the benchtop assay had one false negative . Note also that the threshold times on the chip were significantly shorter than in the benchtop assay , indicative of the larger number of target molecules available for the on-chip reaction , consistent with the larger sample volume . In summary , the capability of our chips to concentrate nucleic acids from samples with exceedingly low concentrations of cell-free parasite gDNA and to remove contaminants and amplification inhibitors greatly enhances sensitivity and eliminates the requirement for separate DNA extraction and elution . The Kato-Katz technique , the most commonly used diagnostic tool for schistosome infections , depends on the presence of egg-producing adult worms beginning at 5–6 weeks post infection . As an alternative , researchers have used benchtop LAMP and other amplification protocols to detect parasite DNA originating from pre-patent schistosome infections in definitive host serum [15 , 19] and stools [15 , 18] . We tested the feasibility of detecting these pre-patent infections in serum from infected mice , using our on-chip LAMP assay and 20 μL serum volume . For comparison , a few of the samples were also tested on the benchtop using the standard protocol of adding 1 μL of serum [32 , 33] . The results of these experiments are summarized in Table 1 . Similar to previous results from LAMP benchtop assays [15 , 18 , 19] , we observe a positive Sm1-7 signal in serum from three mice as early as one week following inoculation with 200 cercariae , several weeks prior to onset of parasite egg production . Fig 4 depicts the corresponding real time amplification curves at one week post inoculation . Differences in the threshold time indicate variations in the DNA concentrations , most likely resulting from variations in the number and survival of infected parasites . As the time from infection increases , so does the DNA concentration as reflected by the declining threshold time ( see S4 Fig for additional data ) . Although we did not monitor the presence of SM1-7 in mouse blood beyond 7–8 weeks after inoculation , we expect that once the infection has reached a chronic state , the DNA concentration will achieve a steady state , consistent with reports of continued molecular detection of cell-free schistosome DNA in chronic infections [15 , 29 , 34 , 35] . We also tested serum from mice infected with lower numbers of cercariae ( 100 cercariae , average adult worm recovery = 21 ± 16 . 9 ) . As Table 1 and S4B Fig show , three of the four infected samples tested exhibit positive signals at three weeks following infection; by 7 weeks , serum from 5 out of 5 mice produced a relatively strong positive signal ( S4C Fig ) ; indeed , a mouse with only 4 adult worms recovered showed a positive signal at 7 weeks . We also repeated a few of the tests on the benchtop , using the standard 1 μL sample volume [32 , 33] ( S4D Fig ) . Not surprisingly , given the smaller sample volumes used in the benchtop experiments , the benchtop sensitivity was much inferior to that of our chip . The benchtop assay exhibited , respectively , 0/3 and 2/3 positives at one and three weeks following infection with 200 cercariae . Moreover , our chip can provide semi-quantitative data on DNA concentration . Based on the threshold time Tt and our standard curve obtained with S . mansoni gDNA ( Fig 2C ) , we estimate that the DNA concentrations of S . mansoni gDNA averaged , respectively , 0 . 025 , 0 . 04 , and 0 . 25 fg/μL one , three , and seven weeks post infection . In summary , we have demonstrated that the chip-based assay readily detects early , prepatent infection and has a sufficient dynamic range to detect and monitor DNA concentrations in early and most likely in chronic infections as well . Moreover , the semi-quantitative nature of the assay should allow one to monitor disease progression and perhaps the effectiveness of therapy [29] , particularly in mass drug administration programs . In the tests described earlier , we used the standard laboratory procedure of centrifugation to separate serum from whole blood . Centrifugation is , however , incompatible with point-of-care diagnostics . To eliminate the need for centrifugation , we have recently developed a unique , inexpensive plasma separator [24] that combines sedimentation and filtration to separate relatively large volumes of plasma from whole blood without any instrumentation . In the first set of the experiments reported in this section , we used our separator to separate plasma from whole blood prior to introducing the sample into the chip . Alternatively , one can aspirate the liquid component from whole blood that has been allowed to clot ( without centrifugation ) to provide crude serum in a manner consistent with a point-of-care approach . We used plasma and serum separated with these two approaches in our on-chip LAMP assay , and obtained results similar to the ones described in the earlier sections . For example , Fig 5 depicts amplification curves of mouse samples ( obtained 7 weeks after infection with 200 cercariae ) . The figure shows that S . mansoni DNA can be detected as readily in either plasma or serum ( Tt ± s . d . = 61 ± 10 , n = 7 ) obtained with these POC methods as in plasma or serum separated by centrifugation ( Tt ± s . d . = 63 ± 16 , n = 7 ) .
A major challenge in treatment , control , and monitoring of neglected tropical diseases is to develop highly sensitive and accurate diagnostic methods as adjuncts or replacements for current labor-intensive and unreliable procedures [1 , 2] . Sensitive and specific methods for detecting early , pre-patent schistosome infections may also help prevent irreversible pathological reactions induced by eggs , and might help monitor and possibly determine the basis for treatment failures ( praziquantel is relatively ineffective against immature worms ) . Since schistosomiasis is particularly prevalent in low-resource settings , lacking appropriate laboratory facilities , inexpensive tools for point-of-care diagnostics that do not require centralized laboratory support are highly desirable . It is not surprising , therefore , that various groups are developing and evaluating point-of-care diagnostic tools that rely on circulating antigens [36 , 37] and electrochemical detection in a sandwich assay format of RNA derived from parasite eggs in urine [38] . All these assays suffer , however , from low sensitivity and occasionally lack of specificity . Molecular diagnostic tests are widely accepted as the gold standard since they provide high sensitivity and specificity , are capable of distinguishing between various species and strains of the pathogen , and are relatively easy to adapt to new threats . Since conventional molecular diagnostics requires elaborate sample processing , expensive equipment , and highly trained personnel , molecular diagnostics has been mostly confined to centralized laboratories . To overcome some of the shortcomings of traditional molecular diagnostic techniques , we have developed a platform that simplifies sample processing and flow control and enables one to carry out all unit operations at the point of contact without reliance on expensive laboratory equipment . Although our technology would benefit from further improvements to facilitate automated , seamless operation , it can be deployed in its current state at the point of care to satisfy urgent needs . Once fully developed , this technology could be implemented for diagnostics and epidemiological monitoring in endemic settings , where sensitivity , reliability , cost , and convenience are paramount [39] . Parasitological diagnosis methods such as the Kato-Katz method for detecting eggs in stool are useful , but suffer from poor sensitivity and reliability [3 , 4] , and are laborious . The POC circulating cathodic antigen ( CCA ) test on urine samples has proven to be quite useful , but suffers from reduced sensitivity in areas of lower endemicity as well as other challenges [12] . Molecular tests offer outstanding sensitivity and specificity , but are seldom used in the field due to the need for expensive equipment and highly-trained personnel [39] . In contrast , our LAMP-based system would require essentially no equipment or special training . Finally , the estimate for per-person costs of current diagnostic tests ranges from ~US$7 . 00 for a single Kato-Katz test , US$17 . 50 for a more reliable triplicate Kato-Katz test , and ~US$7 . 25 for the CCA test [40] . Although it is not yet possible to estimate the cost of our chip , the materials and reagents are not particularly expensive , and we would expect the per-person costs to be competitive with the other less effective tests . Fig 6 details the sequence of operations that are needed to detect cell-free , parasite nucleic acids in blood samples . Although LAMP is reported to be less susceptible than PCR to inhibition by compounds in whole blood such as heme-protein complexes [41] , it may still be adversely impacted by these inhibitors [42] . Therefore , the first step includes separation of plasma from whole blood , which can be accomplished using our POC plasma separator with a pipette or dropper and without a centrifuge . A single drop of blood from a finger stick should be sufficient . Alternatively , as we have shown ( Fig 5 ) , serum separated from whole blood without centrifugation can also be used directly in this system . The plasma/serum is then mixed with nucleic acid binding agents and filtered through our unique , multifunctional chip that combines nucleic acid isolation , concentration , and purification . During this process , the nucleic acids bind to the isolation membrane that is installed at the inlet to our amplification reactor and the filtrate is discharged to waste . The nucleic acid isolation is carried out as part of the sample introduction into the chip , and does not require a separate operation ( i . e . , a spin column ) nor elution . This is followed with wash steps , which require only pipetting operations and no instrumentation . Next , the reaction chamber is filled with water . In the experiments described here , we pipetted the various reagents needed for LAMP amplification into the reactor . In actual field use , we expect the reaction mixes to be pre-stored in the reaction chambers . Sample application and removal could be accomplished with an inexpensive , disposable calibrated dropper as opposed to syringes or pipette tips . To prevent the sample and wash solutions from washing away the reagents , we will encapsulate the reagents in paraffin . In separate work , we have demonstrated that this storage method provides at least six months ( and likely much more ) of refrigeration-free shelf life without any deterioration in the reagents’ effectiveness [43] . Once the chip is heated to its operating temperature , the paraffin melts , moves out of the way , the reagents are hydrated , and are available for the amplification reaction . The stored reagents include intercalating dye that allows us to monitor the progress of the amplification process in real time . Various reviews ( i . e . , [44] ) have emphasized the need for these types of advances in sample preparation , processing , and detection . In this paper , for heating and fluorescent emission detection , we used an inexpensive , home-made , battery-powered processor ( S1 Fig ) . In separate work [26] , we demonstrated that heating can be provided with an exothermic reaction and temperature can be regulated with phase change material , eliminating the need for electric power . Similarly , as we have shown ( S2 Fig ) , a smartphone can be deployed to monitor fluorescent emission intensity [45] , greatly reducing the cost of the system . Since smartphones are ubiquitous even in resource-poor countries , one can assume their availability . The core of our platform is the multifunctional amplification reactor that combines a number of unit operations such as nucleic acid isolation , concentration , purification , and amplification that are typically carried out in the laboratory separately . An added benefit of our method is that the sample volume is decoupled from the amplification reaction volume . In other words , we can use sample volumes that are much greater than the reaction volume , improving effective assay sensitivity . Indeed , in this work , we have demonstrated sensitivity of ~25 ag/μL ( 2 . 5 x 10−17 g/μL ) for schistosome gDNA ( based on detection of 0 . 5 fg S . mansoni DNA in a 20 μL sample ) . This sensitivity is two orders of magnitude higher than that of benchtop PCR protocols without sample concentration ( which are limited to small sample volumes ) and many million-fold better than methods that do not use amplification . This high sensitivity facilitates early detection , within a few days of infection in asymptomatic patients , well prior to the production of eggs . The system can also be used to monitor infection progression , chronic state , and perhaps the effectiveness of therapies [12 , 30] . Indeed , various groups have demonstrated that cell-free schistosome DNA is detectable in chronic human infections [15 , 29 , 34 , 35] and , in animal models , at >100 days post infection [15 , 29 , 35] . This high sensitivity also suggests the feasibility , which we did not explore here , of detecting cell-free DNA in other body fluids , such as saliva and urine [5] , which are less intrusive to collect than blood . Furthermore , our method can readily be modified for other parasitic helminth infections and to detect RNA and micro RNAs , with further improvements in sensitivity and potential monitoring of infection stage or severity . Simple benchtop assays [32 , 33] , unlike our chip , do not incorporate nucleic acid isolation and elution , and the total volume of the sample and the reaction mix are restricted , typically to 25 μL . These characteristics limit one to small sample volumes ( typically , one or a few μL ) to assure sufficient dilution of inhibitors and contaminants and appropriate concentrations of the various ingredients needed for the amplification process . In contrast , in our chip , only the volume of the reaction mix is fixed . The sample volume is decoupled from the reaction volume , allowing the use of relatively large samples without increasing the concentration of inhibitors that adversely affect amplification efficiency . Since nucleic acid isolation is part of the sample introduction process in our chip , and no separate elution is required , our chip provides better performance than simple benchtop assays , without any added complexity . Since amplicons never leave the sealed chip , cross contamination is unlikely to be a significant problem , and can be further monitored with appropriate negative controls built into the chip . As we have demonstrated here , our chip can house an array of amplification reactors , all of which can be monitored concurrently with a single camera . This enables us to use one or more reactors for positive control and calibration . For example , in a positive control reactor , we could store a known quantity of target DNA and monitor its amplification to verify that the amplification process operates correctly as well as obtain the threshold times of known quantities of DNA to construct a calibration line . Multiple reactors can also be used to detect concurrent infections and possibly drug-resistant parasites once appropriate markers are defined . | Schistosomes infect hundreds of millions of people worldwide , with devastating effects on human health and economic development . One of the major challenges in treatment and control of these diseases is the accurate diagnosis of infection and monitoring of transmission dynamics . Current parasitological and serological diagnostic tests often lack sensitivity , accuracy , and reliability; more sensitive and specific molecular methods require infrastructure typically unavailable in resource-limited settings . Here , we have detected cell-free parasitic helminth DNA in serum and plasma from schistosome-infected mice using LAMP , an isothermal amplification technique , in a simple , hand-held , custom-made , field-ready microfluidic device we have developed . Schistosome DNA is detectable as early as one week following infection , long before the worms have matured and begun producing eggs . The device is small , entirely self-contained and disposable , and is readily adaptable to rapid and simple point-of-contact ( POC ) detection of infection . The entire process from sample collection to diagnosis can be carried out on-site , without any laboratory equipment . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Molecular Detection of Schistosome Infections with a Disposable Microfluidic Cassette |
The introduction ten years ago of RNA interference ( RNAi ) as a tool for molecular exploration in Trypanosoma brucei has led to a surge in our understanding of the pathogenesis and biology of this human parasite . In particular , a genome-wide RNAi screen has recently been combined with next-generation Illumina sequencing to expose catalogues of genes associated with loss of fitness in distinct developmental stages . At present , this technology is restricted to RNAi-positive protozoan parasites , which excludes T . cruzi , Leishmania major , and Plasmodium falciparum . Therefore , elucidating the mechanism of RNAi and identifying the essential components of the pathway is fundamental for improving RNAi efficiency in T . brucei and for transferring the RNAi tool to RNAi-deficient pathogens . Here we used comparative genomics of RNAi-positive and -negative trypanosomatid protozoans to identify the repertoire of factors in T . brucei . In addition to the previously characterized Argonaute 1 ( AGO1 ) protein and the cytoplasmic and nuclear Dicers , TbDCL1 and TbDCL2 , respectively , we identified the RNA Interference Factors 4 and 5 ( TbRIF4 and TbRIF5 ) . TbRIF4 is a 3′-5′ exonuclease of the DnaQ superfamily and plays a critical role in the conversion of duplex siRNAs to the single-stranded form , thus generating a TbAGO1-siRNA complex required for target-specific cleavage . TbRIF5 is essential for cytoplasmic RNAi and appears to act as a TbDCL1 cofactor . The availability of the core RNAi machinery in T . brucei provides a platform to gain mechanistic insights in this ancient eukaryote and to identify the minimal set of components required to reconstitute RNAi in RNAi-deficient parasites .
RNA interference ( RNAi ) was first described in 1998 and within a short period of time tremendously facilitated the analysis of gene function , especially in organisms where classical genetic approaches are not available . This is particularly evident in the human pathogen Trypanosoma brucei , where RNAi has become a primary tool to interrogate its biology aided by the availability of an inducible and heritable system [1] , [2] . The effectiveness of RNAi in T . brucei is documented by over 500 publications in the past ten years and the availability of the genome sequence has opened up the possibility for genome-wide RNAi screens [1] . In particular , the very recently introduced RIT-Seq method ( RNA Interference Target Sequencing ) took advantage of the power of genome-wide RNAi screens and combined it with the strength and depth of next-generation Illumina sequencing [3] . This strategy provided the scientific community with a catalogue of genes whose knock-down is detrimental to the parasite under a variety of developmental conditions and is likely to find numerous applications in RNAi-positive parasites . Although RNAi has flourished immensely in T . brucei , as highlighted by the above brief synopsis , it was rather disappointing to recognize both experimentally and at the genome sequence level that other protozoan parasites with a major impact on mankind , including T . cruzi [4] , Leishmania major , L . donovani [5] and Plasmodium falciparum [6] , were lacking a functional RNAi pathway . On the other hand , the genome sequence of L . ( Viannia ) braziliensis [7] predicted the existence of the RNAi pathway in this leishmania subgenus , and this prediction was recently experimentally validated [8] . The realization that T . cruzi and old world leishmanias are RNAi-negative was quite unfortunate , but it has been argued that once the core genes involved in T . brucei RNAi are identified it might be possible to try to reconstruct the pathway in RNAi-negative trypanosomatids [1] . Indeed , the recent success of David Bartel's group in reconstructing the RNAi pathway in S . cerevisiae [9] provides a proof of principle and informs us that it is realistic to try to achieve the same goal in T . cruzi or old world Leishmania sp . Comparison of RNAi mechanisms in different model organisms [10] suggests that the common , minimal machinery consisted of firstly , a Dicer endonuclease of the RNase III family that processes long dsRNAs into duplex small interfering RNAs ( siRNAs ) with characteristic two-nucleotide 3′ overhangs; secondly , an Argonaute ( AGO ) “slicer” endonuclease that in a complex with a single-stranded ( ss ) guide siRNA ( as opposed to the passenger strand that is discarded ) cleaves target mRNA; and thirdly , a Dicer cofactor with a dsRNA-binding domain that facilitates siRNA biogenesis and loading into AGO . The AGO-siRNA complex forms the catalytic engine of the RNA-induced silencing complex or RISC [11] . An additional factor found only in some RNAi-proficient organisms is an RNA-dependent RNA polymerase [12] that creates secondary siRNAs to amplify the initial RNAi response . The players identified so far in T . brucei RNAi are a single AGO slicer , TbAGO1 [13] , [14] , and two Dicer homologs , TbDCL1 [15] and TbDCL2 [16] . This set of genes is also present in L . ( V ) braziliensis [8] , although their precise function needs to be experimentally addressed . In T . brucei we have provided evidence that the nuclear Dicer TbDCL2 is the first line of defence against dsRNAs originating from retroposons ( ingi and SLACS ) and satellite-like repeats ( CIR147 ) , whereas in the cytoplasm TbDCL1 processes intermediate-sized dsRNA molecules generated by TbDCL2 , as well as dsRNA that may escape from the nucleus or enter the cytoplasm from the exterior milieu [15] , [16] . The RISC loading mechanism in T . brucei is not known; no RNAi-specific dsRNA-binding protein has been identified yet [17] . In Drosophila [18] , [19] and Neurospora [20] it has been shown that mutations of AGO slicer catalytic residues prevent cleavage of both the siRNA passenger strand and the target RNA , and siRNAs associated with the corresponding AGO are double-stranded . In contrast , mutations that affect target RNA degradation by TbAGO1 in vivo do not affect the maturation of siRNAs from duplex to single-stranded form and TbAGO1 is associated with single-stranded siRNAs [21] , [22] . The above observations hinted at an additional activity involved in the formation of the TbAGO1/ss-siRNA complex . To test this hypothesis we compared the genomes of RNAi-proficient trypanosomatid protozoa with those that have lost the RNAi machinery . In addition to the three characterized RNAi proteins in T . brucei , we identified two novel factors , both of which we have shown to be active in RNAi . RNA Interference Factor 5 ( TbRIF5 ) is required for cytoplasmic , but not nuclear RNAi , and appears to act in conjunction with TbDCL1 . TbRIF4 contains a C-terminal domain related to 3′-5′ exonucleases of the DnaQ superfamily . In the absence of TbRIF4 protein or function , duplex siRNAs accumulate , and are not associated with TbAGO1 . Human AGO2 can replace both TbRIF4 and TbAGO1 functions , suggesting that the slicing function of human AGO2 in RISC maturation is replaced in trypanosomes by the TbRIF4 exonuclease .
In order to identify the core RNAi genes we compared the genomes of RNAi-deficient ( T . cruzi and L . major ) and RNAi-proficient ( T . brucei , T . congolense and L . braziliensis ) trypanosomatids and selected ORFs that are exclusively present in all the RNAi-positive organisms , but absent from the RNAi-negative parasites . This analysis only returned five genes . In addition to TbAGO1 , TbDCL1 and TbDCL2 , the search identified two putative RNAi factors ( Tb927 . 10 . 8880 and Tb927 . 10 . 10730 in T . brucei ) , which we named T . brucei RNA Interference Factor 4 and 5 ( TbRIF4 , 490 amino acids long , and TbRIF5 , 1509 amino acids long ) . At the primary sequence level the C-terminus of TbRIF4 contains a domain that resembles the DnaQ superfamily of 3′-5′ exonucleases ( Figure 1 ) . This structural feature is shared with Neurospora crassa QIP ( QDE2 Interacting Protein ) , which functions in removal of the passenger strand siRNA fragments , after it has been cleaved by the AGO slicer NcQDE2 [20] . This protein family is characterized by three motifs encompassing the active site residues DEDDh that are present in TbRIF4 , as noted in a bioinformatic screen by Trudeau and colleagues [23] . The active site residues are conserved in all RIF4 homologs identified in RNAi-proficient trypanosomatids ( Figure S1A ) and using the alignment interface of SWISS-MODEL [24] , structural predictions ( Figure S1B ) revealed significant similarity with the exonuclease domain of DnaQ [25] . Besides the exonuclease motifs , TbRIF4 and NcQIP do not share any other sequence similarity . TbRIF5 is specific to the trypanosomatid lineage and bioinformatic analysis did not reveal any known domains ( Figure S2 ) . TbRIF4 is essential for RNAi in both the cytoplasm and the nucleus . First , rif4−/− cells ( Figure S3A ) had a much reduced ability to respond to transfection with α-tubulin dsRNA ( Table 1 , Figure 2A ) , an assay that monitors the cytoplasmic RNAi response and in wild-type cells results in degradation of α-tubulin mRNA . Second , in rif4−/− cells the levels of long and heterogeneous repeat-derived ( CIR147 ) transcripts were substantially increased as compared to wild-type cells ( Figure 2B , lane 2 , and Figure 2C , lane 3 ) , a previously-reported hallmark of nuclear RNAi deficiency [16] . A TbRIF4-GFP cassette , integrated into the tubulin locus of rif4−/− cells ( rif4c cell line , Figure S3A ) , restored TbRIF4 levels and complemented the mutant phenotypes , leading to wild-type response to dsRNA transfection ( Table 1 , Figure 2A ) and restoration of long retroposon- and repeat-derived transcript levels ( Figure 2B , lane 3 , and Figure 2C , lane 4 ) . Consistent with the involvement of TbRIF4 in both the nuclear and cytoplasmic arms of the RNAi pathway , the TbRIF4-GFP fusion protein was distributed uniformly between the nucleus and cytoplasm ( Figure 2D ) . TbRIF5 , on the other hand , only appears to be required for the cytoplasmic branch of the RNAi pathway . rif5−/− cells ( Figure S3B ) transfected with double-stranded α-tubulin RNA showed a severe defect in the cytoplasmic RNAi response ( Table 1 ) . However , levels of CIR147 transcripts were not increased by TbRIF5 ablation ( Figure 2B , lane 7 ) , suggesting that nuclear RNAi was unaffected . A TbRIF5-BB2-HA cassette , integrated into the tubulin locus of rif5−/− cells ( rif5c cell line , Figure S3B ) , complemented the cytoplasmic RNAi defect ( Table 1 ) . Due to the low level of expression of the epitope-tagged protein , the cellular localization of TbRIF5 could not be ascertained by IFA . However , cell fractionation experiments indicated that most of the protein partitioned in the cytoplasm ( data not shown ) . To determine the stage in the RNAi pathway at which TbRIF4 and TbRIF5 act , we first asked whether siRNAs were produced in their absence . Northern blot analysis revealed that in rif4−/− cells ( Figure 3A , lane 2 ) ingi- and CIR147-derived siRNAs accumulated to levels greater than wild-type ( lane 1 ) or rif4c ( lane 3 ) trypanosomes , although their size distribution was more heterogeneous than in wild-type cells , possibly due to nuclease nibbling as these siRNAs are not associated with TbAGO1 ( see below ) . An in vitro dicing assay programmed with long dsRNA [16] indicated that siRNA processing was as active in rif4−/− as in wild-type cell extracts ( Figure 3B ) , suggesting that TbRIF4 acts downstream of the dicing step . siRNAs also accumulated in rif5−/− parasites . However , as in a dcl1−/− cell line [16] , while siRNAs derived from the CIR147 tandem repeats accumulated in rif5−/− cells to a level comparable to wild-type or rif5c trypanosomes ( Figure 3A ) , SLACS retroposon siRNA levels were reduced in the mutant cell line ( lane 5 ) , but were restored by complementation ( lane 6 ) . A similar phenotype was observed for the ingi-derived siRNAs ( data not shown ) . These data suggested a link to the cytoplasmic Dicer and the following experiments provided corroborating evidence . Firstly , the in vitro dicing assay showed that the siRNAs generated by a rif5−/− cell extract were the same size as those created by a dcl1−/− extract , which , as has been shown previously by our group , are one nucleotide smaller than those generated by a dcl2−/− extract ( [16]; Figure 3B ) . Secondly , we co-immunoprecipitated the two proteins ( Figure S4 ) and found that a proportion of TbDCL1 is in a complex with TbRIF5 . We expressed TbDCL1-BB2-FLAG and TbRIF5-BB2-HA in the same cell line . Immunoprecipitation of TbDCL1 ( top panel ) or TbRIF5 ( bottom panel ) was carried out using anti-FLAG or anti-HA antibodies , respectively , and the fate of both TbDCL1 and TbRIF5 was monitored by Western blotting for the common BB2 epitope . It should be noted that TbDCL1-BB2-FLAG is at least two to three times more abundant than TbRIF5-BB2-HA in this cell line ( see input lane in Figure S4 ) . When TbDCL1 was pulled down , the majority of TbRIF5 was found in the immunoprecipitated fraction . In contrast , when TbRIF5 was immunoprecipitated , approximately 50% of TbDCL1 was detected in the precipitate , in agreement with the lower abundance of TbRIF5 ( the identity of the immunoprecipitated proteins was verified by Western blotting with HA monoclonal antibody for TbRIF5 , or FLAG monoclonal antibody for TbDCL1; data not shown ) . The interaction of these two factors was further supported by: i . co-fractionation on a Superdex-200 sizing column; ii . mass-spectrometry of FLAG-immunoprecipitated TbDCL1 , which revealed several peptides derived from TbRIF5 as well as TbDCL1 ( data not shown ) . Thus , we concluded that TbDCL1 and TbRIF5 interact either directly or indirectly . An essential step in the RNAi pathway is the formation of the RNA-induced silencing complex or RISC [26] , consisting of an AGO “slicer” , an endonuclease of the RNase H family , in a complex with an siRNA , which guides target cleavage by AGO . Biochemical studies have shown that human and Drosophila AGO2 are initially loaded with duplex siRNAs and , following passenger strand nicking by AGO , the resulting fragments dissociate [27] . Specific factors required for this transition have been identified , such as the multimeric endonuclease C3PO ( Component 3 Promoter of RISC ) in Drosophila and humans [28] , [29] or the Neurospora 3′-5′ exonuclease QIP [20] , [30] , [31] . Thus , to test whether TbRIF4 and/or TbRIF5 were involved in RISC formation , we next asked whether the rif4−/−- and rif5−/−-derived siRNAs were single- or double-stranded . We purified RNA from wild-type , rif4−/− , rif4c , rif5−/− , and rif5c S100 extracts and analyzed the samples on a native polyacrylamide gel with or without denaturation at 100°C ( Figure 3C ) . A radiolabelled double-stranded synthetic siRNA was used as a control ( lanes 7 and 8 ) . Following Northern blot hybridization with a CIR147 probe , most siRNAs from wild-type cells appeared single-stranded ( lane 1 , see [22] ) . A similar result was obtained with siRNAs from rif5−/− and rif5c cells ( lanes 11 and 13 ) . In contrast , by this analysis non-denatured rif4−/−-derived siRNAs displayed the mobility expected for duplex molecules ( compare lanes 3 and 7 ) , but were converted to ss-siRNAs upon denaturation ( lane 4 ) , whereas siRNAs from the rif4c sample again reproduced the single-stranded nature present in wild-type cells ( lane 5 ) . To provide additional evidence that siRNAs isolated from rif4−/− trypanosomes were double-stranded , we incubated the RNA samples at different temperatures ( 20° to 100°C ) prior to loading them on a native gel ( Figure S5A ) . Ingi siRNA strands dissociated between 60°C and 65°C , consistent with a duplex structure . Lastly , we assessed the sensitivity of siRNAs isolated from wild-type and rif4−/− cells to treatment with RNase T1 and RNase A , which preferentially cleave ss RNA in high salt ( Figure S5B ) . Wild-type CIR147 siRNAs were mostly degraded by the ribonucleases ( lane 2 ) , with trace amounts resistant to digestion , but siRNAs derived from rif4−/− cells were largely resistant to degradation ( lane 5 ) and were only cleaved efficiently following denaturation ( lane 6 ) . The above results strongly indicated that rif4−/− siRNAs are predominantly in duplex form . We think it is unlikely that the duplex siRNAs are an artefact of the RNA isolation procedure , since very little duplex siRNA can be observed in wild-type samples prepared under the same conditions ( Figure 3C , lane 1 ) . We showed previously by Northern blotting [32] that both sense and antisense siRNAs from the retroposons ingi and SLACS accumulate in T . brucei . Recent deep-sequencing of siRNAs associated with TbAGO1 confirmed these early observations and extended them to include CIR147 ( unpublished observations ) . Although wild-type siRNAs have the potential to anneal to form duplex structures , they do not appear to do so under the experimental conditions used . Next we asked whether siRNAs in rif4−/− cells were associated with TbAGO1 . To this end , we introduced TAP-tagged TbAGO1 , which fully complements TbAGO1 deficiency [14] , into rif4−/− cells . It should be noted that the level of TAP-tagged TbAGO1 was much lower in rif4−/− cells as compared to wild-type trypanosomes ( Figure 4A , top panel , compare lanes 4 and 8 ) . This dramatic diminution of TbAGO1 levels was also observed for endogenous TbAGO1 in rif4−/− cells ( Figure 4B , middle panel , compare lanes 1 and 2 ) , but returned to normal upon complementation ( lane 3 ) . By semi-quantitative Western blot analysis we estimated that the level of endogenous TbAGO1 was reduced approximately 20-fold in rif4−/− cells ( data not shown ) . TAP-tagged TbAGO1 was immunoprecipitated from wild-type and rif4−/− S100 extracts and varying amounts from the different samples were analysed by Western ( Figure 4A , top panel ) or Northern blotting ( middle panel ) . This experiment showed that when roughly similar amounts of TbAGO1 protein were loaded ( compare 90% of the immunoprecipitated material from rif4−/− cells with 9% or even 1% of the material from the wild-type sample ) , no siRNAs could be detected in the pellet from the rif4−/− sample ( lanes 6–8 ) , although these molecules were clearly visible in the wild-type immunoprecipitate ( lanes 2–4 ) . We concluded that the majority of CIR147 siRNAs in rif4−/− cells were not associated with TbAGO1 . Our experiments so far suggested that TbRIF4 plays a role in the conversion of double- to single-stranded siRNAs . To test whether the predicted 3′-5′ exonuclease domain in the C-terminus of TbRIF4 was involved in this process , we generated recombinant GST-TbRIF4 fusion proteins ( Figure S6A ) and tested its activity in vitro . Incubation of 25-nt duplex α-tubulin siRNA-315 [16] , labelled at the 5′ end of the sense strand , with increasing amounts of GST-TbRIF4 produced a distinct ladder of fragments that progressively decreased in size with increasing protein concentration ( Figure 5A , lanes 4–11 ) and with time ( Figure S6B , lanes 2–7 ) . Mutation of conserved exonuclease active site residue H472 to alanine drastically reduced the detected exonuclease activity ( Figure 5A , lane 12 and Figure S6B , lane 8 ) . With the above substrate we observed accumulation of a 7-nt fragment , but this was not the case when the antisense strand of the duplex substrate was labelled ( Figure S6C ) , indicating that digestion by TbRIF4 is somewhat affected by the substrate sequence . Nevertheless , these results suggested that in vitro both strands are accessible to TbRIF4 digestion and that the enzyme has a distributive rather than processive mode of action . To further characterize TbRIF4 activity , we first used substrates with no , single or double 3′ nucleotide overhang ( the label was placed at the 5′ end of the sense strand ) and found that they were also processed , suggesting that the 2-nt 3′ extension is not important for substrate recognition by recombinant TbRIF4 in vitro ( Figure S6D ) , consistent with a distributive mode of action . Secondly , we showed that recombinant TbRIF4 acted preferentially on duplex as opposed to single-stranded RNA ( Figure S6E ) and that catalysis required magnesium ions ( Figure S6F ) and a free 3′ end ( Figure S6G ) , as processing of the RNA substrate was inhibited by EDTA or by blocking the 3′ end with a 2′ , 3′-dideoxycytosine , respectively . Thirdly , we asked whether GST-TbRIF4 can convert the RNA duplex into ss-RNA . Recombinant TbRIF4 does not exhibit clear strand selectivity ( Figure S6C ) , thus either strand can be degraded and any released ssRNA will base-pair with available complementary products to form dsRNA . To circumvent this problem , we carried out an in vitro assay where one strand of the double-stranded RNA substrate was labelled at the 5′ end and the 3′ end of the same strand was blocked by dideoxy-C to prevent it from being a substrate for TbRIF4 ( Figure 5B ) . This mimics the effect of a bound protein protecting one end of the duplex , a likely in vivo scenario . The products of this reaction were separated on a native gel , showing that many of the molecules had been converted to the single-stranded form . Thus , TbRIF4 has an intrinsic 3′-5′ exonuclease activity in vitro , which acts preferentially on double-stranded RNA , and its activity is dependent on a predicted active site residue . Next we assessed whether TbRIF4's conserved DnaQ exonuclease family DEDDh residues ( Figure 1 ) were essential for in vivo function by mutating each amino acid to alanine in the context of the TbRIF4-GFP complementation cassette , and introducing the resulting mutants into rif4−/− cells . With the exception of mutant E296A ( Figure 4B , lane 5 ) , the other proteins ( D294A , D392A , H472A and D477A , lanes 4 , 6 , 7 and 8 ) were expressed close to the level of wild-type TbRIF4-GFP ( lane 3 ) . Upon transfection of 5 µg α-tubulin dsRNA , which in wild-type cells results in approximately 84% degradation of target mRNA , in cells lacking TbRIF4 or expressing mutants D294A , D392A or H472A there was very low or no detectable degradation of α-tubulin mRNA ( Figure 2A ) . In contrast , transfection of dsRNA into cells expressing mutant D477A showed that at each dsRNA concentration tested , the cytoplasmic RNAi efficiency was reduced about 50% as compared to the wild-type level . Thus , it appeared that the D477 residue is not as important as the other predicted catalytic residues for TbRIF4 activity in vivo . However , our results indicated that mutation of residues D294 , D392 and H472 severely impaired the ability of cells to respond to dsRNA transfection . Similarly , nuclear RNAi as measured by RT-PCR of CIR147 repeats was partially restored in cells expressing mutant D477A ( Figure 2C , lane 8 ) , in agreement with the results of Figure 2A , whereas cells expressing mutants D294A , D392A and H472A appeared significantly impaired in nuclear RNAi ( lanes 5–7 ) . Finally , expression of active-site TbRIF4 mutants D294A , D392A , H472A and D477A failed to restore TbAGO1 to wild-type levels ( Figure 4B , middle panel , lanes 4–8 ) , although the amount of TbAGO1 was increased as compared to rif4−/− cells . The finding that siRNAs were not detectably associated with TbAGO1 in rif4−/− cells could indicate a physical interaction between the two proteins . To test this hypothesis , we performed immunoprecipitations . Pull-downs of either wild-type or H472A TbRIF4-GFP revealed the presence of TbAGO1 in the immunoprecipitates ( Figure 6A ) , with more TbAGO1 in the H472A TbRIF4-GFP sample compared to the wild-type sample ( compare lanes 3 and 6 , bottom panel ) . We next asked whether siRNAs could be immunoprecipitated with wild-type or H472A TbRIF4-GFP ( Figure 6B ) . In cells expressing wild-type TbRIF4-GFP , CIR147 siRNAs were below the limit of detection ( lane 2 ) . In contrast , a fraction of CIR147 siRNAs were found in the immunoprecipitated material after pull-down of H472A TbRIF4-GFP ( lane 4 ) . These results indicated that a proportion of a catalytically inactive TbRIF4 was in a complex containing TbAGO1 and siRNAs . We take these results as supportive evidence that TbRIF4 and TbAGO1 interact in vivo , either directly or indirectly , and that the interaction can be stabilized by mutation of the TbRIF4 exonuclease active site , although the significance of the latter observation remains to be investigated further . Our past and present data indicated that the transition from ds- to ss-siRNAs in T . brucei is uncoupled from TbAGO1 slicer function and is carried out by the TbRIF4 exonuclease . We previously reported that human AGO2 slicer ( HsAGO2 ) , but not HsAGO1 , which lacks target transcript slicing activity , can partially complement TbAGO1 null cells [17] . Since HsAGO2 slicing activity is involved in the cleavage of both the passenger strand of duplex siRNAs and the target RNA [27] , [33] , we hypothesized that the restoration of the RNAi response by HsAGO2 may occur by a TbRIF4-independent mechanism . Thus , HA-FLAG-tagged HsAGO2 was introduced into rif4−/−:ago1−/− cells . Expression of HsAGO2 resulted in a partial cytoplasmic RNAi response measured by transfection with dsRNA ( Figure 2A ) , as previously described for cells expressing HsAGO2 and TbRIF4 , but nuclear RNAi was not restored ( Figure 2C , lane 9 ) , as evidenced by the accumulation of CIR147 transcripts . To monitor the physical state of the siRNAs , HsAGO2 was immunoprecipitated and the associated siRNAs were analyzed by native gel electrophoresis ( Figure 6C ) . In agreement with the partial RNAi response , a fraction of the siRNAs was single-stranded ( lane 3 ) . These results indicated that in trypanosomes expressing HsAGO2 maturation of siRNAs from duplex- to ss-form , takes place independently of TbRIF4 and underscored a model where TbRIF4 in a complex with TbAGO1 degrades the siRNA passenger strand , thus substituting for HsAGO2 passenger cleavage activity in ss-siRNA biogenesis in T . brucei .
The availability of genome sequences from trypanosomatids that are either RNAi proficient or not , provided us with an opportunity to tease out genes specific to the RNAi mechanism . Quite surprisingly , the comparative genomics analysis revealed a limited set of five genes: the previously characterized TbAGO1 , TbDCL1 and TbDCL2 , as well two additional candidates , TbRIF4 and TbRIF5 . Here , we showed that both TbRIF4 and TbRIF5 are essential for RNAi , and carry out distinct functions . TbRIF4 is implicated in the formation of a stable TbAGO1/guide siRNA complex through conversion of duplex siRNAs to their single-stranded form , whereas TbRIF5's function is less clearly defined due to a lack of recognizable domains , but this factor appears to be working in concert with TbDCL1 . The latter conclusion is based on the observations that TbRIF5's ablation resulted in cytoplasmic RNAi deficiency and that the protein associated with TbDCL1 , the cytoplasmic Dicer . We therefore propose a role for TbRIF5 as a cofactor in TbDCL1-mediated processing of long dsRNA . It is unlikely that TbRIF5 is involved in nuclear RNAi as CIR147 siRNAs , which are generated by TbDCL2 , are not affected by TbRIF5 ablation , and we have recently shown that RNAi of small nucleolar RNAs similarly requires TbDCL2 , but is independent of TbDCL1 [34] . The specific reduction of SLACS and ingi siRNAs in TbRIF5 null cells points to the possibility that TbRIF5 is required at some point in the biogenesis of such siRNAs in the cytoplasm , either by stimulating TbDCL1 cleavage activity and/or by functioning as a bridge between TbDCL1 , the siRNAs and TbAGO1 . RNase III enzymes , such as Dicer and Drosha , in general require the assistance of dsRBP cofactors [12] . For instance , in Drosophila R2D2 forms a complex with DCR-2 , enhances the incorporation of siRNAs into RISC [35] and acts as a sensor for strand asymmetry of siRNAs [36] . In human cells the interaction of DGCR8 with Drosha is essential for excision of pre-miRNAs from primary miRNA containing transcripts [37] . Although TbRIF5 lacks a recognizable dsRNA binding domain , it may be that such a motif exists but is highly divergent . TbRIF4 contains a predicted 3′-5′ exonuclease domain . In vivo TbRIF4 knockout results in both nuclear and cytoplasmic RNAi deficiency and in the accumulation of duplex siRNAs that are not detectably associated with TbAGO1 by co-immunoprecipitations . As predicted from its domain structure , TbRIF4-GST is endowed with in vitro 3′-5′ exonuclease activity , and processes duplex siRNAs . Complementation of rif4−/− cells with TbRIF4-GFP carrying mutations of catalytic residues D294A , D392A and H472A , which also impair exonuclease activity in vitro ( data not shown ) , did not restore RNAi competency of TbRIF4 null cells , indicating that TbRIF4 catalytic activity is required for RNAi function . TbRIF4 and TbAGO1 interact , either directly or indirectly , and form a complex that can be recovered in greater amounts from mutant H472A TbRIF4-GFP than from wild-type cells and contains a small proportion of siRNAs . Although at present it is unclear how this observation relates to TbRIF4 function , it indicates that mutant TbRIF4 becomes “stuck” in a complex with TbAGO1 . It is possible that the interaction between the two wild-type proteins is transient and that after the siRNA is converted to single-stranded form within this complex ( either before or after loading into TbAGO1 ) , TbRIF4 dissociates from TbAGO1 . TbRIF4 on its own does not appear to show any strand selectivity; therefore , the strand removed is possibly determined by the orientation in which the duplex is presented by Dicer , as has been reported in Drosophila [38] . The use of chaperone proteins , for example heat shock proteins , in RISC loading has been reported in higher eukaryotes [39]–[42] . It is possible that TbRIF4 acts in this way in addition to its role as an exonuclease . We also observed that TbAGO1 levels are diminished in cells lacking functional TbRIF4 . However , we note that there was no straightforward correlation between the amount of TbAGO1 present in the various mutant cell lines and their degree of RNAi competency , suggesting that catalytically impaired TbRIF4s can to some extent promote TbAGO1 accumulation through a mechanism that is presently unknown . As TbAGO1 mRNA levels are only marginally affected by TbRIF4 deletion ( data not shown ) , either TbAGO1 translation is severely inhibited or the protein is made and rapidly degraded . So far inhibition of the proteasome and lysosome degradation pathways has not restored TbAGO1 levels ( unpublished observations ) , pointing to the possibility that translational control regulates the amount of TbAGO1 that is synthesized . T . brucei RIF4 shares with Neurospora QIP a C-terminal 3′-5′ exonuclease domain of the DnaQ superfamily . Although both proteins are essential RNAi factors and interact with the corresponding Argonaute , they are functionally distinct in several respects . First , in Neurospora qipko cells duplex siRNAs are stably loaded into the AGO homolog QDE-2 and , after QDE-2 slicer-mediated cleavage of the passenger strand , remain bound to QDE-2 in duplex , nicked form [20] . In contrast , siRNAs can be successfully loaded into TbAGO1 and TbRIF4 can accomplish the transition from double- to single-stranded siRNAs in the absence of TbAGO1 slicer activity [21] , [22] . Secondly , we showed that TbRIF4 activity is required for TbAGO1 protein accumulation . This finding is not mirrored in Neurospora or Drosophila , where mutation of passenger strand fragment removal factors QIP or C3PO has no effect on the corresponding AGO levels [20] , [28] . In light of these data , it is evident that TbRIF4 and NcQIP are functionally distinct RNAi factors . In trypanosomes expressing HsAGO2 , maturation of siRNAs takes place independently of TbRIF4 , suggesting a model where TbRIF4 substitutes for AGO slicing activity in the generation of ss-siRNAs . This would represent a departure from strategies of RISC maturation in higher eukaryotes , which favor slicer activity marking the passenger strand for degradation , although bypass pathways can operate in the absence of slicer activity [27] , [43] , [44] . Taken together with our earlier studies , the results presented here provide a detailed depiction of the core RNAi machinery in the early divergent parasitic protozoan T . brucei . In these organisms , the RNAi pathway is initiated by two distinct Dicer-like enzymes , namely TbDCL1 , mostly found in the cytoplasm [15] , and TbDCL2 , a mainly nuclear Dicer responsible for processing dsRNAs originating from retroposons and satellite-like repeats [16] . siRNAs generated by both Dicers are channelled into a single member of the Argonaute family of proteins , namely TbAGO1 [13] , [14] . The identification and characterization of TbRIF4 and TbRIF5 most likely completes the collection of core components shared by RNAi-positive trypanosomatids . However , it is likely that , as described in other organisms , other cellular factors interact with the core RNAi machinery . Some of these factors may perform housekeeping functions , for instance the Hsp90/70 family of chaperons that functions in RISC assembly in higher eukaryotes , and be present in all trypanosomatids irrespective of their RNAi proficiency , while others may be organism-specific . Indeed , we have recently discovered that the T . brucei homologue of the plant HEN1 2′-O methyltransferase [45] is required for siRNA 3′ end modification ( unpublished observation ) . However , siRNA modification is not an absolute requirement for trypanosomatid RNAi , as in L . braziliensis the HEN1 gene is absent , and deletion of TbHEN1 is still compatible with a robust RNAi response ( unpublished observations ) . It could be argued at this point that attempting to reconstitute RNAi in RNAi-deficient pathogens is quite daunting with a minimum of five core components . However , our past and current mechanistic studies have highlighted that not all components are necessary . In particular , we have shown previously that TbDCL2-deficient cells are more responsive to RNAi triggers than wild-type cells [16] suggesting that this factor does not need to be included in a reconstitution attempt . In addition , in the present study we have shown that human AGO2 can functionally replace both TbAGO1 and TbRIF4 , indicating that the TbRIF4 function can be bypassed by using an appropriate AGO protein . It will be interesting to see whether human AGO2 can similarly function in L . braziliensis . The factor remaining a mystery is TbRIF5 and its function will be challenging to ascertain due to a complete lack of a possible functional indicator . Nevertheless , the available proof of principle that RNAi can be reconstituted in S . cerevisiae by using only AGO1 and DCR1 from S . castelli [9] in combination with our functional studies raises the intriguing possibility that a similar strategy might be applicable in RNAi-negative trypanosomatids .
Previously published procedures were followed for culturing trypanosome YTat 1 . 1 cells [46] , generation of knockout cell lines by PCR-based methods [47] , Northern blots of total RNA [32] , Western blots [14] , S100 preparation [32] , immunoprecipitations [14] , and the Dicer in vitro assay [16] . 5 µg of total RNA from various cell lines was treated with 2 units of DNase RQ ( Promega ) , phenol extracted and ethanol precipitated . DNase-treated RNA was used in a reverse transcription reaction using the manufacturer's protocol for the Superscript II enzyme ( Invitrogen ) , phenol extracted , ethanol precipitated , and resuspended in 100 µl water . Mock reactions without the reverse transcriptase enzyme were carried out in parallel . 22 cycles of PCR were performed using the Phusion enzyme ( Finnzyme ) and the GC buffer provided , according to manufacturer's instructions: 50°C annealing temperature , 10 sec extension time for the CIR147 reactions , 30 sec extension time for the histone 4 reactions . Small RNAs were extracted from S100 extracts by incubation in polysome buffer plus 20 mM EDTA , 2% SDS and 0 . 1 µg/µl proteinase K for 1 hour at room temperature . An equal volume of phenol and 1/10 volume 20× SET [pH 7 . 4] were added and the mixture was centrifuged at 13 , 200× g for 5 min . The supernatant was incubated with 600 µl isopropanol and 30 µg GlycoBlue ( Ambion ) at −20°C for 30 min and centrifuged at 13 , 200× g for 10 min at 4°C . The pellet was washed with 70% ethanol , dried and resuspended in water ( for native gels ) or urea loading buffer ( for denaturing gels ) . RNA was transferred to a positively-charged nylon membrane ( Hybond N+ , GE Healthcare ) and UV cross-linked at 120 mJ/cm2 . The membrane was incubated in boiling water , which was allowed to cool to room temperature , twice prior to probe hybridization . This treatment significantly improved detection of dsRNA . DNA oligos were 5′ end-labelled with T4 polynucleotide kinase ( NEB ) using [γ-32P]ATP and purified using P6 columns ( BioRad ) . Hybridizations were carried out in ExpressHyb solution ( Clontech ) overnight at 30°C . Washes in 2× SSC , 0 . 2% SDS were carried out at room temperature . Blots were analyzed by PhosphorImager . TbRIF4 was cloned into the pGEX4T-1 vector . Inductions of 500 ml DH5α bacteria were carried out by adding 0 . 2 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) overnight at 16°C . Cells were washed in PBS containing protease inhibitors , lysed by sonication in 5 ml PBS containing protease inhibitors and GST-TbRIF4 was purified using a SpinTrap column ( GE ) . Proteins were eluted in 50 mM Tris-HCl [pH 8 . 0] , 10 mM glutathione , before being applied to a desalting column and stored in assay buffer ( 20 mM HEPES-KOH [pH 7 . 9] , 150 mM sucrose , 20 mM potassium L-glutamate , 3 mM MgCl2 , 2 mM DTT ) . Exonuclease assays were carried out for 30 min at 28°C in 10 µl assay buffer including RNase Inhibitor ( Roche ) and 0 . 01 µg/µl bovine tRNA . Reactions were terminated by the addition of 4× SET , 20 mM EDTA , 30 µg GlycoBlue and isopropanol , stored at −20°C for 30 min and spun at 13 , 200× g for 10 min at 4°C . The pellet was washed with 70% ethanol , dried and resuspended in urea loading buffer . Samples were resolved on a 20% denaturing polyacrylamide gel and analyzed by PhosphorImager . | RNA interference ( RNAi ) , a naturally-occurring pathway whereby the presence of double-stranded RNA in a cell triggers the degradation of homologous mRNA , has been harnessed in many organisms as an invaluable molecular biology tool to interrogate gene function . Although this technology is widely used in the protozoan parasite Trypanosoma brucei , other parasites of considerable public health significance , such as Trypanosoma cruzi , Leishmania major , and Plasmodium falciparum do not perform RNAi . Since RNAi has recently been introduced into budding yeast , this opens up the possibility that RNAi can be reconstituted in these pathogens . The key to this is getting a handle on the essential RNAi factors in T . brucei . By applying comparative genomics we identified five genes that are present in the RNAi-proficient species , but not in RNAi-deficient species: three previously identified RNAi factors , and two novel ones , which are described here . This insight into the core T . brucei RNAi machinery represents a major step towards transferring this pathway to RNAi-deficient parasites . | [
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"m... | 2012 | Comparative Genomics Reveals Two Novel RNAi Factors in Trypanosoma brucei and Provides Insight into the Core Machinery |
The human genome encodes thousands of long noncoding RNA ( lncRNA ) genes; the function of majority of them is poorly understood . Aberrant expression of a significant number of lncRNAs is observed in various diseases , including cancer . To gain insights into the role of lncRNAs in breast cancer progression , we performed genome-wide transcriptome analyses in an isogenic , triple negative breast cancer ( TNBC/basal-like ) progression cell lines using a 3D cell culture model . We identified significantly altered expression of 1853 lncRNAs , including ~500 natural antisense transcript ( NATs ) lncRNAs . A significant number of breast cancer-deregulated NATs displayed co-regulated expression with oncogenic and tumor suppressor protein-coding genes in cis . Further studies on one such NAT , PDCD4-AS1 lncRNA reveal that it positively regulates the expression and activity of the tumor suppressor PDCD4 in mammary epithelial cells . Both PDCD4-AS1 and PDCD4 show reduced expression in TNBC cell lines and in patients , and depletion of PDCD4-AS1 compromised the cellular levels and activity of PDCD4 . Further , tumorigenic properties of PDCD4-AS1-depleted TNBC cells were rescued by exogenous expression of PDCD4 , implying that PDCD4-AS1 acts upstream of PDCD4 . Mechanistically , PDCD4-AS1 stabilizes PDCD4 RNA by forming RNA duplex and controls the interaction between PDCD4 RNA and RNA decay promoting factors such as HuR . Our studies demonstrate crucial roles played by NAT lncRNAs in regulating post-transcriptional gene expression of key oncogenic or tumor suppressor genes , thereby contributing to TNBC progression .
While more than 80% of the genome is transcribed to RNA , high throughput gene expression analyses have revealed that only 2% of transcribed RNAs are translated into proteins . Current studies estimate that the human genome harbors several thousands of noncoding RNA ( ncRNA ) genes [1 , 2 , 3 , 4] . NcRNAs are grouped into different subclasses; from short non-coding transcripts like miRNAs and piRNAs ( ~20–30 nucleotides [nts] long ) , to middle range ncRNAs like snRNAs and snoRNAs ( ~30–200 nts long ) , and finally the long non-coding RNAs ( lncRNAs ) ( >200 bp in length ) . So far , the most studied class is microRNAs ( miRNAs ) , which promote gene silencing by inhibiting translation of target genes and/or by destabilizing the mRNAs [5 , 6] . LncRNAs comprise the least studied , but most complex group of ncRNAs . Unlike miRNAs , lncRNAs are very diverse with respect to their function , localization , abundance and interacting partners [7] . For instance , lncRNAs can form complex 3D secondary structures with the capacity to bind to proteins as well as to nucleic acids ( DNA as well as RNA ) . This dual capacity renders lncRNAs as an ideal regulator in protein-nucleic acid network . The human genome is estimated to contain ~16000 lncRNA genes [https://www . gencodegenes . org] . Based on the genome positioning , lncRNA genes could further be grouped into subclasses , including NATs or natural antisense transcripts ( ~5501 ) , lincRNAs or long intergenic non-coding RNAs ( ~7499 ) , sense intronic RNAs ( ~905 ) , sense overlapping RNAs ( ~189 ) , and processed transcripts ( ~544 ) [https://www . gencodegenes . org] . Breast cancer ( BC ) is the most common cancer in women , underscoring a need for research and development of more efficient treatment strategies [8] . BC is a heterogeneous disease and comprises several subtypes based on the presence or absence of three hormone receptors; estrogen receptor ( ER ) , progesterone receptor ( PR ) and human epidermal growth factor 2 ( HER2 ) . Based on the expressions of receptors , BC is categorized as Luminal A ( ER positive and/or PR positive and HER2 negative ) , Luminal B ( ER positive and/or PR positive and HER2 negative or positive ) , HER2+ ( ER and PR negative , HER2 positive ) and triple-negative breast cancer ( ER/PR/HER2 negative ) . The clinical outcome is worst for triple-negative breast cancer ( TNBC ) patients mainly due to lack of any of the three hormone receptors and , consequently , poor response to hormone-targeted therapies [9 , 10 , 11 , 12] . Therefore , there is an emergent need to investigate the molecular biology of the TNBC subtype to identify efficient prognostic and diagnostic markers . Current research on BC primarily focuses on the role of protein-coding genes in the disease progression . However , recent studies indicate that a significant number of lncRNAs show aberrant expression in BC patients ( For review please see [13] ) . Abnormal expression of several lncRNAs is associated with chemoresistance in BC cells [14] . However , the underlying molecular mechanism remains to be determined for most cases . Mechanistic studies have indicated that several of the BC-deregulated lncRNAs play crucial roles in disease pathology . For example , HOTAIR is known to negatively regulate the expression of many protein-coding genes by recruiting repressive PRC2 and LSD1 complexes to chromatin . HOTAIR is overexpressed in a significant number of BC patients , and is shown to act as a powerful predictor of metastasis [15] . We and others have demonstrated the involvement of MALAT1 in breast cancer progression and metastasis [16 , 17] . MALAT1 is overexpressed in a significant number of BC patients , and its depletion compromises both tumorigenic and metastatic properties of BC cells . In a mouse mammary carcinoma model , genetic loss or systematic depletion of MALAT1 in MMTV-PyMT resulted in slower tumor growth and reduction in metastasis [16] . In addition to HOTAIR and MALAT1 , both of which promote oncogenesis , lncRNAs such as GAS5 are shown to act as tumor suppressors [18] . As of now , we understand the molecular action of only a handful of the several thousands of lncRNAs that show aberrant expression in BC patients . In order to understand the role of lncRNAs during TNBC progression , we performed RNA-seq in an isogenic tumor progressive TNBC cell line series and compared the expression of all of the annotated lncRNAs to a normal-like mammary epithelial cell line . We found that 1853 lncRNAs showed aberrant expression in the metastatic BC cells . Among these lncRNAs , >1/4 ( 504/1853 ) of them are found to be natural antisense transcripts ( NATs ) . Interestingly , we observed that several of these NATs are transcribed in opposite orientation to key oncogenic and tumor suppressor protein-coding genes , and the expression of both sense and antisense transcripts is co-regulated in both TNBC cells and BC patient samples . Mechanistic studies of one such NAT , PDCD4-antisense RNA1 ( PDCD4-AS1 ) in BC progression demonstrated that it regulates the expression of its sense protein-coding partner , PDCD4 ( Programmed Cell Death 4 ) in cis . PDCD4 , initially identified in a screen aimed to determine apoptosis-induced targets [19] , is a well-established tumor suppressor gene [20] . We observed that the reduced levels of PDCD4-AS1 lncRNA in TNBC cells were correlated with reduced expression of PDCD4 in these cells . Further , we demonstrated that PDCD4-AS1 acted upstream of PDCD4 and induced PDCD4 expression by enhancing the stability of PDCD4 RNA . Our studies have unearthed novel NAT-mediated post-transcriptional mechanisms controlling the expression of protein coding genes in cis .
Human breast carcinomas are suggested to evolve via sequential genetic modifications from benign hyperplasia of mammary epithelial cells , into atypical ductal hyperplasia , to ducal carcinoma in situ ( DCIS ) and eventually to fully malignant tumors that possess the potential to metastasize into distant organs [21 , 22 , 23] . In order to understand the role of lncRNAs during various stages of breast cancer ( BC ) progression , we utilized a well-established isogenic mammary epithelial cell line-derived BC progression model system [21 , 23] . This system consists of multiple cancer cell lines of basal-like or TNBC subtype , all of which were initially derived from the spontaneously immortalized , non-tumorigenic mammary epithelial cell line , MCF10A [24] . The model system comprises of 4 isogenic cell lines , categorized as M1-M4 [21 , 23] . M1 represents the normal , non-tumorigenic , immortalized MCF10A cells . Transfection of MCF10A with activated T24-HRAS and selection by xenografting generated the M2 ( MCF10AT1k . cl2 ) cell line , which is highly proliferative and gives rise to premalignant lesions with the potential for neoplastic progression . M3 ( MCF10Ca1h ) and M4 ( MCF10CA1a . cl1 ) were derived from occasional carcinomas arising from xenografts of M2 cells . M3 gives predominantly well-differentiated low-grade carcinomas on xenografting , while M4 gives rise to relatively undifferentiated carcinomas and colonizes to the lung upon injection of these cells into the tail vein [22 , 25 , 26 , 27 , 28 , 29] . These lines represent progression through various stages of breast tumorigenesis and recapitulate key steps that mimic the progression of breast cancer in vivo [25] . In addition , the common genetic background of these cells enables us to rule out the genetic variation behind the deregulated gene expression . We hypothesized that functional characterization of lncRNAs , especially those displaying differential expression among these cell lines , would help us to determine their roles in TNBC development . We cultured M1-M4 cells as three-dimensional ( 3D ) acinar or organoid-like structures in Matrigel for 7–10 days , as 3D acini structurally and morphologically resemble in vivo acini of breast glands and lobules [28 , 30] . We performed poly A+ selected paired-end deep RNA-seq ( ~160–250 million reads/sample ) in two biological replicates and analyzed the expression of 28905 genes in M1 , M2 , M3 and M4 cells ( 17396 protein coding and 11509 lncRNAs ) ( GENCODE Release v19 [GRCh37] ) ( Fig 1A ) . We identified transcripts , which were more than 2-fold deregulated in both biological repeats . Since we were primarily interested in lncRNAs that show abnormal expression during BC progression and metastasis , we initially compared gene expression between M1 and M4 cells ( S1 and S2 Tables ) . Expression of 4668 genes ( 2815 protein coding and 1853 lncRNAs ) were altered >2-fold change in their expression between M1 and M4 cells in both biological repeats ( Fig 1B ) . 1159 out of the 1853 deregulated lncRNA genes showed >2-fold upregulation in M4 cells ( Fig 1C , S2 Table ) . On the other hand , 694 lncRNA genes displayed reduced expression in M4 compared to M1 cells . Further , we noticed that natural antisense transcripts ( NATs ) comprised one of the largest types of lncRNAs ( 504 out of 1853 ) , along with lincRNAs and pseudogenes , which showed deregulation in M4 cells ( Fig 1D ) . Our data supports observations from a recent study , reporting deregulated expression a significant number of NATs in breast cancer samples [31] . NAT lncRNAs are typically enriched in the nucleus [1 , 32 , 33] , and recent studies indicate that several of the NATs function in cis by regulating the expression of their sense partner protein-coding genes ( for review please see [31 , 33 , 34] ) . To gain insights into the potential NAT-mediated cis-gene regulation in BC cells , we examined the status of co-regulated expression of 504 NATs and their protein-coding partner in M1 and M4 cells . We observed that 108 out of 504 deregulated NATs and their sense protein coding genes showed >2-fold change in expression ( S3 and S4 Tables ) . Among them , 94 ( ~87% ) NAT: mRNA pairs showed concordant pattern of deregulation ( i . e . , both sense/antisense pairs are up- or are down-regulated concordantly ) and 14 ( ~13% ) pairs exhibited discordant pattern of expression ( Fig 1E and 1F ) . To assess if these NATs potentially regulate the expression of protein-coding genes that play crucial roles in BC progression , we determined the percentage of the sense protein coding genes in the sense: NAT pair that play well-established roles in cancer progression . We compiled data sets from multiple sources to identify potential cancer-associated genes , that are involved in vital cellular processes such as cell cycle and Epithelial-to-Mesenchymal transition ( EMT ) ( https://www . qiagen . com ) , ( http://www . bushmanlab . org/links/genelists ) , [17 , 35] ( S5 Table ) . By such analysis , we identified 29 deregulated NAT: mRNA pairs in which the protein coding genes have established roles in cancer progression ( Fig 1F and 1G , S6 Table ) . Furthermore , comparison of expression data of these NATs with ‘clinical survival in invasive breast carcinoma patient dataset’ ( TCGA dataset , containing 105 normal samples and 814 breast tumors ) revealed that the expression of 3 of these NATs was well correlated with survival outcomes in BC patients ( S7 Table ) [36] . Thus , BC deregulated NAT: sense protein-coding genes could potentially play vital roles in BC progression and survival . To gain insights into the role of NATs in BC progression , we focused our attention on one NAT lncRNA , PDCD4-AS1 for the following reasons . PDCD4-AS1 is a NAT lncRNA , transcribed from the complementary strand of Programmed Cell Death 4 ( PDCD4 ) gene ( Fig 2A ) . PDCD4 is a known tumor suppressor gene that negatively regulates cell proliferation , neoplastic transformation and tumor invasion [37] . RNA-seq , RT-qPCR and immunoblot analyses demonstrated reduced levels of PDCD4-AS1 , PDCD4 mRNA and protein in M2 , M3 & M4 cells compared to M1 ( Fig 2B–2D & S1A and S1B Fig ) . Furthermore , PDCD4 and PDCD4-AS1 RNAs showed significant positive correlation with each other in breast cancer patient RNA data set ( Fig 2E ) . Further , gene expression data from breast invasive carcinoma patients ( TCGA data set ) [36] revealed that PDCD4-AS1 showed lowest levels in basal-like or TNBC patients compared to Luminal A , Luminal B and HER2 subtypes ( Fig 2F ) . Highest levels of PDCD4-AS1 were observed in stage Tis ( stage 0 , pre-cancer ) breast samples compared to samples from the more aggressive stages of BC ( Fig 2G ) . Finally , the elevated levels of PDCD4-AS1 were correlated with better survival rate in a cohort of BC patients ( Fig 2H ) . Similar to PDCD4-AS1 , TNBC patient samples showed lowest levels of PDCD4 mRNA , and higher PDCD4 mRNA levels correlated with better survival in BC patients , further supporting its role as a potential tumor suppressor ( S1C and S1E Fig ) . Our results indicate that the levels of PDCD4-AS1 and PDCD4 mRNA are co-regulated in BC cell lines and in BC patients . Low expression of PDCD4-AS1 in BC patient samples as well as better survival of patients with higher levels of PDCD4-AS1 implies that PDCD4-AS1 , similar to its sense partner PDCD4 , might function as a tumor suppressor . RNA-seq and RT-qPCR analyses in M1 cells determined PDCD4-AS1 as a multi-exonic ( two exons ) , ~778 nts long polyadenylated transcript ( S1F Fig & S2I Fig ) . CPAT algorithm ( Coding Potential Assessing Tool ) identified PDCD4-AS1 as a noncoding RNA , as its coding potential score was relatively low and comparable to other well-established lncRNAs such as MALAT1 ( S1G Fig ) . Further , cellular fractionation followed by RT-qPCR assays indicated that PDCD4-AS1 lncRNA was enriched in the nuclear fraction in mammary epithelial cells ( Fig 2J ) . Finally , we determined the turnover rate of PDCD4-AS1 in M1 cells . RNA stability assay indicated that PDCD4-AS1 is a relatively stable transcript , and it displayed similar stability to its protein-coding partner PDCD4 mRNA ( t1/2 of ~4hrs; Fig 2K ) . Our results identify PDCD4-AS1 as a stable , poly A+ lncRNA that is enriched in the nucleus . PDCD4 was initially identified as a tumor suppressor gene that was upregulated during serum starvation or cellular quiescence [19] . To test whether PDCD4-AS1 is also induced under conditions that activate PDCD4 , we determined the expression of PDCD4 and PDCD4-AS1 in asynchronous and quiescent ( serum-starved ) M1 cells ( S2A Fig and S2B Fig ) . RT-qPCR and immunoblot data revealed elevated levels of both PDCD4 ( mRNA and protein ) and PDCD4-AS1 RNA in quiescent cells ( S2C Fig & S2D Fig ) . Our results indicate that PDCD4-AS1 shows co-regulated expression with its protein-coding partner PDCD4 . Since a lower level of PDCD4-AS1 RNA was associated with poor survival in breast cancer patients , and since it showed positive correlated expression with the tumor suppressor gene PDCD4 both in breast cancer cells and in patients , we evaluated whether PDCD4-AS1 contributes to cancer-associated phenotypes . We stably depleted PDCD4-AS1 transcripts by using three independent shRNAs targeting the sequences of PDCD4-AS1 ( exon 2 ) that were not overlapping with PDCD4 mRNA ( S3A Fig & S3B Fig ) in non-tumorigenic mammary epithelial ( M1 ) cells . RT-qPCR revealed that PDCD4-AS1 shRNA successfully depleted both nuclear and cytoplasmic pool of PDCD4-AS1 ( S3C Fig ) . Next , we analyzed the migration potential of control and PDCD4-AS1-depleted cells . M1 cells depleted of PDCD4-AS1 showed enhanced migration as observed by both transwell migration and wound healing assays ( Fig 3A–3D ) . Next , we overexpressed the full length PDCD4-AS1 in highly tumorigenic and metastatic M4 cells ( M4 cells contain lower levels of endogenous PDCD4-AS1 ) and determined the effect on cell migration and long-term cell proliferation . We observed that PDCD4-AS1-overexpressing M4 cells showed significant reduction in their ability to migrate ( Fig 3Ea-b ) and displayed reduced proliferation ( Fig 3Ec-d ) . It is known that tumor suppressor PDCD4 inhibits cell proliferation [38] . Flow cytometric analyses revealed increased population of S and G2/M in PDCD4-depleted M1 cells ( Fig 3F and 3G ) . Similarly , PDCD4-AS1-depleted M1 cells also showed increased population of S and G2/M cells ( Fig 3H & 3I ) . Collectively , these results indicate that both PDCD4 and PDCD4-AS1 negatively regulate cell proliferation in human mammary cells . We observed that depletion of PDCD4-AS1 increased cell cycle progression , and migratory properties of M1 cells . Depletion of PDCD4 is also known to promote tumorigenic properties of human cells ( For review please see [37] ) . Similar to what we observed upon depletion of PDCD4-AS1 , PDCD4-depleted M1 cells also showed enhanced cell cycle progression and cell migration ( Fig 3F and 3G & Fig 3J and 3K ) . Based on this , we hypothesize that PDCD4-AS1 negatively regulates tumorigenic properties of cells via modulating the expression of PDCD4 . To determine whether PDCD4-AS1 acts upstream of PDCD4 , we exogenously expressed of PDCD4 in PDCD4-AS1-depleted M1 cells and tested the effect on cell migration phenotype ( S4A Fig ) . Trans-well migration assays revealed that M1 cells transiently overexpressing PDCD4 alone did not show any significant change in their ability to migrate in vitro ( S4A Fig ) , while PDCD4-AS1-depleted control cells displayed increased migration ( Fig 3A and 3B & L [left and middle panels] ) . In contrast , overexpression of PDCD4 in cells that were stably depleted of PDCD4-AS1 rescued the enhanced migration , as these cells showed comparable levels of migration to control cells ( Fig 3L and 3M; compare left and right panels in 3L ) . Based on these results , we hypothesize that PDCD4-AS1 negatively regulates cellular migration via modulating PDCD4 expression/activity . To determine whether PDCD4-AS1 negatively regulates cell proliferation and cell migration by regulating the expression of PDCD4 in cis , we examined the level of PDCD4 mRNA and protein in M1 cells stably depleted of PDCD4-AS1 using shRNAs . We observed that PDCD4-AS1-depleted cells showed consistent reduction in the levels of PDCD4 mRNA and protein ( Fig 4A & Fig 4B ) . In addition , cells depleted of PDCD4-AS1 using modified antisense DNA oligonucleotides ( GAPMER ASOs ) against PDCD4-AS1 also showed reduction in the levels of PDCD4 mRNA ( S3D Fig ) . Also , cells treated with PDCD4-AS1 specific ASOs displayed cell cycle defects that were similar to PDCD4-AS1 shRNA-treated cells ( S3E Fig ) . In addition , cell fractionation followed by RT-qPCR in control and PDCD4-AS1-depleted cells showed significant reduction in the levels of PDCD4 in the nuclear pool , supporting the argument that PDCD4-AS1 primarily functions in the nucleus ( S3F Fig ) . Cells depleted of PDCD4 using two independent PDCD4 specific siRNAs did not show similar decrease in the levels of PDCD4-AS1 transcript ( Fig 4C & 4D ) . In case of PDCD4-AS1-mediated regulation of PDCD4 , we tested whether depletion of PDCD4-AS1 also alters the expression of other genes located in close genomic proximity . RT-qPCR analyses revealed that the expression of several other genes ( BBIP1 , SHOC2 and RBM20 [Fig 2A] that are located in genomic regions close to PDCD4-AS1/PDCD4 locus remained unaltered upon PDCD4-AS1 or PDCD4 depletion ( S4B Fig & S4C Fig ) . These results imply that PDCD4-AS1 positively and specifically regulates the expression of its sense transcript . NATs could regulate the expression of their sense partner genes either by influencing transcription or by modulating post-transcriptional processing of sense transcripts ( for review please see [33] ) . To determine whether PDCD4-AS1 regulates the transcription of PDCD4 gene , we quantified the levels of nascent PDCD4 pre-mRNA in control and PDCD4-AS1-depleted cells by nascent RNA capture followed by RT-qPCR analysis . PDCD4-AS1-depleted M1 cells did not show any significant change in the total levels of nascent PDCD4 pre-mRNA , indicating that PDCD4 transcription remained unaffected in cells lacking PDCD4-AS1 ( Fig 4E ) . Next , to test whether PDCD4-AS1 influenced post-transcriptional processing of PDCD4 mRNA , we performed RNA stability assay . We treated control and PDCD4-AS1-depleted cells with an RNA polymerase II transcription inhibitor Falvopiridol ( 1μM ) , collected samples at several time points post drug treatment , and performed RT-qPCR analyses to determine the relative levels of PDCD4 mRNA . Control cells displayed a half-life of ~5 hrs for PDCD4 mRNA ( Fig 4F ) . However , cells depleted of PDCD4-AS1 showed ~50% reduction in the stability of PDCD4 mRNA ( half-life ~2 . 5 hrs ) ( Fig 4F ) . These results indicate that PDCD4-AS1 positively regulates the stability of PDCD4 mRNA . NATs regulate the stability of their sense RNAs by forming RNA duplex [39 , 40] . Among the several NATs that are involved in conferring mRNA stability , only a few have been shown to form RNA:RNA duplex with their sense RNAs [41 , 42] . In the case of PDCD4-AS1/PDCD4 pair , the 5’end of both the transcripts , including exon 1 and part of intron 1 , showed complete complementarity ( Fig 5A; relative position within PDCD4-AS1 is highlighted in red lines ) . In addition , two other repetitive sequence elements located within exon 2 of PDCD4-AS1 show significant complementarity with sequences within the 3’UTR of PDCD4 mRNA . A 258 nt long sequence ( position 523–778 in exon 2 ) in PDCD4-AS1 shows 75% complementarity to a sequence within the 3’UTR PDCD4 mRNA ( position 3164–3417 ) . Besides this one , another shorter repeat of 103 nts long ( position 204–306 of exon 2 ) in PDCD4-AS1 also shows 82% complementarity with the PDCD4 mRNA 3’UTR ( position 3134–3236 ) ( S4D Fig ) , indicating that multiple elements within PDCD4-AS1 and PDCD4 mRNA could complement to form RNA duplexes . To determine whether PDCD4-AS1 and PDCD4 RNA form RNA-duplex under in vivo conditions , we initially performed double-strand RNase protection assays as reported earlier [43 , 44] . RNaseA specifically cleaves the single-stranded RNAs but have no activity on double-stranded/duplex RNAs . RNase protection assays revealed regions within PDCD4-AS1 and PDCD4 mRNA that were protected from RNaseA treatment , implying the presence of RNA duplex under in vivo conditions ( Fig 5B ) . We used BACE1/BACE1-AS pairs as a positive control [43] ( Fig 5B ) . Next , we performed RNA pulldowns followed by RT-qPCR to test physical association between PDCD4-AS1 and PDCD4 RNAs [42] . Towards this , we incubated biotin-labeled PDCD4-AS1 with cell extracts and performed RNA pulldowns using streptavidin-coated beads , followed by RT-qPCR assays . We observed significant interaction between PDCD4-AS1 and endogenous PDCD4 RNA in the pulldown experiment ( Fig 5C ) . Next , we determined to identify sequence elements within PDCD4-AS1 that play crucial roles in promoting PDCD4 mRNA stability . To this end , we generated full length and three mutant PDCD4-AS1 constructs ( PDCD4-AS1-FL , PDCD4-AS1Δ208–778 , PDCD4-AS1Δ477–778 , PDCD4-AS1Δ1–207 ) , each of the mutants lacks specific sequence elements that contain PDCD4 complementary sequences ( Fig 5A ) . We expressed these constructs in control and endogenous PDCD4-AS1-depleted M1 cells and determined the effect on endogenous PDCD4 mRNA levels . RT-qPCR assays in nuclear and cytoplasmic fractionated cell extracts revealed that the transiently expressed full-length and mutant RNAs were localized in both the nucleus and cytoplasm ( S4E Fig & S4F Fig ) . Interestingly , endogenous PDCD4-AS1-depleted M1 cells expressing PDCD4-AS1-FL , PDCD4-AS1Δ1–207 and PDCD4-AS1Δ477–778 RNA rescued PDCD4 mRNA levels ( Fig 5D ) . However , PDCD4-AS1Δ208–778 construct , which lacks the second exon of PDCD4-AS1 , expressing cells failed to rescue the level of PDCD4 mRNA . Furthermore , RNA stability assays revealed that both PDCD4-AS1Δ1–207 and PDCD4-AS1Δ477–778 and not PDCD4-AS1Δ208–778 rescued the overall stability of PDCD4 mRNA ( S4G Fig ) . Based on this , we conclude that sequence elements within the exon 2 of PDCD4-AS1 , which display complementarity to the 3’UTR of PDCD4 mRNA play crucial roles in stabilizing PDCD4 mRNA . Association of RNA-binding proteins ( RBPs ) to 3’UTRs is known to influence the cellular levels of PDCD4 mRNA . It was reported recently that RBPs such as HuR ( human antigen R ) and TIA1 ( T-Cell intracellular antigen-1 ) recognize overlapping sequence within PDCD4 mRNA 3’UTR , and positively regulate PDCD4 mRNA levels [45] . Hence , we sought to determine if PDCD4-AS1 regulates the stability of PDCD4 mRNA by influencing the binding of these RBPs to PDCD4 mRNA 3’UTR . ENCODE eCLIP data set identified several potential binding sites of HuR and TIA1 on PDCD4 RNA [46] . We performed RNA-immunoprecipitation ( RIP ) under crosslinking conditions using HuR or TIA1 antibody followed by RT-qPCR to determine the interaction between endogenous HuR or TIA1 and PDCD4 mRNA in control and PDCD4-AS1-depleted cells . RIP assays in control cells revealed that both HuR and TIA1 interacted with PDCD4 mRNA ( Fig 5E & S4H Fig ) . PDCD4-AS1-depleted cells showed reduced interaction between TIA1 and PDCD4 mRNA ( S4H Fig ) . On the contrary , PDCD4-AS1-depleted cells showed significantly enhanced interaction between HuR and PDCD4 mRNA ( Fig 5E ) . Altered interaction of TIA1 or HuR with PDCD4 mRNA in PDCD4-AS1-depleted cells was not due to overall changes in the total cellular levels of RBPs ( S4I Fig ) . Next , we examined if the depletion of HuR and TIA1 would affect the PDCD4 mRNA levels in mammary epithelial cells . Contrary to the earlier report , [45] , TIA1-depleted mammary cells did not reduce the levels of PDCD4 mRNA ( S4J Fig & S4K Fig ) . On the other hand , HuR depletion significantly increased PDCD4 mRNA and protein levels in control cells , indicating that in mammary epithelial cells HuR negatively regulates the levels of PDCD4 mRNA ( Fig 5F and 5G & S4L Fig ) . Finally , depletion of HuR in PDCD4-AS1-depleted M1 cells rescued the levels of PDCD4 mRNA and protein ( Fig 5F–5H ) . On the other hand , HuR depletion did not significantly alter the levels of PDCD4-AS1 RNA , indicating that HuR functions downstream of PDCD4-AS1 in the PDCD4-AS1: PDCD4: HuR axis ( Fig 5H ) . Thus , we conclude that PDCD4-AS1 promotes PDCD4 mRNA stability by negatively regulating HuR binding to PDCD4 mRNA . It is likely that the reduced binding of TIA1 to PDCD4 mRNA in PDCD4-AS1-depleted cells is a consequence of enhanced interaction of HuR to the same sequence elements , which also interact with TIA1 .
In the present study , we have attempted to understand the involvement of lncRNAs that are differentially expressed in TNBC cell lines , in cancer cell properties . We focused our efforts on NATs and in particular , the roles played by PDCD4-AS1 in regulating the expression of its sense partner , PDCD4 . We selected PDCD4-AS1/PDCD4 pair for mechanistic studies due to the following reasons . First , PDCD4 is a tumor suppressor gene , and shows reduced expression in several types of cancer , including BC [20 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57] . Second , both PDCD4-AS1 and PDCD4 show concordant expression in BC cell lines and in TNBC patient samples . Finally , clinical survival data in BC patients revealed that similar to PDCD4 gene , lower expression of PDCD4-AS1 reduced overall patient survival , implying a tumor suppressor role for PDCD4-AS1 . PDCD4 is a homolog of eukaryotic translation initiation factor 4G ( EIF4G ) , and by forming a complex with EIF4A1 , PDCD4 reduces the interaction between EIF4A1 and EIF4G , thereby inhibiting EIF4A1’s helicase activity . PDCD4 negatively regulates the translation of several oncogenes such as Cyclins , B-Myb and c-Myb [58 , 59] . Because of its critical role in several vital biological processes , its cellular level under normal physiological conditions is tightly regulated via several transcriptional and post-transcriptional regulatory mechanisms [60 , 61 , 62 , 63 , 64 , 65 , 66] . Our studies , demonstrating the role of PDCD4-AS1 in enhancing the cellular levels of PDCD4 adds another layer of complexity in PDCD4 regulation during BC progression . NATs are widely present in the human genome , and on an average ~38% of genomic loci in cancer cells express sense: anti-sense pairs [35] . However , NATs are expressed in much lower levels compared to sense transcripts , are mostly enriched in the nucleus , and several of them are shown to influence the expression of their sense partners via cis-mediated gene regulation [35] . Similar to earlier observations , we observed aberrant expression of a significant percentage of NATs during BC progression [31 , 34 , 35] . Moreover , we observed that several of the NATs expressed from cancer-associated gene loci showed concordant expression with the oncogenic or tumor suppressor sense partner genes and also displayed survival significance in patients , implying their potential involvement in contributing to the molecular pathology of BC progression and or metastasis . We observed that PDCD4-AS1 promotes the stability of PDCD4 mRNA in TNBC cells . PDCD4-AS1 depletion did not alter PDCD4 transcription significantly while it compromised the stability of PDCD4 mRNA . Further , we observed that PDCD4-AS1 forms RNA duplex with PDCD4 mRNA , and exon 2 of PDCD4-AS1 contains sequence elements that promote PDCD4 mRNA stability . PDCD4-AS1 could utilize multiple mechanisms to enhance RNA stability . It is possible that by forming RNA duplex , PDCD4-AS1 could prevent RNase-mediated degradation of PDCD4 mRNA , as observed in the case of FGFR3-AS1 [67] . Additionally , such RNA duplexes could prevent the binding of miRNAs to the 3’UTR of PDCD4 mRNA , thereby stabilizing the transcript , as observed in the case of BACE-AS1/BACE1 pair [33 , 43] . However , it is quite unlikely that PDCD4-AS1 promotes PDCD4 mRNA stability via regulating miRNA binding . Unlike BACE-AS1 , PDCD4-AS1 is predominantly localized in the nucleus , and stabilizes nuclear pool of PDCD4 RNA . A recent study also reported the role of NAT in regulating the expression of its sense partner by modulating chromatin organization [68] . VIM-AS1 transcribed from Vimentin ( VIM ) gene locus positively regulates VIM expression by forming RNA:DNA R-loop structure [68] . Disruption of VIM-AS1-mediated R-loop structure compromised VIM expression by inducing local chromatin compaction as well as reduced association of transcription factors to VIM promoter . In the case of PDCD4-AS1 , its depletion did not significantly change PDCD4 transcription , indicating that PDCD4-AS1 might not act via such a mechanism . Alternatively , PDCD4-AS1 by forming RNA duplex with PDCD4 RNA could influence the binding of RNA-binding proteins ( RBPs ) to PDCD4 mRNA . We observed that PDCD4-AS1 negatively regulates the association of HuR with PDCD4 mRNA . HuR-depletion studies in M1 cells further identified HuR as a destabilizer of PDCD4 mRNA . HuR/ELAVL1 is a U-/AU-rich element interacting RBP that is known to regulate mRNA stability . ( For review on HuR in breast cancer cells please see [69] . Several recent studies have described the role of HuR in destabilizing RNAs [70 , 71 , 72 , 73 , 74] . For example , HuR utilizes AUF1 , Ago2 or let-7 miRNA as co-factors to enhance the decay of p16 ( INK4 ) and MYC mRNAs [73 , 74] . HuR also promotes the early steps of myogenesis by destabilizing nucleophosmin/NPM mRNA [72] . We recently reported that in mouse cells , double stranded RNA binding protein ADAR1 & 2 negatively regulates HuR-mediated degradation of a significant number of RNAs [70 , 71] . Earlier studies have observed that NATs by forming RNA duplex with regions of mRNA containing AU-rich sequences , influences that association of AU-rich interacting RNA decay factors , thereby controlling mRNA stability [75 , 76] . For example , an antisense RNA from HIF1α locus destabilizes one of the isoforms of HIF1α by binding to it and exposing the AU-rich sequence element within the HIF1α 3’UTR [76] . On the other hand , a NAT transcribed from the Bcl2/IgH hybrid gene stabilizes the mRNA by masking the AU-rich sequence element [75] . In the present study , we observed that HuR destabilizes PDCD4 mRNA . The molecular mechanism underlying PDCD4-AS1-mediated inhibition of HuR/PDCD4 RNA interactions remained to be determined . It is known that a significant proportion of HuR is localized in the nucleus , and we have previously shown that nuclear pool of HuR destabilizes RNA [70 , 71] . Based on this , we hypothesize that the formation of RNA duplex between PDCD4-AS1 and PDCD4 RNA in the nucleus occludes the binding of HuR to the PDCD4 RNA , thereby stabilizing PDCD4 RNA ( Fig 5I ) . At present , it is not clear how NATs , which in general are present in lower copy numbers ( ~10–100 fold ) than their sense protein coding transcripts modulate post-transcriptional RNA processing in cis [35] . For example , Wrap53 , a NAT that is expressed at 100-fold lower levels than its sense partner , the tumor suppressor p53 gene , positively regulates the stability of p53 mRNA [42] . Similarly , low copy NAT , iNOS-AS ( expressed in 7 fold lower ) transcribed from iNOS locus interacts with the 3’UTR of iNOS RNA and positively regulates its stability [77 , 78] . As a matter of fact , the question of how low copy NATs regulate post-transcriptional processing of their sense transcripts remains an “unresolved conundrum” in the antisense-RNA field [79] . At present , there is no convincing molecular explanation of how NATs regulate the stability of high copy sense RNAs . Several studies have posed models to explain potential mode of action [42 , 80] . It is suggested that transient association of NAT with its sense RNA allows one NAT molecule to interact with multiple sense transcripts in a ‘hit and run’ fashion [42] . Such interactions could initiate local changes in sense RNA structure that favor or inhibit the binding of RBPs [42] . In a “recycling hypothesis” model , short complementary regions within the sense RNA:NAT pair promote intermolecular RNA:RNA interactions [80] . These interactions are transient and unstable due to the low melting temperature of the small duplex , and trigger conformational changes in the sense RNA , allowing either enhanced accessibility of a stabilizing RNA-binding protein or decreased affinity of an RNA decay factor to RNA , thereby modulating RNA stability . Once an RNP complex is formed , and the sense RNA is stabilized , the NAT is released from the complex and is recycled to stabilize another RNA molecule [80] . We observed that PDCD4-AS1 is expressed ~18 fold lower than PDCD4 mRNA in total cell extracts . However PDCD4-AS1/PDCD4 ratio in the nucleus , especially at their site of transcription will be much higher due to the fact that a major fraction of PDCD4-AS1 is enriched in the nucleus , where as most of the PDCD4 mRNA is exported to the cytoplasm . Based on these data , we hypothesize that transient interaction between PDCD4-AS1 and PDCD4 RNA in the nucleus , preferentially at the site of transcription , trigger conformational changes in PDCD4 RNA , resulting in differential binding of RBPs , such as HuR ( decay factor ) and AUF1 ( stabilizing factor ) to PDCD4 RNA . In this scenario , a single PDCD4-AS1 RNA could interact with several PDCD4 RNAs during its lifetime . In general , our studies have underscored the importance of a NAT in BC progression via its role in regulating the expression of a tumor suppressor sense partner . Future studies will unravel mechanistic roles of hundreds of other BC-deregulated lncRNAs in breast cancer biology .
All of the patient RNA-seq data was obtained from the publicly available database , TCGA ( https://cancergenome . nih . gov/ ) , and no additional ethics approval was needed . Acinar culture of M1-M4 cells was performed similar to three-dimensional culture of MCF10A cells described elsewhere [30] . Briefly , growth-factor reduced Matrigel was used to coat multi-well plates . A single-cell suspension of each of the cell lines M1-M4 was prepared . M2-M2 cells were suspended in an assay medium containing growth medium ( DMEM/F12 containing 2% Horse serum , 1 mg/ml hydrocortisone , 1 mg/ml cholera toxin , 10 mg/ml insulin , 10 ng/ml EGF , and 1% penicillin/streptomycin as well as 2 . 5% Matrigel dissolved in the medium . M3-M4 cells are prepared in the same way but omitting the EGF in the medium . The cells were seeded at a concentration of 8000 cells/mL . Media was changed every fourth day . Cells were cultured for 8 days prior to harvesting . M1 and M2 cells were cultured in DMEM/F12 medium containing 5% horse serum supplemented with 100 U/mL penicillin , 100μg/mL streptomycin , 20ng/mL EGF ( epidermal growth factor ) , 0 . 5 μg/mL Hydrocortisone , 100ng/mL Cholera toxin , 10 μg/mL insulin and 5% horse serum . M3 and M4 cells were cultured DMEM/F12 medium containing 5% horse serum supplemented with 100 U/mL penicillin , 100μg/mL streptomycin . 8 ( biological replicates of M1-M4 ) poly A+ RNA samples were pooled and sequenced in two lanes on HiSeq using Illumina TruSeq mRNA Prep Kit RS-122-2101 and paired-end sequencing . The samples have 163 to 256 million pass filter reads with a base call quality of above 94% of bases with Q30 and above . Reads of the samples were trimmed for adapters and low-quality bases using Trimmomatic software before alignment with the reference genome ( Human—hg19 ) and the annotated transcripts using STAR . The average mapping rate of all samples is 96% . Unique alignment is above 87% . There are 3 . 74 to 4 . 07% unmapped reads . The mapping statistics are calculated using Picard software . The samples have 0 . 59% ribosomal bases . Percent coding bases are between 67–72% . Percent UTR bases are 23–26% , and mRNA bases are between 94–96% for all the samples . Library complexity is measured in terms of unique fragments in the mapped reads using Picard’s MarkDuplicate utility . The samples have 31–52% non-duplicate reads . In addition , the gene expression quantification in raw count format was performed for all samples using STAR/RSEM tools by the annotation of Gencode v19 and normalized by size factor implemented in DESeq2 package . We calculated the fold change gene expression based on FPKM data . We identified deregulated genes with >2 fold cut off and then made the overlap list between two biological repeats . RNA seq data is deposited to GEO ( GEO accession number GSE120796 ) . Trizol reagent ( Invitrogen ) was used to extract total RNA according to manufacturer’s protocol . The concentration was measures using Nanodrop instrument ( ThermoFisher SCIENTIFIC ) . RNA was treated with RNase-free DNase I ( Sigma , USA ) and cDNA was synthesized from RNA using High capacity reverse transcription kit ( Applied Biosystem ) . Quantitative PCR was carried out by StepOnePlus system ( Applied Biosystem ) . For gene specific primers please see S8 Table . PDCD4 depletion was achieved by transfection with siRNA against GL3 ( control ) or siRNAs against PDCD4 ( 40–50 nM con , IDT ) for one round using Lipofectamine RNAiMax reagent ( Invitrogen ) . TIA1 depletion was performed using siRNA purchased from IDT . HuR depletion was carried out using siRNA as used in [81] . PDCD4-AS1 knockdown was performed by shRNA lentivirus-mediated transduction . PDCD4-AS1 depletion was achieved using gapmer ASOs at 200 nM final concentration ( Ionis Pharmaceuticals Inc . ) . For overexpression , full-length PDCD4 was purchased as pGEX6p1-hPdcd4 from Addgene [82] and cloned into pCGT vector . Full length PDCD4-AS1 and mutants were purchased as gblocks from IDT technology , cloned and expressed in pCGT vector , and empty vector was used as control . We used transwell migration chambers ( Corning , Cat# 354578 ) and to perform migration assays as previously explained [17] . Briefly , cells were starved in a serum-free medium , which was then trypsinized , counted and seeded in serum-free medium in transwell chamber ( 8μM ) . We placed the cell containing chambers into a well containing serum ( 24-well plate ) . Cells were kept in incubator 37 C , 5% CO2 overnight . Migrating cells were stained by Crystal Violet 0 . 05% and counted the day after . The wound was created by 200 μl filter tips . After washing with PBS , serum-free medium was added to cells in order to discourage the cell proliferation . Images were taken at Day0 , 1 , 2 and 3 after wound creation to monitor the wound healing . Click-iT Nascent RNA capture kit ( Invitrogen , Cat # C10365 ) was used to isolate nascent RNA following the product’s protocol . Then quantitative RT-qPCR was performed using gene-specific primers . Cells were treated with Flavopiridol ( 1M ) and were collected at different time points post treatment . RNA extraction and RT-qPCR was carried out as explained above . RIP was conducted as described before [71 , 83] . Briefly , RNA-Protein interactions were reversibly crosslinked by formaldehyde in cells . Cells were lysed and lysate was immunoprecipitated using Anti-HUR ( HuR ( 3A2 ) : sc-5261 , Santa Cruz Biotechnology ) and Anti-TIA1 antibody ( TIA-1 ( G-3 ) : sc-166247 , Santa Cruz Biotechnology ) . After RIP , we reversed cross-link and RNA extraction using Trizol LS ( Invitrogen ) . DNase I treatment , reverse transcription and qPCR was performed as described above . As explained in [71] , we washed cells with PBS and lysed in RSB buffer ( 10 mM Tris-HCl pH7 . 4 , 100 mM NaCl , 2 . 5 mM MgCl2 , RNase Inhibitor ) supplemented with Digitonin ( 8 g/ml ) ( D141-100MG , Sigma-Aldrich , USA ) for 10 min on ice . Lysate was centrifuged at 2000 rpm , 4°C , 10 min . Supernatant was collected as cytoplasmic fraction and RNA was extracted from with Trizol LS ( Invitrogen ) . The pellet included the nuclear fraction . We washed the nuclear pellet with RSB-Digitonin solution one more time and then RNA was extracted using Trizol reagent ( Invitrogen ) . Poly ( A ) fractionation was performed as previously described [44] . In brief , NucleoTrap mRNA kit ( Clontech ) was used to fractionate Poly ( A ) plus and Poly ( A ) minus fractions following by extraction , RT and qPCR . The experiment was performed as described previously [44] . Cells were washed with PBS and lysed in lysis buffer ( 10 mM Tris pH 7 . 4 , 3 mM CaCl2 , 2 mM MgCl2 , and 0 . 7% NP-40 ) . Cell lysate was passed through needle ( 27 . 5 gauge ) five times and then incubated on ice for 10 minutes . The final solution was adjusted to DNase I ( Sigma ) 12 . 5 units/ml and 125 mM NaCl . The lysate was divided to two fractions . To one fraction RNase A ( QIAgen ) and the other fraction RNAse Inhibitor was added to final concentrations of 200 ng/ml and 250 units/ml; respectively . Then , solutions were incubated at 37°C for 40 minutes . RNA was extracted using Trizol LS ( Invitrogen ) . PDCD4-AS1 and YFP ( -ve control ) full-length cDNA cloned in pGEM-Teasy plasmids were in vitro transcribed to generate biotinylated RNA ( Biotin Labeling Mix; Roche ) . M1 whole cell extract was incubated with biotin-labeled transcripts followed by streptavidin-mediated RNA pull down . Then RNA extraction , RT-PCR and qPCR were performed to analyze potential RNA: RNA interactions . | Breast cancer is the most common cancer in women worldwide . The molecular mechanisms underlying the disease have been extensively studied , leading to dramatic improvements in diagnostic and prognostic approaches . Despite the overall improvements in survival rate , numerous cases of death by breast cancer are still reported per year , alerting us about the potential gap of knowledge in cancer molecular biology era . The emerging advances in new generation sequencing techniques have revealed that the majority of genome is transcribed into non-protein coding RNAs or ncRNAs , including thousands of long ncRNAs ( lncRNAs ) of unknown function . Natural antisense RNAs ( NATs ) constitute a group of lncRNAs that are transcribed in the opposite direction to a sense protein-coding or non-coding gene with partial or complete complementarity . In this manuscript , we investigate the role of NATs in breast cancer progression , focusing on the role of PDCD4-AS1 , a NAT expressed from the established tumor suppressor PDCD4 gene locus . We observe that both PDCD4-AS1 and PDCD4 display concordant expression in breast cancer cell lines and patients . In mammary epithelial cells , PDCD4-AS1 promotes the stability of PDCD4 mRNA . PDCD4-AS1 by forming RNA duplex with PDCD4 RNA prevents the interaction between PDCD4 RNA and RNA decay factors in the nucleus . | [
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Brucellosis is regarded as a major zoonotic infection worldwide . Awareness and knowledge of brucellosis among occupational workers is considered an important aspect of brucellosis control in both humans and animals . The aim of this study was to explore the distributions of the pooled awareness level and the knowledge level of the disease worldwide . A meta-analysis was carried out to obtain pooled brucellosis awareness levels and knowledge levels of respondents regarding the zoonotic nature of brucellosis , mode of brucellosis transmission , and brucellosis symptoms in animals and humans . The analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-analyses guidelines . A total of seventy-nine original articles reporting the brucellosis awareness levels of in populations from 22 countries were assessed . The total pooled awareness level of brucellosis was 55 . 5% , and the pooled awareness levels regarding the zoonotic nature of brucellosis , mode of brucellosis transmission , signs of human brucellosis and signs of animal brucellosis were 37 . 6% , 35 . 9% , 41 . 6% , and 28 . 4% respectively . The pooled awareness level was higher than the brucellosis-related knowledge level . Subgroup analyses showed that no obvious differences in brucellosis awareness levels between high-risk populations in Asia and Africa . Health workers ( including human health workers and veterinarians ) had the greatest overall awareness and knowledge of human brucellosis . The overall awareness levels and knowledge levels of livestock owners ( farmers ) and herders were higher than those of dairy farmers and abattoir workers . In addition , awareness and knowledge levels were higher among people who were involved in bovine , caprine and ovine animal production or in caprine and ovine animal production than among people who were involved in only bovine animal production . Insufficient awareness and knowledge of brucellosis were observed in the original studies conducted mainly in Asia and Africa . Interventions to improve public knowledge about brucellosis are urgently needed .
Brucellosis is considered as one of the most important zoonoses in the world with more than 500 , 000 human cases occurring globally every year [1 , 2] . Despite a high burden of infection in many areas of the world , brucellosis is rarely prioritized by health systems and is considered a neglected zoonosis by the World Health Organization ( WHO ) [3] and World Organisation for Animal Health ( OIE ) [4] . Brucellosis causes abortion , infertility and milk production decline in animals [5 , 6] . It is transmitted to humans through consumption of unpasteurized dairy products and uncooked meat or through direct contact with infected animals , placentas or aborted fetuses [7] . Clinically , human disease is characterized by fever , fatigue , sweating , joint pain , headache , loss of appetite , muscular pain , lumbar pain , weight loss , and arthritis [8 , 9] and is often misdiagnosed as other febrile syndromes , such as malaria and typhoid fever , resulting in mistreatments and underreporting [6 , 10 , 11] . Generally , poor hygiene , prevalence of the disease in animals and practices that expose humans to infected animals or their products can significantly increase the risk of the occurrence of the disease in humans [12] . Therefore , farmers , pastoralists , abattoir workers , animal health personnel , laboratory personnel and other people involved in the livestock value chain are considered the highest occupational risk groups [13] . Vaccination is an important control tool particularly where there is no compensation for livestock owners for test-and-slaughter , there is no individual identification system and mobile livestock keeping is practiced . And the control and eradication of brucellosis cannot be achieved by vaccination and test-and-slaughter only; the cooperation of relevant occupational groups is an important component in achieving this goal [14] . Therefore , adequate knowledge of the epidemiology of brucellosis is of great public health importance , particularly among high-risk groups , as knowledge promotes people to take protective measures at work and actively participate in disease control programs , thus greatly assisting the development of brucellosis control strategies . Although there are many original studies that evaluate the knowledge and awareness of brucellosis , the overall awareness and detailed knowledge of the disease and the distribution of the literature remain unclear . To this end , we conducted this meta-analysis study to pool brucellosis awareness and knowledge levels worldwide as well as to seek out factors associated with the levels of awareness and knowledge .
This review was reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-analyses ( PRISMA ) guidelines [15] , and the PRISMA checklist is appended as S1 Appendix . Between March and June 2018 , a literature search was conducted in PubMed , Web of Science , China National Knowledge Infrastructure ( CNKI ) , Wan Fang and Yahoo search engines to identify the relevant articles about people’s brucellosis awareness and knowledge globally . The search string applied a combination of related words and was applied to each database separately , using Boolean operators . Searches used in all databases are shown in S2 Appendix . To identify additional relevant citations as much as possible , reference lists of included papers as well as “cited by” and “related information” tools in PubMed were searched . Not only English terms but also corresponding Chinese terms were applied to the Chinese databases . All primary study designs were considered eligible , thus secondary reports , nonoriginal research , comments , editorials and reviews were directly excluded . Studies were included if they were related to brucellosis awareness or knowledge assessment . Studies conducted to evaluate the awareness and knowledge levels of zoonotic diseases were included as long as they reported data about brucellosis , but only data related to brucellosis were considered and analyzed . Studies containing any of the following criteria were included: ( i ) studies reporting the awareness of brucellosis , where the original expression was similar to “Have you heard of brucellosis ? ” , “Do you know about brucellosis ? ” or “be aware of brucellosis”; ( ii ) studies reporting brucellosis knowledge about the mode of transmission to people , the zoonotic nature , and signs in humans and animals; ( iii ) studies reporting knowledge about consumption of unpasteurized milk and uncooked meat as high-risk practices for brucellosis infection in humans; and ( iv ) studies providing the information sources of people who had heard of brucellosis . All citations were imported and duplicates were removed using the software EndNote X8 . Two team members independently screened the literature in two stages . In the first stage , titles and abstracts were screened to exclude duplicates and ineligible studies based on relevance . In the second stage , the two reviewers independently evaluated the full text of the selected literature to ensure full compliance with the inclusion criteria . At each stage , the selected papers were compared by the two investigators for analysis consistency . At the event of a disagreement , a third investigator joined the discussion and made a decision . The screening and selection of studies were promoted by the creation of appropriately labeled subgroups in EndNote . A data abstraction form was constructed after screening the selected articles . For each included study , we extracted the following basic information: author , publication year , geographic region , study design , study population , sampling method , number of participants , education distribution , gender distribution and main livestock contacted by the studied population . Furthermore , the number of participants who answered positively ( n ) and sample size ( N ) were the two necessary parameters for the calculation of the pooled levels of brucellosis awareness and knowledge in the meta-analysis . In particular , the number of participants who answered positively ( n ) was obtained directly from these studies or by multiplying the sample sizes ( N ) with the proportions ( % ) associated with the investigated items reported in the studies . All the data extraction work was performed independently and then compared by two investigators . In the event of a disagreement , a third person joined the discussion and made a decision . All available data were pooled in the present meta-analysis . The subgroups and categories considered included geographic regions ( classified into five regions , Asia , Africa , South/Central America , North America and Oceania ) , animal species ( bovine , ovine and caprine ) , human populations ( occupational and nonoccupational groups; farmers , abattoir workers , traders , human and animal health workers , pastoralists and livestock transporters were identified as the occupationally exposed population ) and countries . Additional subgroup analyses were performed for specified occupations ( animal and human health workers , livestock owners ( farmers ) , dairy farmers , abattoir workers , pastoralists , patients , students and residents ) . Meta-analysis was performed based on a random-effect model . To stabilize the variance , the original rates were transformed by arcsine transformation . Cochran’s chi-square ( Q-test ) and the I-square ( I2 ) statistic were used to estimate the heterogeneity of the results . A funnel plot was constructed to visually examine the publication bias , and Begger’s rank test was used to test the significance of the plot’s asymmetry . R statistical software ( Version 3 . 0 . 0 ) was applied for all the aforementioned calculations . The quality and risk of bias of studies were assessed comprehensively as outlined in Hoy et al . [16] and Crombie et al . [17] . The risk of bias in the included studies was evaluated with a total of ten risk-biased items regarding external validity ( items 1 to 4 assessed domain selection and nonresponse bias ) and internal validity ( items 5 to 9 assessed the domain of measurement bias , and item 10 assessed the bias related to the analysis ) . For each item , the study was classified as “Yes” or “No” , which meant “Low risk” or “High risk” , respectively . At the end of the overall risk assessment of study bias , studies with a “No” score ≤3 were classified as low risk , studies with a “No” score 4–6 were classified as moderate risk and studies with a “No” score ≥7 were classified as high risk . The risk bias and assessment results are provided in S3 Appendix . Studies with overall high risk of study bias were still included in this present meta-analysis as long as the research purpose and design were reasonable and the numerator and denominator for the parameter of interest were appropriate .
The search and selection process of related studies is presented in Fig 1 . After the removal of articles published before 2010 , articles with data that could not be interpreted , articles with duplicated data and studies without full-text , seventy-nine studies were included in the meta-analysis . The characteristics of the included studies are provided in Table 1 . Among the included publications , 52 studies were from Asia , 24 were from Africa , one each from Europe , South/Central America and North America , respectively . Among the included studies , one was published in Portuguese , one was published in Turkish , 31 were published in Chinese , and 56 were published in English . The target populations of the studies included human health workers , high-risk occupational populations ( farmers , traders , abattoir workers , livestock transporters , and animal health workers . ) , students and residents . Main animal species reared by the respondents were cattle and buffalo , sheep and goats , pigs , camels and dogs . The sample sizes of the studies ranged from 26 to 2 , 491 respondents . A questionnaire-based survey was administered in all the included studies; five studies adopted a self-administered questionnaire , while 74 studies collected the data during face to face interviews . A low risk of bias was found in 63 studies , a moderate risk of bias was found in 15 studies and a high risk of bias was indicated in one study , which was included due to its reasonable research purpose and study design . The detailed risk of bias of each study is shown in S3 Appendix . In addition , with Begger's test , no evidence of publication bias was found ( Table 2 ) . An awareness of brucellosis was reported in 52 studies , with a pooled awareness level of 55 . 5% . An awareness of the zoonotic nature of brucellosis and its transmission mode were reported in 33 and 30 studies , respectively , with respective pooled awareness levels of 37 . 6% and 35 . 9% , as shown in Table 2 . An awareness of the clinical signs and symptoms of human brucellosis and animal brucellosis were reported in 23 and 16 studies , respectively , and the pooled awareness levels were 41 . 6% and 28 . 4% , respectively . In addition , we explored the distribution of brucellosis symptoms that were mentioned in the included studies . Fever , fatigue , joint pain , sweating and urogenital disease were the most commonly mentioned and studied symptoms in humans , but the pooled awareness level was lower than 35 . 0% . Abortion was the most commonly mentioned symptom of animal brucellosis , with a pooled awareness level of 37 . 2% , followed by a reduction in milk production ( 18 . 5% ) , as shown in Table 2 . Nine included studies explored the awareness of infected animals as the source of human infection , with a pooled awareness level of 54 . 1%; respondents listed sheep and goats as an animal source , followed by cattle , pigs and dogs as an infection source . The pooled awareness levels of raw milk consumption and the consumption of infected meat as risk factors for brucellosis were 44 . 5% and 34 . 6% , respectively . The pooled knowledge level of direct contact with aborted fetuses and abortion materials as high-risk practice was 54 . 9% ( Table 2 ) . Fifteen studies explored the awareness regarding the vaccination of animals against brucellosis , and the pooled awareness was only 26 . 1% ( Table 2 ) . Nine studies analyzed the information sources of those respondents who had heard of brucellosis . People mainly acquired knowledge of brucellosis from the following four sources: neighbors/friends , mass media ( TV/radio ) , health workers and health education-related lectures . Overall , 58 . 7% of respondents acquired the information about brucellosis through their neighbors or friends , which was notably higher than those that acquired information through TV/radio , health workers and lectures ( Table 2 ) . Regarding the awareness of brucellosis , no obvious differences were found between the occupation-related population and students and residents . Subgroup analysis by occupation showed that animal health workers had the greatest awareness of brucellosis ( 100 . 0% ) . Pastoralists had higher awareness of brucellosis ( 72 . 0% ) than livestock owners/farmers ( 57 . 0% ) , abattoir workers ( 24 . 3% ) , dairy farmers ( 29 . 5% ) and livestock ( product ) traders ( 30 . 3% ) . We also found that people who were involved in bovine , ovine and caprine production ( 72 . 5% ) and ovine and caprine production ( 74 . 3% ) had higher awareness levels than those people who were involved in only bovine production ( 35 . 6% ) , as shown in Tables 3 and 4 . Regarding the zoonotic nature of brucellosis , people involved mainly in bovine , ovine and caprine production had an awareness level of 54 . 7% and people involved in ovine and caprine had an awareness level of 62 . 2% , while people involved in only bovine production had an awareness level of 21 . 2% . The pooled awareness level of the zoonotic nature of brucellosis in the African population ( 17 . 8% ) was notably lower than that in the Asian population ( 44 . 0% ) . The results indicated that there was no clear difference in the brucellosis awareness levels between Asia ( 56 . 5% ) and Africa ( 53 . 4% ) ( Table 3 ) . Livestock owners ( farmers ) showed relatively higher awareness of the zoonotic nature of brucellosis than dairy farmers ( 15 . 4% ) and abattoir workers ( 2 . 6% ) ( Table 4 ) . Regarding the mode of transmission from infected animal to human , a low awareness level ( 37 . 4% ) was found in the occupationally exposed population , whereas a relatively higher awareness level was found in human health care providers ( 80 . 9% ) and animal health workers ( 75 . 9% ) . Abattoir workers and dairy farmers had extremely low awareness levels ( Tables 3 and 4 ) . Regarding awareness of the symptoms of human brucellosis , higher awareness levels were found in human health care providers ( 75 . 8% ) , animal health workers ( 50 . 5% ) and pastoralists ( 74 . 3% ) than in abattoir workers ( 18 . 3% ) and dairy farmers ( 3 . 1% ) . The awareness among people involved in bovine , ovine and caprine production ( 46 . 6% ) and ovine and caprine production ( 46 . 2% ) were notably higher than people involved in only ovine production ( 14 . 8% ) . Regarding regions , the awareness of human brucellosis symptoms was higher in Asia ( 45 . 1% ) than in Africa ( 18 . 7% ) . An extremely low awareness level of animal symptoms was observed , and no obvious differences were found among geographic regions and people involved in different animal production methods . ( Tables 3 and 4 ) . Regarding the awareness of vaccination of animals against brucellosis , the pooled awareness level in the African population ( 4 . 6% ) was notably lower than that in the Asian population ( 46 . 3% ) ( Table 3 ) . And the high awareness level of vaccination as a preventive measure for brucellosis was only found in dairy farmers ( 88 . 4% ) ( Table 4 ) . For the awareness level of brucellosis among the high-risk population ( animal health workers , farmers , abattoir workers , traders and transporters other related populations , not including human health workers ) , no significant difference ( P = 0 . 8 ) was observed between Asia and Africa . The results showed extremely low awareness of brucellosis in India ( 13 . 7% ) , Sri Lanka ( 11 . 6% ) , Angola ( 23 . 9% ) , Ethiopia ( 17 . 3% ) , Zimbabwe ( 21 . 0% ) and Senegal ( 0 . 0% ) ( Table 5 ) .
Raising the awareness of brucellosis and brucellosis-related knowledge in occupation-related groups is an important aspect for the effective control of brucellosis [97] . Health education about the disease for high-risk groups was essential in gaining support for a control program [98 , 99] . Therefore , assessing the overall disease awareness level of the occupational population is a basis for the development and implementation of more efficient health education activities and brucellosis control programs that should fit the needs and perceptions of local communities [100] . This is the first systematic review and meta-analysis aimed at exploring the brucellosis awareness level worldwide . Most of the original studies that assessed the awareness and knowledge of brucellosis were conducted in Asia and Africa , and with less from Europe , America and Oceania , which is generally consistent with the geographical distribution of brucellosis . Brucellosis is endemic to Asia and Africa , and countries in central and southwestern Asia are currently seeing the greatest increase in cases [101 , 102] . Overall , only approximately half of the occupation-related groups knew about brucellosis , which means that awareness and knowledge of brucellosis were insufficient . The knowledge levels regarding the zoonotic nature , mode of transmission and symptoms in humans and animals of brucellosis were lower than the awareness level of brucellosis , which means that people had heard of brucellosis but did not necessarily have a clear understanding of brucellosis . This might suggest that people in Asia and Africa have superficial and inadequate knowledge about brucellosis . Poor knowledge about brucellosis is an obstacle for brucellosis control and elimination [103] . The low awareness and knowledge levels elucidated in this study are therefore of great importance , particularly considering the zoonotic nature and the public health significance of brucellosis . Due to the low awareness and knowledge of brucellosis , the health of occupationally exposed populations and public food safety need more attention . It has been reported that a lack of knowledge about the disease could potentially lead to a delay in seeking medical support and , hence , a delay in the diagnosis and treatment of the disease [104 , 105] . Misdiagnosis often leads to a delay in treatment and can result in long-term complications from the disease [106] . In addition , the low brucellosis awareness and knowledge level of people involved in the livestock value chain could lead to a neglect in disease prevention and incorrect practices in handling , cooking and preserving animal-based food , which poses a great threat to public food safety [97] . Knowing the high-risk behaviors associated with brucellosis infections can also promote individuals to take protective measures , such as avoiding the consumption of raw milk and uncooked meat and wearing gloves when delivering or handling abortion materials . Many factors are thought to be related to the level of awareness and knowledge of brucellosis . Several studies in the meta-analysis have indicated that education is positively associated with awareness and knowledge levels [28 , 29 , 39 , 62 , 80 , 81 , 92 , 93 , 95 , 96] . It has been shown that previous experience with brucellosis in livestock and brucellosis prevalence levels are positively correlated with awareness and knowledge levels of brucellosis [107] . A study in southwestern Ethiopia [108] suggested that the lack of awareness of zoonotic diseases in the study area might have been due to the lack of awareness-creating activities provided by public health agencies and veterinary departments in the region . In summary , a low level of awareness could be due to remoteness , a lack of health facilities , poor extension services , little training on the rearing and handling of animals , a lack of health education programs and low literacy rates , which have been reported as major contributors to the low level of awareness among dairy farmers [109] . Currently , cross-sectoral and disciplinary cooperation in the control of zoonoses is encouraged by the “One Health” framework [110 , 111] . Communication and cooperation between the animal and human health sectors , the agricultural sector , the education sectors , animal producers and other relevant occupational groups are very important to improve the awareness and control of brucellosis . In the present study , greater brucellosis awareness and knowledge were reported in the respondents involved in both bovine and small ruminant production , and the awareness and knowledge level in the respondents involved in small ruminant production was higher than that in people involved in only bovine animal production . This might be because brucellosis seropositivity was higher in goats than in other species [112] . Health workers play an important role in health education and disease knowledge advocacy for occupational groups . In this study , the greatest awareness was reported in health care providers , including both animal and human health workers . This can be explained by their medical background and the training and experience they receive over their career , which proves the importance of education and training to improve the awareness of brucellosis in high-risk groups [113 , 114] . The results showed that the main brucellosis information sources were friends and neighbors . A low proportion of participants mentioned mass media ( radio/TV ) as a source of information about brucellosis; this fact may suggest that the role of television/radio as a mass media outlet for the dissemination of knowledge about brucellosis has not received much attention . This should be considered in the development of education programs regarding brucellosis control . The strength of our meta-analysis was that the evaluation of recent studies on about brucellosis awareness and knowledge among high-risk populations , health workers , general residents and students worldwide offered the evidence-based guidance for the implementation of education services and brucellosis control measures . However , there were several limitations in this study . Obvious heterogeneity existed in the meta-analysis . Although a theoretical framework was designed for this study , it was difficult to ensure that a reasonable design and rigorous questionnaire and sampling methods were used in all original studies to complete the investigations . In summary , mainly in Asia and Africa , an insufficient proportion of the populations in rural communities is aware of brucellosis and a low knowledge level of brucellosis was observed . Since the occupationally exposed population's perception of brucellosis influences the development and implementation of disease control strategies as well as the adoption of best practices and habits during work and life , it is very important to raise the awareness level of brucellosis in occupationally exposed populations . | Brucellosis is considered a neglected zoonotic disease that creates a very large obstacle to the development of animal production and is a great threat to human health . High brucellosis awareness and knowledge is critical for the implementation of correct practices and habits and consequently the control and prevention of brucellosis . The aim of this study was to estimate the awareness and knowledge of brucellosis , specifically regarding its zoonotic nature , mode of transmission , and signs in humans and in animals as well as awareness information sources . To this end , a meta-analysis of data from 79 studies was performed . The included studies on the awareness and knowledge of brucellosis were mainly from Africa and Asia . There were no significant differences in the awareness levels of brucellosis among high-risk groups in Asia and Africa . Overall , people’s awareness and knowledge of brucellosis were low and insufficient . Health workers had the highest pooled levels of awareness and knowledge regarding brucellosis . In addition , livestock stock owners ( farmers ) had notably higher awareness and knowledge levels than dairy farmers and abattoir workers . Neighbors and friends were the most common sources of brucellosis information for farmers . The low and insufficient awareness and knowledge about brucellosis is an obstacle for public health . Raising awareness and increasing detailed knowledge of brucellosis are of great significance for the control of brucellosis and the protection of human health . The potential of the media and health workers in the dissemination of knowledge about the disease needs to be fostered . | [
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"ne... | 2019 | Brucellosis awareness and knowledge in communities worldwide: A systematic review and meta-analysis of 79 observational studies |
Despite more than 25 years of research , the molecular targets of quinoline-3-carboxamides have been elusive although these compounds are currently in Phase II and III development for treatment of autoimmune/inflammatory diseases in humans . Using photoaffinity cross-linking of a radioactively labelled quinoline-3-carboxamide compound , we could determine a direct association between human S100A9 and quinoline-3-carboxamides . This interaction was strictly dependent on both Zn++ and Ca++ . We also show that S100A9 in the presence of Zn++ and Ca++ is an efficient ligand of receptor for advanced glycation end products ( RAGE ) and also an endogenous Toll ligand in that it shows a highly specific interaction with TLR4/MD2 . Both these interactions are inhibited by quinoline-3-carboxamides . A clear structure-activity relationship ( SAR ) emerged with regard to the binding of quinoline-3-carboxamides to S100A9 , as well as these compounds potency to inhibit interactions with RAGE or TLR4/MD2 . The same SAR was observed when the compound's ability to inhibit acute experimental autoimmune encephalomyelitis in mice in vivo was analysed . Quinoline-3-carboxamides would also inhibit TNFα release in a S100A9-dependent model in vivo , as would antibodies raised against the quinoline-3-carboxamide–binding domain of S100A9 . Thus , S100A9 appears to be a focal molecule in the control of autoimmune disease via its interactions with proinflammatory mediators . The specific binding of quinoline-3-carboxamides to S100A9 explains the immunomodulatory activity of this class of compounds and defines S100A9 as a novel target for treatment of human autoimmune diseases .
The medical need for novel treatments of human autoimmune/inflammatory disease is high . Quinoline-3-carboxamides ( Q compounds ) have been explored as treatments for autoimmune/inflammatory diseases in humans . They have shown proof-of-concept in clinical trials for the treatment of multiple sclerosis ( MS ) [1–4] and Type I diabetes [5] , and are currently in Phase III clinical development for the treatment of MS [6] and are about to enter Phase II for the treatment of systemic lupus erythematosus ( SLE ) . The target molecule and the mode of action of this class of compounds have remained unknown for over 25 years . Q compounds are unique in that they have a potent effect on disease development in several animal models of autoimmune/inflammatory disease without inducing suppression of adaptive immunity [7–10] . From these studies , it was obvious that the molecular target for Q compounds was novel since no known signalling pathway could explain the experimental data obtained . Furthermore , it appeared likely that the mode of action of Q compounds would be targeting early stages of immune stimulation that could be common for many autoimmune disorders while keeping the immune effector stage intact . S100A9 [11–13] belongs to the family of calcium-binding S100 proteins and has been extensively studied [13–17] . It is expressed in granulocytes and at early stages of monocyte differentiation [14] . Complexes of S100A8 and S100A9 ( S100A8/A9 ) are expressed and released at inflammatory sites [15 , 17] . A correlation between serum levels of S100A8/A9 and disease activity has been observed in many inflammatory disorders [18] . Direct inflammatory activities of the S100A8/A9 proteins include the description of mouse S100A8 as an endogenous ligand of TLR4 [17] , activation of monocytes [17] , and activation of endothelial cells [16 , 19 , 20] . S100A9 has also been detected on the cell surface of murine macrophages at sites of inflammation [21] , but the role of surface-bound S100A9 in immunity and inflammation is still unclear . We present here data that point to a central role for S100A9 in the control of immune responses leading to inflammatory disease .
In order to identify the target molecule of Q compounds , we synthesised analogs of these compounds containing linkers that would facilitate detection of the interaction between these molecules and protein targets ( Figure 1A ) . The molecule was modified as indicated in the R1 and R2 position to create a compound suitable for photoaffinity labelling of proteins ( ABR-216893; the asterisk [*] indicates a 14C atom in this compound ) , or in the R1 position to create a compound ( ABR-225356 ) labelled with FITC . Since no reliable in vitro system has been established for assaying the biological effect of Q compounds , we verified that these linker-containing Q compounds still had biological effect using the in vivo model acute experimental autoimmune encephalomyelitis ( aEAE ) ( unpublished data ) . We also used the FITC-labelled compound ( ABR-225356 ) to investigate binding to human peripheral blood mononuclear cells ( PBMC ) . We observed that only the monocyte ( CD14+ ) fraction was surface stained with ABR-225356 ( unpublished data ) . On the basis of this , we decided to use human peripheral blood monocytes as a source of protein in our effort to isolate quinoline-binding molecules . Human PBMC were separated into CD14+ and CD14− fractions , incubated with 14C labelled ABR-216893 , and photoaffinity labelled . The membrane fraction of both cell populations was subsequently prepared , and the proteins separated on two-dimensional gels followed by autoradiography . Labelled proteins found exclusively on gels from the CD14+ cell fraction were extracted from the gels and identified using MALDI-TOF ( matrix assisted laser desorption/ionization time-of-flight ) . The most prominent binding protein was identified as S100A9 and was selected for further analysis ( Figure 1B ) . In the next step , recombinant human S100A9 was analysed for binding to a defined Q compound ( ABR-215757; currently in clinical development for treatment of SLE ) using surface plasmon resonance ( SPR ) . As shown in Figure 1C , it was evident that recombinant S100A9 bound strongly to ABR-215757 coupled to solid phase . As S100A9 , in most cases , is found colocalised with S100A8 at inflammatory sites , we decided to analyze homo- and heterodimeric complexes of S100A8 and S100A9 for their interaction with Q compounds . Figure 1D shows that binding was more or less exclusively restricted to homodimeric S100A9 , whereas only weak binding was observed for the S100A8/A9 complex , and close to baseline levels for S100A8 . Last , we determined that the Q compound/S100A9 interaction could be competed in a dose-dependent way by free compound ( Figure 1E ) . Additional proteins were identified using photoaffinity labelling but could not be verified using follow-up SPR analysis . We concluded from these studies that human S100A9 was a potential pharmacological target for Q compounds . S100A9 belongs to the S100 family of proteins that are known to be Ca++ binding proteins and are involved in inflammatory processes [18] . However , S100A9 has also been shown to bind Zn++ , and this interaction might have conformational consequences for the protein [22] . We therefore investigated the dependence of the interaction between ABR-215757 and S100A9 for both these divalent ions . When Ca++ or Zn++ were titrated in the presence of a fixed concentration of either Zn++ or Ca++ , we found that both ions were required for S100A9 binding to Q compounds ( Figure 1F ) . If titration was carried out in the absence of either Ca++ or Zn++ , the binding of S100A9 to ABR-215757 was reduced to baseline values ( Figure S5 ) . It should also be noted that the levels of Ca++ or Zn++ required for optimal S100A9/quinoline interaction are within the concentration range found for these ions in human serum [23] . We are aware of the fact that S100A9 is prone to form dimers [16 , 18] and complexes of even higher oligomeric states at high concentrations , and therefore expect the interaction between Q compounds and human S100A9 to be occurring primarily with at least bivalent structures of the molecule . This assumption was supported by the complex kinetics and sigmoidal-shaped dose-response curve for S100A9 binding to immobilised Q compound . The next question was to gain some understanding concerning the mechanism whereby S100A9 binding by Q compounds could inhibit autoimmune disease . Members of the S100 protein family have been shown to interact with the proinflammatory molecule RAGE ( receptor for advanced glycation end products ) [18 , 24] , but to our knowledge , there are no data in the literature showing a direct , physical interaction between RAGE and S100A9 . Furthermore , it has been shown that soluble RAGE may alleviate aEAE [25] . We therefore decided to investigate whether human S100A9 was a human RAGE ligand using SPR . In this study , RAGE was covalently coupled to the sensor chip to allow exposure of the extracellular domain of RAGE to S100A9 , thus reconstituting a biological model in which anchored membrane receptor interacts with soluble ligands . As shown in Figure 2A , S100A9 interacted strongly with immobilised RAGE when injected at the concentration yielding half-maximal binding to ABR-224649 , almost a 6-fold higher response compared to the S100A8/A9 heterodimer . Moreover , the binding of S100A8 to RAGE was negligible . The interaction between S100A9 and RAGE was also strictly dependent on the presence of physiological concentrations of both Ca++ and Zn++ ( Figure 2B ) . Given the similarities between the binding conditions for S100A9 interaction with RAGE and Q compounds , we proceeded to test whether a Q compound could compete for RAGE binding to S100A9 . Indeed , ABR-215757 in increasing concentrations competed for RAGE-S100A9 binding in the presence of Ca++ and Zn++ ( Figure 2C ) . Furthermore , direct binding of ABR-215757 to RAGE was not observed , indicating that the inhibition of the interaction was mediated by binding of ABR-215757 to S100A9 ( unpublished data ) . In a separate experiment in which human RAGE/Fc and Fc alone were allowed to interact with S100A9 immobilised on the chip , we observed no interaction with Fc alone ( Figure S4A ) . Thus , under our standard conditions , homodimeric S100A9 is the primary RAGE ligand ( Figure 2D ) , as well as target for Q compound binding . Having defined S100A9 as a RAGE ligand , we wanted to investigate whether other proinflammatory signalling molecules would also interact specifically with human S100A9 . We had noted that one Q compound had been shown to inhibit lipopolysaccharide ( LPS ) -induced toxic shock [26] . We therefore decided to investigate whether TLR4 could be a possible S100A9 ligand and whether Q compounds could interfere with such interactions . Since TLR4 is inactive in the absence of the coreceptor MD2 , we here used a complex of human TLR4 and MD2 for amine coupling to a biosensor chip to be used in a SPR assay . As shown in the left panel of Figure 3A , S100A9 showed strong binding when injected over immobilised TLR4/MD2 using our standard conditions , and produced a more than 5-fold higher signal than the S100A8/A9 heterodimer . Furthermore , the signal obtained after injection of S100A9 was proportional to the amount of TLR4/MD2 coupled to the solid phase ( Figure S7 ) . We could also demonstrate that the binding of S100A9 to TLR4/MD2 is TLR4 specific since the TLR4/MD2 complex interacted with immobilised S100A9 with high affinity whereas MD2 alone did not ( Figure S4B ) . Hence , we can show here for the first time that human S100A9 is an endogenous TLR4 ligand . The interaction between human S100A9 and TLR4/MD2 was strictly dependent on the presence of both Zn++ ( Figure 3B ) and Ca++ ( unpublished data ) , which could explain why this interaction has not been previously described . We then proceeded to investigate whether the Q compound ABR-215757 could interfere with human S100A9 binding to TLR4/MD2 . As shown in Figure 3C , a dose-dependent inhibition of the interaction was observed , whereas only very weak inhibition was seen with a control substance ( Figure S6 ) . We also wanted to test whether soluble TLR4/MD2 could displace binding of S100A9 to immobilised TLR4/MD2 . Interestingly , TLR4/MD2 , injected together with S100A9 , was not only able to efficiently block the interaction of S100A9 with immobilised TLR4 , but also inhibit the interaction between S100A9 and immobilised Q compound and RAGE , respectively ( Figure 3D ) . This observation indicates that TLR4/MD2 , RAGE , and Q compound compete for the same binding region in human S100A9 . The TLR4/MD2 complex is known to bind LPS , and therefore we investigated whether LPS could interfere with the binding of human S100A9 to the immobilised human TLR4/MD2 receptor complex . In contrast to the dose-dependent displacement of S100A9 binding by soluble TLR4/MD2 , LPS had no effect on this interaction even at 200 ng/ml ( Figure S1A ) . Thus , human S100A9 in the presence of Ca++ and Zn++ can interact specifically with two distinct proinflammatory receptors . With the results above , we had a foundation on which to understand the effect of Q compounds on inflammatory disease in humans . However , these compounds have also shown a broad activity in several disease models in mice [1 , 8–10] . Thus , we needed to validate our findings using mouse proteins . Figure 4A illustrates that very similar results to those obtained with the human proteins were obtained both with regard to mouse S100A9 binding to mouse RAGE , mouse S100A9 binding to Q compound , and mouse S100A9 binding to the mouse TLR4/MD2 fusion protein ( mLPS-Trap [27] ) . Also , all interactions showed similar requirements for Ca++ and Zn++ ( unpublished data ) . Furthermore , the interaction of mouse S100A9 with solid-phase mouse RAGE and mouse TLR4/MD2 could both be competed by the Q compound ABR-215757 ( Figure 4B ) . Analogous to the human S100A9-TLR4/MD2 interaction , soluble TLR4/MD2 displaced mS100A9 binding to immobilised TLR4/MD2 in a manner independent of both LPS and MD2 ( Figure S1B ) . Moreover , as was shown for human S100A9 , homodimeric mouse S100A9 bound much stronger to immobilised Q compound , RAGE and TLR4 than as a heterodimer with S100A8 ( Figure 4C ) . We conclude from this series of experiments that neither the interactions between S100A9 with RAGE and TLR4/MD2 , nor the competition of this interaction by Q compound , are species specific . Having determined that S100A9 interacted specifically with Q compounds , we next wanted to determine whether S100A9 would qualify as a bona fide pharmacological target for the Q compounds . To this end , we selected six compounds ( see Table S1 ) from our chemical libraries of Q compounds [28] and tested these for their binding strength to human and mouse S100A9 and to human and mouse S100A8/A9 heterodimers , their potency in inhibiting the interaction between human and mouse S100A9 and RAGE , and their potency in inhibiting the interaction between human and mouse S100A9 and TLR4/MD2 . Multivariate analytical tools ( principal component analysis [PCA] and partial least squares projections to latent structures [PLS] ) were used to derive the structure-activity relationship ( SAR ) for the binding activity of a series of quinoline compounds to the S100A9 homodimers and the S100A8/A9 heterodimers ( Table S1 ) . When the potency of these compounds in inhibiting aEAE in vivo was directly correlated to their potency in inhibiting the interaction between human S100A9 and human RAGE , an excellent correlation was observed ( R2 = 0 . 98 ) ( Figure 5A ) . We then proceeded to apply PCA modelling to the dataset , i . e . , the five S100A9 and the two S100A8/A9 assays and the aEAE model , a two-component model with R2X = 0 . 97 and Q2 = 0 . 87 , was obtained . The first model dimension reflected as much as 68% of the total variation . The principal component ( PC ) scores revealed differences between the homodimer and the heterodimer . An overall inspection of the loading plot ( Figure 5B ) reveals that the aEAE inhibition ( point 6 ) and the S100A9 homodimer binding ( points 3–5 , 7 , and 8 ) are situated close to each other on the first principal component ( p[1] ) , indicating that strong positive correlations exist among them . On the other hand , the S100A8/A9 heterodimer binding ( points 1 and 2 ) are more distant from the aEAE point , meaning that S100A8/A9 heterodimer binding and aEAE are not strongly correlated . A set of five quinoline derivatives incorporating different substitution patterns at position 5 with relative binding affinities measured to the S100A9 homodimer in the mS100A9–RAGE interaction assay was used to derive the SAR of the binding activity of the quinoline derivatives towards the S100A9 homodimers . The results confirmed that the structural modifications carried out on the 5-position have a profound effect on binding affinity . The PLS evaluation resulted in a three-component model , obtained with cross validation , giving a SAR model with R2Y = 0 . 99 ( 85% + 12% + 2% ) and Q2 = 0 . 81 , which indicates that mS100A9 homodimer binds the quinoline compounds with a high structural selectivity . The observed and predicted half-maximal inhibitory concentration ( IC50 ) values for these compounds for inhibition of mS100A9/RAGE interactions are shown graphically in Figure S2 and very similar results were obtained for all other S100A9 interactions investigated . The analysis pointed out the major importance of steric and hydrophobic factors ( L , B1 , π of the 5-substituent , and the acid strength of the 4-hydroxy group . Furthermore , local electrostatics at positions 4 and 5 were important for the biological activity . In an ensuing step , the SAR model was further tested using an additional quinoline derivative , i . e . , ABR-212662 ( Table S1 ) . This compound was selected based upon its substitution and variation within the activity range , i . e . , being unsubstituted in the 5-position and displaying low binding activity . The observed and predicted binding activities for this compound showed high correspondence and were 1 , 026 and 1 , 235 μM , respectively . Hence , this SAR model is robust and valid for prediction as used . We conclude from the data shown that S100A9 by its SAR to disease inhibition qualify as a pharmacological target molecule for Q compounds . Having shown that S100A9 binding by Q compounds showed a SAR with their activity in inhibiting autoimmune disease , the next step in our investigation was to validate S100A9 as a drug target in vivo . We first considered the obvious experiment of using S100A9 null mice [29] . To this end , we back-crossed the S100A9−/− animals against C57BL/6 mice and induced experimental autoimmune encephalomyelitis ( EAE ) using MOG peptide ( Figure S8 ) . We observed that the S100A9−/− animals had a more severe disease than C57BL/6 controls , but still responded to treatment with Q compounds . This was an unexpected result given the very strong SAR between the binding strength of Q compounds to S100A9 and their potency in inhibiting aEAE ( Figure 5 ) . However , the absence of an obvious functional phenotype with a specific gene deletion does not necessarily prove that the protein it codes for has an insignificant function in an intact host . The S100 family of proteins is large and complex . For example , whereas S100A12 has been shown to be a RAGE ligand in humans [30] , its gene is absent in the mouse genome [31] . S100A8 is expressed almost exclusively as a S100A8/A9 heterodimer , but whereas S100A9−/− mice are viable , the S100A8−/− genotype is embryonically lethal [32] . In addition , S100A9−/− mice show spontaneous alterations of their inflammatory response also in other experimental models [17] . Given that S100A9 convey important biological functions , it can be suspected that biological redundancy may occur in the S100A9−/− animal , in which another molecule ( s ) , maybe from the S100 family , would serve as a ligand for RAGE , TLR4 , and Q compounds . Such a molecule could have very limited function in a genetically intact animal . To be able to perform the in vivo validation of S100A9 as a pharmacological target for Q compounds in wild-type animals , we therefore turned to an alternative approach . Hence , we decided to generate a set of monoclonal antibodies to S100A9 that could compete for S100A9 binding to RAGE and TLR4/MD2 . S100 proteins are rather conserved during evolution [33 , 34] . Assuming that their biological function also has been conserved , one may speculate that it would be difficult to obtain antibodies to key regulatory epitopes using xenoimmunisation . We therefore elected to immunize S100A9−/− mice with recombinant human S100A9 in order to obtain antibodies to novel , potentially functional , epitopes on the S100A9 protein . Approximately 50 S100A9-specific hybridomas were obtained in this experiment , and one of these , 43/8 , was used for further validation . Figure 6 shows the basic features of the 43/8 antibody . It binds both human and mouse S100A9 ( Figure S3A ) . The antibody will also surface stain human monocytes in fluorescence-activated cell sorting ( FACS ) analysis but not as brightly as the S100A8/A9-specific antibody 27E10 ( Figure 6A ) . Fab fragments of the 43/8 antibody ( Figure S3B ) will also inhibit the interaction of S100A9 and RAGE , as well as S100A9 and TLR4/MD2 , showing almost complete inhibition at a concentration of 200 nM ( Figure 6B ) . Very similar results were obtained with intact 43/8 antibody , but not with an isotype control ( unpublished data ) . We could also demonstrate that the epitope recognized by the 43/8 antibody is exposed in an optimal way only in the presence of Ca++ and Zn++ . As is shown in Figure 6C , human S100A9 binds to immobilised intact 43/8 antibody with a more than 10-fold higher signal when injected with Ca++ and Zn++ . Vogl et al . [17] has demonstrated that the induction of systemic TNFα production by LPS is perturbed in S100A9−/− animals . We therefore selected this model for our in vivo validation . C57BL/6 mice were treated with the Q compound ABR-215757 2 h before being challenged intraperitoneally with 3 or 6 μg of LPS . Ninety minutes later , the animals were sacrificed , and the serum TNFα levels were determined . As shown in Figure 7 , Q compound significantly inhibited TNFα production at both levels of LPS challenge , with the effect being most pronounced using the 6-μg challenge . We then proceeded to use the 43/8 Fab in the same assay . As shown in Figure 7 , after challenge with 3 μg of LPS , the TNFα production was significantly inhibited also using 43/8 Fab . We conclude from this experiment that Q compounds , or an antibody Fab fragment that mimics Q compounds in the sense that it inhibits S100A9 interaction with TLR4 and RAGE , can inhibit a biological activity shown to be compromised in S100A9−/− animals [17] . Hence , we consider these data as an in vivo validation of S100A9 as a pharmacological target for Q compounds .
Although the prognosis and clinical management of patients with chronic inflammatory diseases has improved during the last decades , there is still a great medical need for new treatments . Treatments especially that do not compromise immune function and that are suitable for chronic dosing are urgently needed . A group of compounds that fulfils these criteria are the Q compounds that have been in clinical development for over two decades , but whose molecular target and mode of action are unknown . The present investigation defines S100A9 as one molecular target for Q compounds , and their detailed effects on autoimmune disease in mice and humans [1–5 , 7–10] can now be studied in a more rational fashion . Interestingly , the effect of Q compounds resembles the phenotype recently described for the S100A9 knockout model , in that a diminished TNFα response after LPS challenge was observed [17] . That the target molecule for these compounds , S100A9 , interacts with signalling pathways that are early and potent mediators of proinflammatory responses ( RAGE and TLR4 ) could shed some light on the ability of Q compounds to mediate this effect without causing overt suppression of adaptive immunity . It can be speculated that the interference with proinflammatory signalling at the level of antigen-presenting cells may suppress the reactivity of autoimmune T cells . In addition , human S100A9 can now be regarded as a novel therapeutic target for the treatment of autoimmune diseases . The interactions between S100A9 and Q compound , RAGE , or TLR4/MD2 were all strictly dependent on physiological levels of Ca++ and Zn++ . The interaction between S100A9 and Zn++ especially appears to induce a dramatic structural change in the protein , which also was shown to significantly affect the binding of the 43/8 monoclonal antibody . It is interesting to note that Zn++ has been shown to have a profound impact on the structure of other S100 proteins [35] . Also , elevated levels of Zn++ are seen at inflammatory sites , and many extracellular proteins contain Zn++ binding sites [23] . Thus , it can be speculated that the elevation of Zn++ is a feedforward signal for inflammation and acts by inducing conformational changes in proteins and thereby facilitating novel interactions . That both RAGE and TLR4/MD2 interact with the same surface on human S100A9 and are competed by Q compounds is intriguing . We have investigated several of the mouse and human TLRs for binding to the same interphase on human S100A9 without finding additional targets ( unpublished data ) . However , we expect that other proinflammatory molecules will eventually be shown to interact with the same molecular surface on human S100A9 , and are also open for the idea that other forms of S100 protein combinations may bind to proinflammatory mediators . The common theme remains that a molecular form of S100 proteins can interact with several proinflammatory mediators as a mechanism to modulate the quality of the immune response and inflammatory reactions . At first glance , the data presented here are in conflict with previously published data [17] . In this study , biosensor experiments were conducted with recombinant murine S100A8 immobilized on the chip using amine coupling . Binding to murine TLR4/MD2 fusion protein ( mLPS-Trap ) was demonstrated , as was the ability of murine S100A8 , but not S100A9 , to stimulate TNFα production of bone marrow cells from wild-type mice . In the present study , however , S100 proteins were injected over a surface with immobilized TLR4 to preserve the Ca/Zn conformation of S100A9 that is a prerequisite for binding activity . Under these conditions , S100A8 only showed weak interaction with TLR4 . The biological reason for this discrepancy may be explained by the fact that the biological function of S100A8 and S100A9 is regulated in a complex manner , which additionally may differ between mice and men . For example , human S100A9 activates integrin affinity of CD11b/CD18 on monocytes , whereas human S100A8 has no effect [36] . Vice versa , murine S100A8 activated murine macrophages whereas murine S100A9 was inactive . Regulatory effects of human S100A9 on tubulin metabolism are completely abrogated by phosphorylation of S100A9 at threonine 113 [37] . This MAPK p38-dependent phosphorylation site is not conserved in murine S100A9 . It seems therefore likely that murine S100A9 may mediate so far undefined regulatory mechanisms in vivo , which may be responsible for the discrepancy between different experimental findings between mice and men in vivo and in vitro . Activation of the innate immune system is crucial for initiation and amplification of many inflammatory responses and autoimmune diseases . During this process , endogenous danger signals called alarmins or damage-associated molecular patterns ( DAMPs ) play a pivotal role via interaction with specific pattern-recognition receptors [38] . S100A8 and A9 have been identified as important endogenous DAMPs due to their activation of TLR4 [14 , 17] . Thus , specific blocking of S100 proteins , as presented here , represents the first report of targeted intervention with a DAMP-mediated inflammatory process , which has already shown pharmacological activity in mice and men [4 , 10] . S100A8 and S100A9 are two members of the S100 protein family . Multivariate analytical tools were used to derive the SAR for the binding activity of a series of Q compounds towards the S100A9 homodimer and the S100A8/9 heterodimer , with the assumption that similar analogs bind to the same binding site in a similar binding mode . The results indicate that the Q compounds bind the S100A9 homodimers with high structural selectivity and that this binding showed a strong correlation to their ability to inhibit autoimmune disease . On the other hand , the correlation between Q compound binding to S100A8/A9 heterodimers and inhibition of autoimmune disease was poor . The bulk of S100A8 and S100A9 protein is expressed as S100A8/A9 heterodimers and most of this protein is found as soluble protein in serum . S100A8 and S100A9 are also expressed on the cell surface of monocytes [12 , 15] . Whether the pharmacological activity of Q compounds is primarily mediated by blocking soluble or membrane-bound S100A9 will be a subject for future studies .
Murine and human S100A8 , S100A9 , S100A8/A9 , and human S100A12 were either produced recombinantly in Escherichia coli or purified from granulocytes; essentially as described [39] . Mouse TLR4/MD2 fusion protein ( mLPS-Trap ) was obtained through collaboration [27] . Carrier-free recombinant human RAGE/Fc ( hRAGE ) , human IgG1Fc ( hFc ) mouse RAGE/Fc ( mRAGE ) , human TLR4/MD-2 complex ( hTLR4/MD-2 ) , human MD-2 , mouse anti-hRAGE ( clone #176902 ) , mouse anti-hTLR4 ( clone #285219 ) , and rat anti-mTLR4 ( clone #267518 ) were purchased from R&D Systems . The mouse anti-human S100A9 monoclonal antibody ( clone 43/8 ) was produced in-house by immunisation of S100A9−/− mice . LPS from E . coli was obtained from Sigma . Protein concentration was determined using the microtiter plate BCA assay from Pierce with bovine serum albumin as standard , or by absorbance measurement at 280 nm using molar absorption coefficient . Biotinylation of the 43/8 monoclonal antibody was made using the NHS-LC-biotin reagent from Pierce . The antigen binding fragment ( Fab ) of mouse anti-human S100A9 monoclonal antibody 43/8 was prepared by enzymatic digestion on immobilized ficin using the mouse IgG1 Fab preparation kit from Pierce . Gel electrophoresis under denaturing conditions was run on 4%–12% Bis-Tris NuPAGE gels with MES-SDS as running buffer ( Invitrogen ) . For details on the synthesis and features of quinoline compounds , see Jönsson et al . [28] and references therein . The following antibodies and second steps reagents were used for surface stain of human PBMC , CD14-APC , mouse IgG1 ( BD Biosciences Pharmingen ) , 27E10-FITC ( BMA Biomedicals ) , Streptavidin-Alexa Fluor 488 ( Invitrogen ) , and biotinylated 43/8 monoclonal antibody . Stained cells were analyzed by flow cytometry on a FACSCalibur ( Becton Dickinson ) using CellQuest software . For protein isolation , PBMC were divided into CD14+ and CD14− populations using positive selection of CD14+ cells with magnetic beads ( Miltenyi Biotec ) . Both cell types were incubated with ABR-216893 in the dark on ice after which the cells were exposed for a light source for 30 min , lysed , and protein extracts prepared as described [40] . The proteins were subsequently subjected to conventional two-dimensional gel analysis and autoradiography . Radioactive spots that were present selectively in extracts from CD14+ cells were isolated , the proteins eluted , trypsin digested , and prepared for analysis in a Bruker Reflex III instrument ( Bruker Daltonik ) using protocols and software supplied by the manufacturer . TNFα induction after intraperitoneal challenge with LPS was performed essentially as described [17] . In brief , mice were pretreated for 2 h with 10 mg/kg ABR-215757 or PBS , after which 3 or 6 μg of LPS was injected intraperitoneally . After an additional 90 min , the animals were sacrificed , and the level of TNFα in blood was determined using commercial TNFα antibodies ( eBioscience; http://www . ebioscience . com ) . S100A9−/− mice ( 10 wk of age ) were injected intraperitoneally with 100 μg of recombinant human S100A9 precipitated in alum . Six weeks later , the mice were boosted with the same dose of antigen and the spleen cells fused to SP2/0 5 d later . S100A9 reactive clones were selected using ELISA , and positive clones were subcloned by limiting dilution . The SPR analysis was carried out with the Biacore 3000 system ( GE Healthcare ) . Sensor chips , amine coupling kit , immobilization and running buffers , and regeneration solutions were obtained from GE Healthcare . Working solutions of all reagents used for Biacore analysis were prepared in 0 . 01 M Hepes , 0 . 15 M NaCl ( pH 7 . 4 ) containing 0 . 005% v/v Surfactant P-20 ( HBS-P; GE Healthcare ) by buffer exchange on Fast Protein Desalting Micro-Spin Columns from Pierce . ABR-215757 was immobilised onto a CM5 chip through an amino-linker ( ABR-224649 ) . Other reagents ( i . e . , RAGE , TLR4/MD2 , and various antibodies ) were immobilised to the aimed density using random amine coupling chemistry . Activity of ligands after immobilisation was tested by injecting specific antibodies ( unpublished data ) . In some experiments , S100A9 was immobilized either by random amine coupling or with a known orientation ( i . e . , by sulhydryl group conjugation to the only cysteine in S100A9 at position 3 ) . Binding to the various surfaces was performed by injecting the analyte at a flow rate of 30 μl/min in a physiological buffer ( HBS-P ) containing 1 mM Ca2+ and 10 μM Zn2+ as proposed for S100A8/9 by Robinson et al . [16] . A typical analysis cycle consists of: ( 1 ) pumping running buffer for 1 min to obtain a stable baseline; ( 2 ) injection of sample for an appropriate period of time ( association ) ; ( 3 ) pumping running buffer for 2 . 5 min ( dissociation ) ; ( 4 ) injection of a short pulse ( 30 s ) of 15 μl 10 mM glycine-HCl ( pH 2 . 0 ) ( regeneration ) ; and ( 5 ) pumping running buffer for 2 min ( stabilisation after regeneration ) at a flow rate of 30 μl/min . As S100A9 is a calcium-binding protein and shown to require low concentrations of Zn2+ to adapt a biologically active conformation [16] , titration of Zn2+ and Ca2+ for optimal binding of S100A9 to immobilised ABR-224649 , RAGE , and hTLR4/MD2 was performed . In one experiment , S100A9 was injected into a buffer with a fixed Ca2+ concentration ( 1 mM ) and Zn2+ in the range 0–50 μM . In a second experiment , Ca2+ was varied from 0–2 mM at a fixed Zn2+ concentration ( 10 μM ) . In subsequent analyses , regeneration was carried out under more mild conditions , i . e . , by injecting 30 μl of HBS-P containing 3 mM EDTA ( HBS-EP; GE Healthcare ) for 60 s , to prolong the lifespan of the chip . In order to study displacement of S100A9 binding to immobilised Q compound , RAGE , and TLR4/MD2 , S100A9 at a concentration yielding approximately half-maximal binding was incubated in the absence or presence of serially diluted Q compounds . Compounds were also injected over the immobilised surfaces in the absence of S100A9 to exclude , or make possible correction for , any direct binding of compound to the surface . Evaluation was carried out using BIAevaluation Software version 3 . 2 ( GE Healthcare ) . The response at steady-state was obtained by fit of sensorgrams to standard binding models , where appropriate , or calculated as responses at late association or early dissociation phase using the Steady state affinity function in BIAevaluation . Affinity was determined from kinetic analysis ( on- and off-rates ) or as the apparent affinity after plotting responses versus concentration of analyte in a saturation curve . In the inhibition assay format , the competitor concentration yielding 50% inhibition ( IC50 ) was calculated by fitting data to a one-site competition model in GraphPad Prism . Multivariate analytical tools ( PCA and PLS ) were used to derive the SAR for the binding activity of a series of quinoline compounds to the S100A9 homodimer and the S100A8/A9 heterodimer ( Table S1 ) . The software SIMCA-P+ 11 ( Umetrics AB; http://www . umetrics . com ) was used to conduct the multivariate data analysis . A number of physicochemical descriptors for size , lipophilicity , and electronic characteristics were used to correlate structural or property descriptors of the compounds with their biological activities . The 5-substituents of the quinoline compounds were described by two dimensionally–based structure descriptors , i . e . , STERIMOL parameters ( L , B1 , and B5 ) as steric parameters , and the substituent constant π as a hydrophobic parameter . The experimentally determined and assigned carbon 13C-NMR chemical shifts of atoms from positions 3 to 10 on the quinoline template were used to reflect local electrostatics . There were only minor 13C-NMR shift differences between carbons in positions 2 , 11 , and 1′-4′ , and the 13C shifts of these latter atoms were not used when establishing the SAR models . The acidity constants ( pKa ) in water of the corresponding ortho-substituted benzoic acid derivatives were used to correlate structure and acid strength of the 4-hydroxy group . The steric and hydrophobic parameters used were 2D-based structure descriptors known from the literature [41] . 13C NMR spectra were recorded with an operating frequency of 125 . 8 MHz . Spectra were recorded in D2O with a small addition of NaOD at ambient temperature . The shift scale was referenced to 3- ( trimethylsilyl ) -propane sulfonic acid Na-salt ( TSPSA ) defined as 0 . 00 ppm . Signals from two rotameric forms in equilibrium ( E/Z isomerism ) were obtained from the anion form of the compounds , and only the major form was used . A training set of five quinoline derivatives with structural diversification performed at position 5 of the quinoline ring system was used for the SAR . PCA was used to uncover any relationship between the binding activities of the quinoline derivatives at the S100 proteins and the inhibitory effect of these derivatives in the aEAE model . PLS was then used to model the relationship between the physiochemical descriptors used to characterize the compounds and their biological responses . The PCA included the binding affinities towards murine and human S100A9 homodimers , murine S100A8/A9 heterodimer , and 50% effective dose ( ED50 ) values from an aEAE mice model . The PLS analysis included binding affinity from the murine S100A9 homodimer and a total of 13 physicochemical variables used to describe the same set of five compounds . All variables were mean centred and scaled to unit variance . | What molecules and mechanisms underlie the development of autoimmune diseases such as multiple sclerosis , rheumatoid arthritis , and systemic lupus erythematosus are largely unknown . To gain some insight into the process , we use a class of chemical compounds , quinoline-3-carboxamides ( Q compounds ) , which modify disease in both experimental animal models and in clinical trials , but whose target ( s ) have been elusive . We show that these Q compounds bind to a molecule called S100A9 that is expressed on the surface of various monocyte populations in the peripheral blood . Furthermore , we show that Q compounds inhibit the interaction of S100A9 with two well-known proinflammatory receptors ( the Toll-like receptor 4 [TLR4] and receptor of advanced glycation end products [RAGE] ) . We provide a missing piece to the puzzle in that we identify S100A9 as a target of Q compound drugs and identify a new mechanism where S100A9 promotes inflammation at early stages of immune activation and thereby a role in the development of autoimmune disease . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
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"Methods"
] | [
"biochemistry",
"neurological",
"disorders",
"chemistry",
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] | 2009 | Identification of Human S100A9 as a Novel Target for Treatment of Autoimmune Disease via Binding to Quinoline-3-Carboxamides |
A subset of patients with stable asthma has prominent neutrophilic and reduced eosinophilic inflammation , which is associated with attenuated airways hyper-responsiveness ( AHR ) . Haemophilus influenzae has been isolated from the airways of neutrophilic asthmatics; however , the nature of the association between infection and the development of neutrophilic asthma is not understood . Our aim was to investigate the effects of H . influenzae respiratory infection on the development of hallmark features of asthma in a mouse model of allergic airways disease ( AAD ) . BALB/c mice were intraperitoneally sensitized to ovalbumin ( OVA ) and intranasally challenged with OVA 12–15 days later to induce AAD . Mice were infected with non-typeable H . influenzae during or 10 days after sensitization , and the effects of infection on the development of key features of AAD were assessed on day 16 . T-helper 17 cells were enumerated by fluorescent-activated cell sorting and depleted with anti-IL-17 neutralizing antibody . We show that infection in AAD significantly reduced eosinophilic inflammation , OVA-induced IL-5 , IL-13 and IFN-γ responses and AHR; however , infection increased airway neutrophil influx in response to OVA challenge . Augmented neutrophilic inflammation correlated with increased IL-17 responses and IL-17 expressing macrophages and neutrophils ( early , innate ) and T lymphocytes ( late , adaptive ) in the lung . Significantly , depletion of IL-17 completely abrogated infection-induced neutrophilic inflammation during AAD . In conclusion , H . influenzae infection synergizes with AAD to induce Th17 immune responses that drive the development of neutrophilic and suppress eosinophilic inflammation during AAD . This results in a phenotype that is similar to neutrophilic asthma . Infection-induced neutrophilic inflammation in AAD is mediated by IL-17 responses .
Asthma is a complex disease of the airways that is generally characterized by symptoms of wheeze , cough , breathlessness and airway inflammation . While eosinophilic inflammation has been considered to be the hallmark of airway inflammation in asthma [1] , [2] , it is present in only 50% of asthmatics [3] . Non-eosinophilic asthma has now been described in persistent [4] , [5] and severe asthma , [6] as well as in steroid naïve asthma [7] . Further investigation of the non-eosinophilic subtype has identified a subgroup with an intense neutrophilic bronchitis [5] , [8] with increased interleukin ( IL ) -8 [4] . Compared to eosinophilic asthmatics , neutrophilic asthmatics have reduced eosinophilic inflammation and AHR . Furthermore , they are frequently resistant to corticosteroid treatment , which results in a significant proportion of asthma-related health care costs [5] , [8] , [9] , [10] , [11] , [12] . IL-17 is also elevated in asthma and other obstructive airway diseases that are characterized by increased neutrophils [13] , [14] , [15] , [16] . IL-8 and IL-17 are important mediators of neutrophilic inflammation during infection and in disease states [4] , [12] , [13] , [17] , [18] and their elevated expression in neutrophilic asthma correlates with increased levels of neutrophils in sputum [15] . IL-8 is a potent neutrophil chemoattractant , produced by macrophages , lymphocytes , epithelial cells and neutrophils [19] , [20] . IL-17 is produced by several cells including Th17 cells [21] , [22] , [23] , γδ T cells [24] , [25] , neutrophils [26] , and macrophages [27] , [28] . IL-17 has critical roles in host defence against bacterial infections [29] , [30] , [31] , [32] , suggesting a potential role in the pathogenesis of bacterial-induced neutrophilic asthma . Chronic bacterial colonization is evident in the airways of patients with neutrophilic asthma [12] and is also associated with an intense neutrophilic bronchitis in asthma [33] . H . influenzae is a common bacterium of the respiratory tract , is one of the bacteria most frequently isolated from the airways of neutrophilic asthmatics [12] , [33] , and often causes recurrent respiratory disease [34] , [35] , [36] in those with compromised airways . Nevertheless , how H . influenzae is associated with the pathogenesis of neutrophilic asthma is unknown . Specifically , whether infection promotes the pathogenesis of neutrophilic asthma , or if neutrophilic asthmatics are predisposed to infection is not known . In this study we used murine models of H . influenzae infection and OVA-induced allergic airways disease , which mimics hallmark features of human asthma , to elucidate the potential association between infection and the development of neutrophilic asthma .
In order to investigate the association between H . influenzae lung infection and asthma we first established and characterized a murine model of NTHi lung infection alone . Inoculation intratracheally ( i . t . ) with 5x105 CFU of NTHi resulted in a mild respiratory infection that induced inflammatory responses but did not significantly affect lung function ( Figure 1 ) . Bacterial numbers in bronchoalveolar lavage fluid ( BALF ) and lung homogenates peaked at 5 days and were cleared 16 days after inoculation ( Figure 1A ) . NTHi infection induced airway inflammation . Neutrophil influx into the airways peaked at 24 hours post-infection while lymphocytes and eosinophils were significantly increased at 5 days . Neutrophil numbers returned to baseline after 5 days , while lymphocyte and eosinophil numbers returned to baseline after 10 days post-infection ( Figure 1B ) . Infection also induced significant but low level increases in NTHi-induced IL-5 , IL-13 , IL-17 and IL-22 , and higher levels of IFN-γ release from mediastinal lymph nodes ( MLN ) cultures after 5 days , which returned to baseline levels after 26 days ( Figure 1C ) . Infection did not affect lung function , with no changes in AHR ( dynamic compliance or transpulmonary resistance in response to increasing doses of methacholine ) compared to sham infected ( Saline ) controls 5 , 16 and 26 days after inoculation ( Figure 1D–E ) . To investigate the effect of infection on AAD in sensitized animals , groups were infected during ( d0 NTHi+OVA ) or 10 days after ( d10 NTHi+OVA ) OVA sensitization ( Figure 2A ) , and AAD assessed on day 16 . Infection suppressed OVA-induced T cell cytokine responses , inflammatory cell influx and AHR in AAD ( Figure 2 ) . The development of AAD ( OVA groups ) resulted in increased OVA-induced release of IL-5 , IL-13 and IFN-γ from MLN and splenic T cells , eosinophilic inflammation and AHR ( decreased compliance and increased resistance in response to methacholine ) , compared to uninfected , nonallergic ( Saline ) controls ( Figure 2B–F and Figure 3 ) . Infection during ( d0 NTHi+OVA ) or after ( d10 NTHi+OVA ) sensitization resulted in significant reductions in OVA-induced IL-5 , IL-13 and IFN-γ release from MLN T cells ( Figure 2B ) , compared to uninfected , allergic ( OVA ) controls . Infection also significantly reduced the numbers of total cells and eosinophils in the airways and blood ( Figure 2C-D ) . The reduction in eosinophils correlated with the reduced release of IL-5 from MLN T cells . Infection significantly suppressed , but did not abolish AHR in AAD . Infection during sensitization ( d0 NTHi+OVA ) had no effect on compliance , but significantly reduced resistance of the lungs . However , infection after sensitization ( d10 NTHi+OVA ) , significantly suppressed both compliance and resistance ( Figure 2E–F ) . Notably , compliance remained decreased in infected , allergic ( NTHi+OVA ) groups compared to uninfected , nonallergic ( Saline ) controls . Infection during ( d0 NTHi+OVA ) or after ( d10 NTHi+OVA ) sensitization had no effect on systemic IL-5 ( Figure 3A ) but significantly reduced systemic IL-13 and IFN-γ release from splenocytes ( Figure 3B–C ) , compared to uninfected , allergic ( OVA ) controls . To determine if Tregs were involved in the suppression of AAD , Tregs , TGF-β and IL-10 were quantified on day 16 of the model . TGF-β and IL-10 are critical immunosuppressive factors that are produced by Tregs . NTHi infection during and after sensitization did not alter the numbers of Tregs ( Figure 4A ) in the lung , compared to uninfected allergic controls . Notably , infection decreased the expression of TGF-β ( Figure 4B ) and IL-10 ( Figure 4C ) in lung tissue in infected , allergic groups compared to uninfected , allergic controls . To determine if alterations in antigen-presenting cells were involved in the suppression of AAD , the effect of infection on MHCII and CD86 expressing DCs was also investigated ( on day 16 ) . The development of AAD resulted in increases in the numbers and proportions of MHCII expressing plasmacytoid DCs ( pDCs ) and myeloid DCs ( mDCs ) , and CD86 expressing MHCII+ pDCs and mDCs in MLNs and lungs ( Figure 5A–H ) , compared to uninfected , nonallergic controls . Infection during or after sensitization resulted in significant decreases in MHCII and CD86 expressing pDCs and mDCs compared to uninfected , allergic controls ( Figure 5A–H ) . We then assessed the effects of infection on other features of AAD ( on day 16 ) . Significantly , whilst eosinophilic inflammatory responses were suppressed by infection during AAD , NTHi infection induced AAD with an enhanced neutrophilic inflammatory profile . The development of AAD resulted in an increase in neutrophil influx into the airways ( Figure 6 ) . Infection during or after sensitization resulted in a two-fold increase in neutrophil recruitment in BALF compared to uninfected , allergic controls . Moreover , infected allergic groups had a four-fold increase in neutrophil recruitment compared to groups with infection alone ( i . e . infected , nonallergic groups ) at the same time point after infection ( i . e . 16d and 5d after infection , Figure 6 ) . These results demonstrate that the combination of infection with AAD results in enhanced neutrophilic inflammation . Collectively , our results show that NTHi infection in AAD may induce a phenotype of neutrophilic AAD that resembles neutrophilic asthma in humans . Neutrophilic inflammation in asthma has been linked with increased IL-17 expression and IL-17 has been shown to be involved in neutrophil recruitment in response to bacterial infection . Therefore , the effects of infection on IL-17 responses during infection-induced neutrophilic AAD were further investigated . The experiments described hereafter were performed with infection during ( d0 NTHi+OVA ) OVA sensitization . The profile of neutrophil influx into the airways and IL-17 production was determined in lung tissue and MLNs during the development of infection-induced neutrophilic AAD ( 1 , 5 , 12 and 16d , Figure 7A ) . The development of AAD resulted in increases in neutrophilic influx into the airways on day 12 and 16 , and had minimal effects on IL-17 responses . Significantly greater numbers of neutrophils were recruited into the airways during infection and OVA sensitization ( 1d ) and after OVA challenge ( 16d ) in infected , allergic compared to uninfected , allergic groups ( Figure 7B ) . Importantly , increases in the infection-induced neutrophil influx were accompanied by significant increases in IL-17 responses in pulmonary tissue and MLN T cells . The expression of IL-17 mRNA in lung tissue was significantly elevated 1 day after infection in infected , allergic groups ( Figure 7C ) and returned to baseline levels by day 12 , immediately prior to OVA challenge . Expression again significantly increased on day 16 , after OVA challenges . NTHi-induced IL-17 release from MLN T cells was also increased from days 5 to 16 in infected , allergic compared to uninfected , allergic groups ( Figure 7D ) . Interestingly , infection did not affect OVA-induced IL-17 release ( Figure 7E ) . Taken together , these data demonstrate that infection induces increased IL-17 responses in lung tissue and MLNs that correlate with elevated airway neutrophil numbers in infection-induced neutrophilic AAD . IL-17 can induce neutrophilic inflammation by enhancing the expression of the chemotactic factor IL-8 . Therefore , the mRNA expression and protein levels of KC and MIP2 , the mouse orthologs of IL-8 , in lung tissue were also investigated . KC and MIP2 mRNA and protein were elevated 1 day after infection and OVA sensitization in the lungs of infected , allergic , compared to uninfected , allergic groups , ( Figure 8A–B ) . There were no differences in mRNA expression between groups at later time points . Therefore , the early induction of neutrophil influx into the lung during the development of infection-induced neutrophilic AAD is associated with early increases in neutrophil chemokine responses during infection . To investigate the mechanisms that underpin infection-induced neutrophilic AAD , we assessed the potential cellular sources of IL-17 and the role of adaptive and innate immune cells in its release . The development of AAD resulted in modest increases in IL-17 factors and responses ( Figure 9A–E ) . ROR-γt , the Th17 differentiation factor was assessed , and expression was significantly elevated after 12 and 16 days in the lungs of infected , allergic , compared to uninfected , allergic groups ( Figure 9A ) , suggesting that there was enhanced Th17 polarization . The numbers and proportions of T cells that were Th17 cells in lung tissue and MLNs were then determined by flow cytometry . CD3+CD4+IL-17+ ( Th17 ) cells were significantly increased in the lungs after 12 and 16 days in infected , allergic groups ( Figure 9B–C ) , but in the MLNs were increased only at day 5 ( Figure 9D–E ) . These results indicate that Th17 cells in the lungs and MLNs may be the potential adaptive immune source of IL-17 after day 5 . We then assessed which cells were the early innate sources of IL-17 on day 1 . Increased numbers and proportions of pulmonary macrophages ( Figure 10A–B ) and to a lesser extent neutrophils ( Figure 10C–D ) produced increased amounts of IL-17 at early but not other time-points in infected , allergic groups compared to uninfected , allergic controls . Lung macrophages and neutrophils isolated on day 1 also had increased levels of IL-17 mRNA transcripts ( Figure 10E–F ) . Taken together these results demonstrate that infection induces early IL-17 responses from lung macrophages and neutrophils and later responses from Th17 cells in lungs and MLNs that are associated with neutrophil influx into the airways . We have shown that neutrophilic inflammation in infection-induced neutrophilic AAD correlates with increased expression of IL-17 during OVA challenge . To determine whether infection-induced neutrophilic inflammation is mediated by IL-17 , IL-17 was depleted in infected , allergic groups during AAD , by administration of anti-IL-17 monoclonal antibody during OVA challenge on days 11 and 13 ( Figure 11A ) , and AAD assessed ( on day 16 ) . This approach has previously been shown to deplete IL-17 in vivo [21] . Importantly , IL-17 depletion significantly reduced the numbers of neutrophils in the BALF compared to isotype treatment of infected , allergic groups ( Figure 11B ) . Significantly , neutrophil numbers were not different to those observed in uninfected , allergic groups , which were unaffected by treatment . IL-17 depletion also significantly reduced KC mRNA expression levels in the lung , but had no effect on MIP2 mRNA ( Figure 11C ) . Anti-IL-17 treatment of infected , allergic groups also partially restored IL-5 ( 3 . 198±0 . 679 ng/ml in anti-IL-17 compared to 1 . 576±0 . 238 ng/ml in isotype-treated infected allergic groups p<0 . 01 ) and IL-13 ( 18 . 240±1 . 533 ng/ml in anti-IL-17 compared to 11 . 988±0 . 938 ng/ml in isotype-treated infected allergic groups p<0 . 01 ) responses , but had no affect on IFN-γ release . These results demonstrate that infection-induced IL-17 release is responsible for neutrophil influx into the airways and the induction of neutrophilic AAD .
In this study we have demonstrated for the first time that H . influenzae respiratory infection drives IL-17-mediated development of neutrophilic AAD . NTHi infection suppressed pulmonary and systemic eosinophilic inflammation and reduced Th2 cytokine responses and AHR in AAD . However , infection induced neutrophilic inflammation during AAD , by promoting early ( innate ) and late ( adaptive ) IL-17 responses from pulmonary macrophages and Th17 cells , respectively . This indicates that H . influenzae infection may modulate immune responses in asthmatics that promote the development of neutrophilic asthma . NTHi is commonly isolated from the nasopharynx of healthy individuals , but is also associated with chronic airway diseases such as bronchiectasis [37] , COPD [38] , and chronic bronchitis [39] . NTHi is the bacterium most commonly isolated during COPD exacerbations , and NTHi strains isolated during these exacerbations induce higher levels of IL-8 , and subsequent neutrophil recruitment to the airways , than colonizing strains [40] . Simpson and colleagues have recently demonstrated that a large proportion of neutrophilic asthmatics are colonized with H . influenzae , have increased innate immune activation , and 6-8 fold higher endotoxin levels compared to other asthma subtypes and healthy controls [12] . A more recent study has demonstrated that 41% of neutrophilic asthmatics assessed had a significant load of potentially pathogenic bacteria , and H . influenzae was identified in 60% of patients that tested positive for these bacteria [33] . We have extended these studies to show that H . influenzae may promote neutrophilic asthma by suppressing Th2-mediated responses that are associated with alterations in antigen-presenting cells , and by inducing potent neutrophilic inflammation that is driven by Th17 responses . We show that infection during and after sensitization inhibits characteristic features of eosinophilic asthma . Irrespective of the time of inoculation , infection significantly reduced both local and systemic allergen-induced cytokine release from MLNs and splenocytes , as well as airway and blood eosinophil recruitment . All of these effects may lead to the suppression of AHR . Tregs are an important cell involved in immune tolerance and the suppression of inflammation [41] . We show that Treg numbers were not changed by infection , and TGF-β and IL-10 expression in the lung , which are involved in the suppression of inflammation by Tregs , were reduced by infection in AAD . These results suggest that Tregs are not involved in the suppression of cytokines or cellular inflammation . The role of infection on DC function was investigated as DCs play an integral role in the uptake and presentation of antigen to naïve T cells , and as a result direct immune responses [42] . Infection significantly decreased markers associated with antigen presentation and co-stimulation of DCs . Therefore , infection is able to alter the phenotype of antigen-presenting cells , which may affect the interaction between APCs and T cells , and result in attenuated adaptive responses to allergen . By contrast , NTHi infection induced potent neutrophilic inflammation in the airways . Persistent airway neutrophilia is also a feature common to chronic airway diseases , such as COPD [38] , chronic bronchitis [39] and bronchiectasis [43] , where recurrent infection is known to play an important role in pathogenesis . Neutrophilic inflammation is often associated with acute asthma exacerbations , and in particular infection-mediated exacerbations . Indeed , several studies have shown increased neutrophilic inflammation in both viral and bacterial infection-induced exacerbations [44] , [45] , [46] . Here we show that NTHi induces strong neutrophilic inflammatory responses , and may be involved in the development of neutrophilic asthma through the induction of neutrophilic inflammation . We demonstrate that immune responses that lead to the development of NTHi-induced neutrophilic AAD occur in two phases . The first involves innate immune activation during infection that is likely to result in neutrophil chemoattraction to the airways . NTHi infection during OVA sensitization resulted in a significant neutrophil influx to the airways 1 day after infection , a two-fold increase compared to NTHi infection alone . This correlated with the early production of KC , MIP2 and IL-17 in the lungs . These neutrophils and to a greater extent macrophages were able to produce significantly more IL-17 than those from infected , nonallergic and uninfected allergic controls . Therefore these cells , particularly macrophages , may be sources of early ( innate ) IL-17 release . This observation may have important implications for other diseases where the innate source of IL-17 has not yet been identified . The second phase involves adaptive immune responses during allergen challenge resulting in increased infection-mediated Th17 responses . During the challenge phase , days 12-15 , there was a significant upregulation of ROR-γt and IL-17 mRNA in the lungs of infected allergic groups compared to infected , nonallergic and uninfected allergic controls . These results directly correlated with increases in Th17 cells in the lungs of infected , allergic groups . The increased production of IL-17 from T cells in conjunction with increases in ROR-γt expression suggest that infection drives Th17 responses that preferentially induce IL-17 production and neutrophilic inflammation during subsequent allergen challenge . Collectively , our data suggest that infection induces early responses , involving neutrophils , KC , MIP2 and IL-17 expression that may prime the host for enhanced Th17-mediated neutrophilic responses upon later allergen challenge , which subsequently induces neutrophilic AAD . Our findings are consistent with data from a recent study by Bullens et al . , which showed that increased IL-17 responses in asthmatics correlate with increased neutrophil numbers in sputum [15] . Hellings et al . , [21] demonstrated that IL-17 is important in lung neutrophil recruitment in response to an allergen , while Ye et al . , [29] showed that IL-17 responses and signalling through the IL-17R is vital for neutrophil recruitment and host defence against Klebsiella pneumoniae infection . Here we extend these findings by demonstrating that H . influenzae infection-induced IL-17 responses in AAD may play a role in driving neutrophilic inflammation in asthma . We recently demonstrated that chlamydial respiratory infection is also able to drive neutrophilic asthma [47] . Chlamydial infection suppressed Th2-mediated eosinophilic inflammation and promoted neutrophilic inflammation and AAD . Neutrophilic asthmatics are resistant to corticosteroid treatment , which is the mainstay of asthma therapy [10] , and with evidence that asthmatics with infection are more resistant to steroids than asthmatics with no infection [48] , alternative therapies are needed for infection-induced neutrophilic asthma . It is possible that either infection alone or the synergistic effects of infection and AAD are required for the induction of the neutrophilic AAD phenotype . NTHi infection alone did increase neutrophil influx into the airways ( p<0 . 01 ) , IL-17 mRNA expression in the lung ( p<0 . 05 ) and the percentages of lung macrophages and neutrophils producing IL-17 ( both p<0 . 001 ) 1 day after infection , compared to uninfected , nonallergic ( Saline ) controls . These effects would be expected as they are normal responses to infection; however , they may contribute to the establishment of a pro-neutrophilic environment and the subsequent development of neutrophilic AAD upon allergen challenge . However , NTHi infection does not persist past 10 days , and yet is able to modify AAD after 16 days . It is , therefore , likely that the major affect of the infection is to induce persistent immune changes , that continue even after the clearance of the infection , that synergize with allergen exposure to drive neutrophilic AAD . This mechanism has recently been proposed in humans [49] . OVA sensitization at the time of , or prior to , infection , and subsequent OVA challenge does not affect NTHi load . When infection occurs during sensitization , bacteria are still cleared by day 16 , and; when infection occurs 10 days after sensitization , bacterial recovery at the end of the protocol ( i . e . 5 days later ) , is the same as that in the infected , non-allergic group ( data not shown ) . It is likely that the induction of a neutrophilic phenotype is specific to a subset of infectious agents , particularly NTHi and Chlamydia [12] , [33] , [50] . In other studies we have investigated the impact on AAD of an unrelated respiratory pathogen and commensal bacterium , Streptococcus pneumoniae , which is not associated with neutrophilic asthma . We show that S . pneumoniae infection or components do not induce changes in IL-17 responses or increase neutrophilic inflammation with infection or component administration either before , during or after the induction of AAD [51] , [52] . Both NTHi- and Chlamydia-induced neutrophilic AAD , may potentially be driven by conserved pathogen-associated molecular patterns ( PAMPs ) , such as lipopolysaccharide ( LPS ) or CpGs . Numerous studies have investigated the effects of LPS and CPG in AAD , and the interactions are complex . LPS is highly variable and different types and levels of LPS have different effects in AAD . Low dose LPS administration during sensitization promotes Th2 responses and is a risk factor for severe asthma [53] , [54] , while high doses decrease Th2 responses and induce non-eosinophilic inflammation [55] , [56] , [57] , [58] . Chlamydia-derived LPS is atypical and is 1 , 000 fold less immunogenic compared to other bacterial-derived LPS [59] , and therefore , is unlikely to be the cause of Chlamydia-induced neutrophilic AAD . CpGs when given together with antigen in established disease induce Th1 and regulatory T cell responses that suppress the features of AAD including Th2 responses and AHR [60] , [61] , [62] . We have shown that depleting neutrophils during a Chlamydia infection inhibits the development of neutrophilic AAD , by contrast IL-17 responses drive NTHi-induced neutrophilic AAD . Therefore , we suggest that it is specific immune responses to these , as well as potentially other infections , which are driven by as yet unidentified factors in the infections , that are the mechanisms that drive neutrophilic AAD [47] . Our studies do not rule out LPS , CpGs , or other PAMPs as drivers of the neutrophilic phenotype and further research is required to elucidate this possibility . Importantly we have shown that infection-induced neutrophilic AAD and the suppression of Th2 responses are dependent upon IL-17 . Depletion of IL-17 with anti-IL-17 monoclonal antibody during AAD prevented the development of infection-induced neutrophilic AAD . This suggests that IL-17 is critical in the recruitment of neutrophils and may suppress Th2 responses in infection-induced neutrophilic inflammation and neutrophilic asthma . Wakashin et al . , demonstrated that adoptive transfer of antigen-specific Th17 cells induced airway neutrophil recruitment , which supports our data [63] . Little is known about how Th17 and Th2 cells interact with or regulate each other . We demonstrate that infection inhibits cytokine release compared to uninfected allergic controls , and that anti-IL-17 treatment partially restored these effects , while having no effect on eosinophil recruitment ( data not shown ) . This suggests that other mechanisms are also involved in the suppression of Th2 responses by NTHi infection , which requires further investigation . Several recent studies have investigated the relationship between Th17 and Th2 cells . Schnyder-Candrian et al . , showed that the administration of rIL-17 in a murine model of AAD significantly reduced allergen-induced eotaxin , thymus and activation-regulated chemokine ( TARC ) and IL-5 , thereby reducing eosinophilic inflammation [64] , while another study showed that inhibiting IL-17 in AAD also reduced airway eosinophils , neutrophils , AHR , and Th2 cytokines [65] . These data suggest that IL-17 may suppress or promote eosinophilic inflammation , but the mechanisms that drive these different effects remain unknown . Our data is in agreement with Schnyder-Candrian et al . , who suggest that IL-17 interferes with DC activation and antigen uptake , which prevents T cell activation and reduced IL-4 , -5 and -13 production , leading to suppressed allergic responses . Interestingly , a recent study has shown a CD4+ T cell subtype that expresses both Th17 and Th2 cytokines , including IL-4 , IL-5 , IL-13 , IL-17 and IL-22 , and this subset is increased in asthmatics compared to healthy controls [66] . However , these cells have thus far only been found in the periphery , and confirmation of their presence is needed in BAL , sputum and/or bronchial biopsies . In conclusion , we show that H . influenzae infection may be involved in the development of neutrophilic asthma . Infection suppressed features of Th2-mediated eosinophilic AAD , while inducing features of neutrophilic asthma that are mediated by infection-induced IL-17 . Therefore , infection-induced IL-17 responses may play a major role in the pathogenesis of neutrophilic asthma . Our studies indicate the important role of infection in driving neutrophilic asthma-like disease , and identify new areas of investigation that may enhance the understanding of disease progression . Developing new treatments targeting infection may lead to better management of individuals with this disease phenotype .
This study was carried out in strict accordance with the recommendations in the NSW Animal Research Regulation 2005 , and the Australian Code of Practice for the care and use of animals for scientific purposes ( National Health and Medical Research Council ) . All protocols were approved by the Animal Care and Ethics Committee of the University of Newcastle ( permit number 987/0111 ) . All surgery was performed under sodium pentobarbital anaesthesia , and all efforts made to minimize pain and suffering . Six to eight week old female BALB/c mice were used . Mice were sensitized by intraperitoneal ( i . p . ) injection , with OVA ( 50 µg , Sigma-Aldrich , Castle Hill , NSW , Australia ) with the Th2-inducing adjuvant Rehydrogel ( 1mg , in 200 µl sterile saline , Reheis , Berkeley Heights , USA ) . On days 12 to 15 mice were challenged intranasally ( i . n . ) with OVA ( 10 µg , 50 µl ) and AAD was assessed on day 16 [67] . Controls were sham sensitized to saline . NTHi ( NTHi-289 ) glycerol stocks were plated onto chocolate agar plates ( Oxoid , SA , Australia ) , grown overnight ( 37°C , 5% CO2 ) , then washed off the plate and suspended in sterile PBS . To determine the effects of infection , mice were inoculated i . t . with 5x105 CFU NTHi ( in 30 µl PBS ) during ( Day 0 ) or after ( Day 10 ) OVA sensitization . Controls were infected but not exposed to OVA . In preliminary studies we determined that this inoculum induced an infection from which the mice recovered and could be used to study the effects of infection on AAD . BALF was collected and processed as previously described [68] . Briefly , the left lung was tied off and the right lung was washed twice with Hank's buffered salt solution ( 700 µl , HBSS; Trace Scientific , Noble Park , Vic , Australia ) . Cells were pelleted and resuspended in red blood cell lysis buffer , washed and resuspended in HBSS , then cytocentrifuged ( 300g , 5 min , ThermoFisher Scientific , Scoresby , Vic , Australia ) onto microscope slides . Blood smears were prepared from a drop of whole blood . BALF and blood cells were stained with May-Grunwald-Giemsa , and differential leukocyte counts were enumerated using light microscopy [68] . Right lobes of lungs , from which BALF had been obtained , were aseptically removed and homogenized in 1ml of sterile PBS . Serial dilutions of BALF and lung homogenates were prepared in sterile PBS , plated onto chocolate agar plates and incubated overnight ( 37°C , 5% CO2 ) . Colonies were enumerated and bacterial numbers per right lung calculated . AHR was measured in response to increasing doses of aerosolized methacholine , by whole body invasive plethysmography as previously described [47] . Briefly , mice were anaesthetized and tracheas were cannulated and attached to a ventilator . Peak dynamic compliance and transpulmonary resistance were assessed by analysis of pressure and flow waveforms following challenge with increasing doses of aerosolized methacholine ( Sigma-Aldrich ) . Supernatants from lung draining MLN T cells were restimulated with OVA ( 200 µg/ml ) or ethanol-killed NTHi ( 2×107 CFU/ml ) and cultured for six days ( 5% CO2 , 37°C , 1x106 cells per well ) . After culture supernatants containing soluble factors were recovered and analyzed for IL-5 , IFN-γ , ( BD Biosciences , North Ride , NSW , Australia ) , IL-13 , IL-17A and IL-22 ( R&D Systems , Minneapolis , MN , USA ) by ELISA , according to manufacturer's instructions [69] . Whole lungs were homogenized in RIPA buffer ( 1ml , Sigma-Aldrich ) and incubated on ice for 5 mins . Cells were pelleted and the supernatant recovered and analyzed for KC and MIP2 by ELISA , ( R&D Systems ) , according to manufacturer's instructions . RNA was TRIZOL extracted from whole lung homogenates according to manufacturer's instructions ( Invitrogen , Mount Waverly , Vic , Australia ) . Target gene expression was determined relative to the reference gene hypoxanthine-guanine phosoribosyltransferase ( HPRT ) [69] . Primers used were IL-17 , Fwd 5′-aaacatgagtccagggagagcttt-3′ , Rev 5′-actgagcttcccagatcacagagg-3′; ROR-γt , Fwd 5′- ccgctgagagggcttcac-3′ , Rev 5′- tgcaggagtaggccacattaca-3′; MIP2 , Fwd 5′- ctagctgcctgcctcattctac-3′ , Rev 5′- caacagtgtacyyacgcagacg-3′; KC Fwd 5′- cttggggacaccttttagca-3′ , Rev 5′- gctgggattcacctcaagaa-3′; TGF-β Fwd 5′- cccgaagcggactactatgctaaa-3′ , Rev 5′- ggtaacgccaggaattgttgctat-3′; IL-10 , Fwd 5′-catttgaattccctgggtgagaag-3′ , Rev 5′- gccttgtagacaccttggtcttgg-3′; and HPRT Fwd 5′- aggccagactttgttggatttgaa-3′ , Rev 5′- caacttgcgctcatcttaggcttt-3′ . Single cell suspensions of MLNs and collagenase-D digested lungs were prepared . IL-17 producing cells were identified by stimulation with phorbol 12-myristate 13-acetate ( PMA , 0 . 1 µg/ml ) and ionomycin ( 1 µg/ml , Sigma-Aldrich ) in the presence of Brefeldin A ( 8 µg/ml , Sigma-Aldrich ) for 4 hours [70] . Cells were incubated with Fc block for 15mins , then stained for surface markers CD4 , CD3 , CD11b , Gr-1 ( BD Bioscience ) , or F4/80 ( eBioscience , San Diego , CA , USA ) , fixed with 4% paraformaldehyde ( PFA ) , permeabilized with 0 . 1% saponin , and stained for intracellular IL-17 ( or isotype control rat IgG2a , eBioscience ) . Tregs were identified using surface markers CD4 , CD25 and a staining kit for intracellular FoxP3 ( or IgG2a isotype control ) according to manufacturer's instructions ( eBioscience ) . pDCs were characterized as CD11clowCD11b-B220+ , and mDCs characterized as CD11c+CD11b+B220− ( BD Bioscience ) ; using MHCII and CD86 ( R&D Systems ) for activation and co-stimulation status . All cells were analyzed using a FACS Canto ( BD Bioscience ) [47] . Single cell suspensions of collagenase-D digested lung tissue were prepared , and resuspended in red blood cell lysis buffer . Resuspended cells were either incubated overnight ( 5% CO2 , 37°C , 1x106 cells per well ) and macrophages isolated by adherence to culture plates; or were put through a mouse neutrophil enrichment kit ( Stemcell Technologies , Melbourne , Vic , Australia ) and neutrophils isolated by negative selection following manufacturer's instructions . mRNA was purified from these macrophages and neutrophils using a PureLink RNA mini kit ( Invitrogen ) according to manufacturer's instructions . Monoclonal anti-IL-17A neutralizing antibody ( clone 50104 , rat IgG2a ) was administered by i . p . injection ( 100 µg/mouse , eBioscience ) on days 11 and 13 , and features of AAD were assessed on day 16 . Control groups were uninfected and treated with anti-IL-17 or treated with IgG2a isotype control antibody [47] . Results are presented as mean ± standard error of the mean ( SEM ) from 6–8 mice , in duplicate . Significance was determined by one-way ANOVA or Student t-test ( GraphPad Software , CA , USA ) . | Approximately 50% of asthmatics have non-eosinophilic inflammation , and 20% of these patients have severe neutrophilic inflammation and increased IL-8 levels . These so-called neutrophilic asthmatics have persistent airway colonization with bacteria , and Haemophilus influenzae is one of the bacteria most commonly isolated . However , how H . influenzae is associated with the pathogenesis of neutrophilic asthma is unknown . In this study we used mouse models to investigate the relationship between H . influenzae infection and allergic airways disease ( AAD ) . We showed that infection promoted the development of hallmark features of neutrophilic asthma . Infection suppressed Th2 cytokines , eosinophilic inflammation , and AHR in AAD , while increasing neutrophilic inflammation and IL-17 responses . Importantly , inhibition of IL-17 during AAD reduced airway neutrophils and neutrophil chemokines , suggesting that infection drives the development of neutrophilic inflammation through an IL-17-mediated mechanism . This provides novel insights into the mechanisms that may underpin infection-induced neutrophilic asthma . These data also suggest that treatments targeting infection may lead to improved management of neutrophilic asthma . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"immunology",
"biology",
"microbiology"
] | 2011 | Haemophilus influenzae Infection Drives IL-17-Mediated Neutrophilic Allergic Airways Disease |
Snakebite poisoning is a significant medical problem in agricultural societies in Sub Saharan Africa . Antivenom ( AV ) is the standard treatment , and we assessed the cost-effectiveness of making it available in 16 countries in West Africa . We determined the cost-effectiveness of AV based on a decision-tree model from a public payer perspective . Specific AVs included in the model were Antivipmyn , FAV Afrique , EchiTab-G and EchiTab-Plus . We derived inputs from the literature which included: type of snakes causing bites ( carpet viper ( Echis species ) /non-carpet viper ) , AV effectiveness against death , mortality without AV , probability of Early Adverse Reactions ( EAR ) , likelihood of death from EAR , average age at envenomation in years , anticipated remaining life span and likelihood of amputation . Costs incurred by the victims include: costs of confirming and evaluating envenomation , AV acquisition , routine care , AV transportation logistics , hospital admission and related transportation costs , management of AV EAR compared to the alternative of free snakebite care with ineffective or no AV . Incremental Cost Effectiveness Ratios ( ICERs ) were assessed as the cost per death averted and the cost per Disability-Adjusted-Life-Years ( DALY ) averted . Probabilistic Sensitivity Analyses ( PSA ) using Monte Carlo simulations were used to obtain 95% Confidence Intervals of ICERs . The cost/death averted for the 16 countries of interest ranged from $1 , 997 in Guinea Bissau to $6 , 205 for Liberia and Sierra Leone . The cost/DALY averted ranged from $83 ( 95% Confidence Interval: $36-$240 ) for Benin Republic to $281 ( $159–457 ) for Sierra-Leone . In all cases , the base-case cost/DALY averted estimate fell below the commonly accepted threshold of one time per capita GDP , suggesting that AV is highly cost-effective for the treatment of snakebite in all 16 WA countries . The findings were consistent even with variations of inputs in 1—way sensitivity analyses . In addition , the PSA showed that in the majority of iterations ranging from 97 . 3% in Liberia to 100% in Cameroun , Guinea Bissau , Mali , Nigeria and Senegal , our model results yielded an ICER that fell below the threshold of one time per capita GDP , thus , indicating a high degree of confidence in our results . Therapy for SBE with AV in countries of WA is highly cost-effective at commonly accepted thresholds . Broadening access to effective AVs in rural communities in West Africa is a priority .
Snakebite poisoning is a significant cause of death and disability in rural West Africa [1 , 2 , 3 , 4 , 5 , 6 , 7] . The exact burden of snakebite is difficult to ascertain and is often undereported . A study by Jean-Philippe Chippaux reported an estimate of over 314 , 000 envenomations , 7300 mortality and nearly 6000 amputations occurring yearly in sub-Saharan Africa ( SSA ) [7] . However , even in West Africa alone , a range of 1504 to 18 , 654 annual mortality from snakebite envenoming has been made [8] . This is further compounded by the variability in snakebite incidence with estimates of as high as 500 bites per 100 , 000 persons per year in parts of northern Nigeria [9] . Vipers ( Echis ocellatus , E . leucogaster and E . jogeri ) are a major cause of snakebite envenoming throughout the sub-region mainly in Benin republic , Burkina Faso , Cameroun , Chad , Gambia , Ghana , Mali , Niger , Nigeria , Togo and Senegal [1 , 2 , 3 , 4 , 5 , 6 , 7] . In the sub-region , envenoming from snakes other than vipers mostly results from African spitting cobras ( Naja nigricollis , N . katiensis ) , puff-adder ( Bitis arietans ) , mambas ( Dendroaspis viridis , D . polylepis ) , burrowing asps or stiletto snakes ( Atractaspis species ) , night adders ( Causus maculatus , C . rhombeatus , C . resimus , C . lichtensteinii ) and very rarely boomslang ( Dispholidus typus ) . Joger’s carpet viper ( E . jogeri ) is confined to Mali . Romane’s carpet viper ( Echis leucogaster ) and Egyptian cobras ( Naja haje and N . senegalensis ) are causes of snakebite envenoming in the Sahelian and drier parts of West Africa while the forest cobra ( Naja melanoleuca ) and the Gaboon viper ( Bitis gabonica ) cause occasional bites in the rain forest and South-eastern parts of the sub-region [1 , 5 , 7] . In West Africa , carpet vipers may account for as many as two thirds of all snakebite envenoming although their range is limited to the savannah region [1 , 9 , 10 , 11] . Envenoming from carpet vipers leads to swelling and tissue damage at the site of bite , local and systematic bleeding , anaemia and shock . Often death results from cerebral haemorrhage , bleeding elsewhere or haemorrhagic shock [1 , 10 , 11] . The bleeding abnormality results from a prothrombin activating metalloprotease “Ecarin” and a FX activating component , an anticoagulant , platelet activator/inhibitor and haemorrhagins in the snake’s venom [1 , 10 , 11] . Non-clotting blood detected by the 20minute Whole Blood Clotting Test [20WBCT] virtually confirms carpet viper envenoming in the northern third of Africa ( roughly north of the equator ) and is utilized to assess adequacy of treatment [1 , 10 , 11] . Most non-carpet viper bites lead to local swelling and tissue damage . The colubrids , boomslangs and twig snake ( Thelotornis kirtlandii ) , are back fanged snakes that rarely envenom but can cause severe bleeding and acute kidney injury . Neurotoxic features may result from Naja haje , Naja melanoleuca and Dendroaspis spp bites with deaths often resulting from respiratory muscle paralysis [12] . The risk of death from snakebites other than viper envenoming is lower [9 , 13 , 14 , 15 , 16 , 17] , but cobra spits may lead to blindness and bites to cancerous ulcers , abortions , scarring , arthrodeses , contractures and psychological impairment leading to permanent disability and productivity loss following hospitalization and incapacitation [7 , 18 , 19 , 20 , 21] . Cessation of bleeding abnormalities and restoration of clotting following administration of effective antivenom usually occurs promptly in carpet viper envenoming . Antivenom is efficacious in decreasing the likelihood of dying and is the main treatment for snakebite envenoming [1 , 11 , 22 , 23] . However , its administration is associated with early adverse reactions ( EAR ) which rarely results in fatality . [24 , 25 , 26] . Specific interventions may be required to either prevent EAR with administration of premedication prior to antivenom or to treat it once developed following antivenom administration [25 , 26] . Antivenoms are formulated as either liquid agents that needs to conveyed and stored at low temperature with a life span of about three years [27 , 28] or as freeze dried substances that are more stable with extended shelf life . Both types of formulations have been produced for the sub-region [6 , 27 , 28] . The average cost per treatment of antivenom was reported as US$124 ( range US$55–$640 ) although a median price of US$153 was also reported for Sub-Saharan Africa [29 , 30 , 31] . The few effective antivenoms in the sub-region generally have been scarce , locally unaffordable and inaccessible where they are most needed . Partly for these reasons antivenom utilization has drastically declined to a very small fraction of indicated need . The situation has been compounded further by the recent announcement by Sanofi-Pasteur that production and distribution of FAV Afrique , currently the most widely distributed and most dependable antivenom in the sub-region , will be discontinued by 2016 . Its loss will exacerbate an already serious public health crisis and makes the management of snakebite even more challenging [32] . It is therefore extremely important within the context of other competing public health priorities to assess the health economics of antivenoms to guide policy . Before the recent publication of our work focusing on Nigeria [33] , few economic evaluations of preliminary nature had been conducted on antivenoms [34 , 35] . Here , we evaluated the cost-effectiveness of antivenom utility in the treatment of snakebite envenomation by computing incremental cost effectiveness ratios ( ICERs ) of the cost per death averted and the cost per DALY averted by adapting a previously published model for Nigeria to 16 countries in WA . We performed the analysis from healthcare system perspective to provide policy makers with evidence towards broadening access to antivenoms given their importance in preventing loss of lives and limbs among poor vulnerable communities in West Africa .
A decision analytic model ( Fig 1 ) was adapted to estimate health outcomes and costs associated with the availability and use of geographically appropriate and effective antivenoms for snakebite poisoning in West Africa [33] . Details of the model structure are described elsewhere [33] . Briefly , the model assessed the availability of effective antivenoms relative to no availability in the decision node . The model differentiated snakebite envenoming by carpet viper and non-carpet viper and distinction was made on the basis of the 20WBCT in the treatment arm of the model . Evidence of coagulopathy would lead to the administration of mono-specific antivenom that neutralizes carpet viper venom only , whereas absence of coagulopathy triggers the administration of a polyspecific antivenom that neutralizes venoms from several snakes , including the carpet viper . In the first chance node , the model included EARs associated with antivenom use , which are more likely to occur with polyspecific rather than the monospecific antivenom [23 , 27 , 28 , 36 , 37] . Symptoms of EAR were diverse and death could happen in about 1% of cases [24 , 25 , 26] . Survivors of snakebite may recover completely or remain with significant sequaela ( e . g . amputation ) that was considered in the model . Treatment outcomes were converted into DALYs on the basis of local life expectancy . Tree Age Pro Suite Healthcare 2014 software was used for analyses .
The cost/death averted for the 16 countries of interest varied . It was as low as $1 , 997 in Guinea Bissau to as high as $6 , 205 in Liberia and Sierra Leone . The cost/DALY averted ranged from a low of $83 ( 95% Confidence Interval: $36-$240 ) for Benin Republic to a high of $281 ( $159–457 ) for Sierra-Leone . In all cases , the base-case cost/DALY averted estimate fell below the commonly accepted threshold of one time per capita GDP , suggesting that AV is highly cost-effective for the treatment of snakebite in all 16 WA countries [51 , 52] . The findings from the analyses were also consistent to variations of inputs in 1-way sensitivity and scenario analyses as depicted ( Table 3 and Fig 2 ) . The individual countries’ model results were most sensitive to effectiveness of antivenom in decreasing mortality , natural ( unattended ) mortality , costs of antivenoms and types of snake causing envenoming ( Fig 2 ) . Results were not sensitive to antivenom associated EAR or the cost of managing it . Varying the cost of antivenom from $125 to two times for victims who may require two doses , i . e . $306 , still yielded ICER estimates that remain cost-effective . The ICERs rose when the frequency of snakebite envenomation due to saw-scaled viper was reduced to 0% except in Benin and Guinea Conakry where Antivipmyn antivenom is used and is effective even against elapids ( Table 3 ) [42] . Moreover , the ICER ranged from $97 . 26 in Benin to high levels of $13 , 964 . 26 in Liberia and $15 , 278 . 99 in Sierra Leone even in the worst case scenario where ( poly-specific ) antivenoms have nil effectiveness ( 0% ) against bites from snakes other than carpet viper . These estimates fall outside the cost-effectiveness thresholds in Liberia and Sierra Leone largely because non-carpet viper accounts for 99% of SBE . Applying a modest reduction of 40% on the probability of EAR with the use of adrenaline premedication [25 , 26 , 33] gave a cost per DALY averted slightly lower than base-case ICERs . Similarly , the ICERs were only very slightly altered even when more serious or more frequent disabilities were substituted in the model . This was demonstrated with venom-induced-blindness ( 0 . 01% ) or Post-Traumatic-Stress-Disorder ( 20% ) with disability-weights of 0 . 552 and 0 . 105 respectively [18 , 21 , 48] . Furthermore , our PSA confirms the model findings remain consistent to concurrent variation of all model inputs , as the ICERs with their respective 95% confidence limits are far less than the cost-effectiveness thresholds ( Table 3 ) . It showed that in majority of simulations ( 97 . 3% in Liberia to 100% in Cameroun , Guinea Bissau , Mali , Nigeria and Senegal ( Fig 3 ) ) our model results yielded an ICER that fell below the threshold of one time per capita GDP , thus , indicating a high degree of confidence in our results [51 , 52] .
The findings from the cost effectiveness analysis demonstrate that providing and broadening antivenom access throughout areas at risk in rural West Africa should be prioritized given the considerable reduction in deaths and disabilities that could be derived at a relatively small cost . | Antivenom is the main intervention against snakebite poisoning but is relatively scarce , unaffordable and the situation has been compounded further by the recent cessation of production of effective antivenoms and marketing of inappropriate products . Given this crisis , we assessed the cost effectiveness of providing antivenoms in West Africa by comparing costs associated with antivenom treatment against their health benefits in decreasing mortality . In the most comprehensive analyses ever conducted , it was observed the incremental cost effectiveness ratio of providing antivenom ranged from $1 , 997 in Guinea Bissau to $6 , 205 for Liberia and Sierra-Leone per death averted while cost per Disability Adjusted Life Year ( DALY ) averted ranged from $83 for Benin Republic to $281 for Sierra-Leone . There is probability of 97 . 3–100% that antivenoms are very cost-effective in the analyses . These demonstrate antivenom is highly cost-effective and compares favorably to other commonly funded healthcare interventions . Providing and broadening antivenom access throughout areas at risk in rural West Africa should be prioritized given the considerable reduction in deaths and DALYs that could be derived at a relatively small cost . | [
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... | 2016 | Cost-Effectiveness of Antivenoms for Snakebite Envenoming in 16 Countries in West Africa |
Paired sense and antisense ( S/AS ) genes located in cis represent a structural feature common to the genomes of both prokaryotes and eukaryotes , and produce partially complementary transcripts . We used published genome and transcriptome sequence data and found that over 20% of genes ( 645 pairs ) in the budding yeast Saccharomyces cerevisiae genome are arranged in convergent pairs with overlapping 3′-UTRs . Using published microarray transcriptome data from the standard laboratory strain of S . cerevisiae , our analysis revealed that expression levels of convergent pairs are significantly negatively correlated across a broad range of environments . This implies an important role for convergent genes in the regulation of gene expression , which may compensate for the absence of RNA-dependent mechanisms such as micro RNAs in budding yeast . We selected four representative convergent gene pairs and used expression assays in wild type yeast and its genetically modified strains to explore the underlying patterns of gene expression . Results showed that convergent genes are reciprocally regulated in yeast populations and in single cells , whereby an increase in expression of one gene produces a decrease in the expression of the other , and vice-versa . Time course analysis of the cell cycle illustrated the functional significance of this relationship for the three pairs with relevant functional roles . Furthermore , a series of genetic modifications revealed that the 3′-UTR sequence plays an essential causal role in mediating transcriptional interference , which requires neither the sequence of the open reading frame nor the translation of fully functional proteins . More importantly , transcriptional interference persisted even when one of the convergent genes was expressed ectopically ( in trans ) and therefore does not depend on the cis arrangement of convergent genes; we conclude that the mechanism of transcriptional interference cannot be explained by the transcriptional collision model , which postulates a clash between simultaneous transcriptional processes occurring on opposite DNA strands .
Sense and anti-sense transcripts ( S/AS ) are simply pairs of RNAs ( either protein-coding or non-protein-coding ) containing sequences that are at least partially complementary to each other . S/AS gene pairs can be transcribed in cis from opposing DNA strands at the same genomic locus [1] . In recent years , genome sequencing projects have revealed the frequent presence of antisense transcripts in cells , even in one of the smallest self-replicating organisms , Mycoplasma pneumoniae [2] . In particular , S/AS pairs located in cis represent a common structural feature in the genomes of both prokaryotes and eukaryotes , including mammals ( human , mouse , rat , cow ) , birds ( chicken ) , lower vertebrates ( zebrafish ) , invertebrates ( Caenorhabditis elegans , Drosophila melanogaster ) , plants ( Arabidopsis thaliana , rice ) , and yeast [3]–[6] . According to their transcriptional orientation and extent of sequence overlap , S/AS genes can be classified into three major groups: ( 1 ) convergent gene pairs , overlapping at their 3′ ends; ( 2 ) divergent gene pairs , overlapping at their 5′ ends; and ( 3 ) consistent gene pairs , overlapping and transcribed in the same direction . The genomic distribution of these different types of gene pair is species-specific . For example , convergent gene pairs are prevalent in Drosophila and C . elegans , but are rare in human and mouse genomes [3] . The structural organization of S/AS gene pairs confers a significant mechanism for regulating gene expression levels . For example , global analysis of the mammalian transcriptome showed that a large proportion of the genome can produce transcripts from both strands and revealed anti-regulation of S/AS pairs [7] . In particular , experimental perturbation of an antisense RNA was shown to alter the expression of the sense mRNA in cis . In both budding and fission yeasts , transcriptional interference has been observed between sense and antisense transcripts of several convergent genes arranged in cis or trans [8]–[12] . This involves one transcriptional process exerting a direct negative impact on a second transcriptional process . Such interference plays important functional roles , for example in the entry of yeast cells into meiosis by regulating the expression of the IME4 gene [9] . Antisense transcripts are mature RNA species ( polyadenylated at their 3′ ends ) and contribute a mechanism of transcription interference that is distinct from the RNAi-mediated regulation of gene expression that is present in most eukaryotes , but absent in Saccharomyces cerevisiae . For example , antisense transcripts involved in RNAi are often encoded elsewhere in the genome from the target gene , and require further processing into shorter , functional sequences by the RNAi machinery . The most obvious explanation for transcriptional interference between convergent S/AS gene pairs is given by the collision model [13]; in this model , RNA synthesis from one DNA strand clashes with transcription from the other strand , and so active antisense transcription would suppress sense RNA transcription [14] , [15] . Though this model is supported by atomic force microscopy data in E . coli [16] , its role in transcriptional interference in budding yeast has not been thoroughly assessed . In this work , we have exploited the availability of genome-wide transcriptome data from the budding yeast S . cerevisiae to explore the significance of transcriptional interference between convergent gene pairs on a global scale . Using a comprehensive set of S/AS pairs with overlapping 3′-UTRs , we have demonstrated that transcriptional interference is common to more than 600 gene pairs , which account for ∼20% ORFs in the S . cerevisiae genome , across a broad range of growth conditions . For a more detailed understanding of the underlying mechanisms , we focus on four such representative gene pairs and show that transcriptional interference is dependent only on the 3′-UTR sequence . Moreover , its occurrence is not restricted to the arrangement of the gene pairs in cis , but can also occur when the partner genes are re-located apart ( in trans ) . We have illustrated the functional importance of this mode of gene regulation during the yeast cell cycle and its role in the phenotypic response of yeast cells to environmental stress .
Based on genome-wide transcriptome [17] and genome sequence data , we identified 645 convergent gene ( ORF ) pairs with overlapping 3′-UTRs , accounting for ∼20% of total genes in the yeast genome ( see materials and methods , Table S1 ) . We compared the convergent ORF pairs predicted from the mRNA-Seq data with those from both nascent RNA sequencing ( NET-Seq ) data [18] and strand-specific RNA sequencing ( ssRNA-Seq ) data [19] . We found that among the 645 convergent pairs , 531 ( 82 . 3% ) and 329 ( 51 . 0% ) were detected as convergent pairs with overlapping 3′-UTRs from the NET-Seq and ssRNA-Seq datasets respectively . For 15 . 5% and 33 . 0% of the 645 convergent pairs , at least one of the two genes in the pair was unexpressed , in the NET-Seq and ssRNA-Seq datasets respectively ( Figure S1 ) . Additionally , there are only 12M sequencing reads in the ssRNA-Seq dataset , whilst the mRNA-Seq and NET-Seq datasets contain 29M and 69M reads respectively . This may , at least partly , explain the lower proportion of the convergent pairs confirmed in the ssRNA-Seq data compared to the NET-Seq data . Based on the mRNA-Seq and genomic sequencing data , we observed a highly significant negative correlation between 3′-UTR length and gene expression level of the corresponding ORF ( r = −0 . 089 , P<10−4 ) , suggesting an important role for the 3′-UTR in the regulation of transcription . However , there was no clear relationship between 5′-UTR length and gene expression level . To assess the occurrence of transcriptional interference between convergent partner genes with overlapping 3′-UTRs , we extracted mRNA expression values for these 645 gene pairs from Affymetrix microarray experiments based on the test strain BY4741 under seven environmental stress conditions ( Table S3 ) . The expression of convergent ORFs showed a consistent and highly significant negative correlation ( −0 . 159≤r≤−0 . 124; 10−4≤P≤0 . 002 ) across all seven conditions ( Figure S2 ) . The significant negative correlation was also observed in both the NET-Seq ( r = −0 . 09 , P<0 . 05 ) and ssRNA-Seq data ( r = −0 . 121 , P<0 . 05 ) . We infer that transcriptional interference between convergent gene pairs with overlapping 3′-UTRs is a widespread phenomenon in the yeast genome . To further understand the transcriptional relationship between genes with overlapping 3′-UTRs , we selected four convergent gene pairs from the complete set of 645 pairs for more detailed study ( Table 1 ) . The 3′-UTR boundaries and overlapping regions were confirmed by 3′-RACE sequencing . In addition , these four pairs were also confirmed as convergent genes with overlapping 3′-UTRs in the NET-Seq and ssRNA-Seq datasets ( Figure S3 ) . We profiled gene expression of the convergent pairs in the wild type strain YL1CWT , a laboratory haploid strain which we have described previously [20] , and in a series of seven derived genetically engineered strains ( Table 2 ) , as illustrated in Figure 1A . When expression of upstream genes ( SHM1 , AXL2 , APT1 and ADE1 ) was inhibited ( group I ) , the expression of the downstream partner genes ( YPT10 , REV7 , UNG1 and KIN3 ) was up-regulated in comparison to the wild type control . Correspondingly , when the downstream genes were over-expressed , expression of the upstream partner genes was clearly repressed ( group II ) . In the extreme situation where expression of the downstream genes was completely silenced ( group III ) , expression of the upstream genes increased to a variable extent compared to the wild type level . Convergent overlapping gene pairs therefore exhibit a pattern whereby a change in the expression of either partner gene leads to the expression of the other gene changing in the opposite direction , a pattern we refer to as ‘anti-regulation’ hereafter . To explore the relationship between transcriptional interference and the sequence of the ORF , and thus of the protein encoded , we created frame-shift mutations in the coding sequence of the downstream genes . These mutations were designed to generate premature termination in translation , which , while not changing the normal transcription of the genes , would mean that the transcripts generated would no longer be translated into normally functioning proteins . However , anti-regulation ( transcriptional interference ) was still observed in the presence of such mutations ( group IV ) . Expression of the frame-shifted downstream genes was increased relative to that of the wild types , while at the same time , expression of the upstream genes was correspondingly decreased . This shows that over-expressing the mutated downstream genes leads to significantly altered expression of their upstream convergent partners , and thus excludes the possibility that anti-regulation of gene partners in these four convergent pairs is dependent on functional interactions between their encoded proteins . Having established the transcriptional interference between the four pairs of convergent genes with overlapping 3′-UTRs , we then created a series of genetically modified strains to test if the interference could be maintained in trans . These modifications included translocation of either the complete ( ORF and 3′-UTR ) or partial ( ORF or 3′-UTR only ) sequence of the downstream genes to the HO locus and ectopic expression stimulated by the constitutive promoter PADH . Ectopic over-expression of the complete downstream genes ( group V ) or only their 3′-UTRs ( group VII ) significantly repressed the expression of the upstream genes . In contrast , over-expression of the ORF alone did not lead to an obvious decrease in expression of the upstream genes ( group VI ) . Firstly , this shows that the 3′-UTR ( rather than the ORF ) plays a key role in anti-regulation between the partner genes of convergent gene pairs . Secondly , the 3′-UTRs can mediate anti-regulation of expression of the partner genes in trans , i . e . when the partner genes are located apart , and therefore does not depend on their native arrangement in cis . It should be noted that the range of variation in relative expression of downstream genes is large in comparison to that of the corresponding upstream genes in groups II , IV , V , VI and VII . This reflects the fact that we designed the experiments to overexpress the downstream genes . We compared the transcriptional interference at both mature RNA and nascent RNA levels for two of the four convergent gene pairs . Figure 1B illustrates expression of one gene when expression of its convergent partner was altered for two convergent gene pairs at nascent RNA ( lower panel ) and mature mRNA ( upper panel ) levels . It is clear that expression of one of the convergent genes markedly responds to altered expression of its convergent partner at both nascent and mature RNA levels . This excludes the possibility that the anti-regulation of expression between convergent gene pairs occurs during the RNA maturation process . We next sought to determine whether the observed anti-regulation in expression between convergent gene pairs with overlapping 3′-UTRs could also be observed at the level of protein abundance . We replaced the ORFs of the upstream partners for two of the four gene pairs ( APT1/UNG1 and ADE1/KIN3 ) with the reporter gene Fluc , and quantified expression of the reporter protein in the wild type and modified backgrounds . Over-expression of either the downstream genes in situ , or of their 3′-UTRs ectopically , caused significant reduction in levels of the reporter protein ( Figure 2 ) . However , ectopic over-expression of only the ORF did not alter levels of the reporter protein . Therefore interference between convergent genes with overlapping 3′-UTRs affects both the expression of gene transcripts and protein levels . These effects are dependent on the 3′-UTR sequence and can be observed in trans as well as in cis . To further confirm the causal role of the overlapping 3′-UTRs in the transcriptional interference between convergent gene pairs , we removed the overlapping 3′-UTRs of two convergent pairs ( APT1/UNG1 and ADE1/KIN3 ) and measured their expression responses ( Figure 3 , group I ) . Inhibition of expression of the upstream gene did not result in up-regulation of the corresponding downstream partner gene ( group II ) . Similarly , over-expression of the downstream gene did not lead to repression of the upstream partner gene ( group III ) . These results reveal that the 3′-UTR plays a crucial , causal role in the anti-regulation of expression between convergent gene pairs with overlapping 3′-UTRs . Three of the four convergent gene pairs involve a biosynthesis pathway gene and a cell-cycle dependent gene ( Table 1 ) , providing an opportunity to investigate the dynamic change in expression of the convergent gene pairs over the course of the cell cycle . We profiled the expression of each pair over the 100 minute time span of the cell cycle and found a consistent pattern whereby one ORF reaches its expression peak , while its convergent partner is suppressed ( Figure 4A–C ) . The fourth gene pair involves a biosynthesis gene ( SHM1 ) and a GTP-binding protein ( YPT10 ) . As expected , this pair did not follow the same pattern ( Figure 4D ) . This demonstrates the functional importance of transcriptional interference between convergent gene pairs in the cell cycle of budding yeast . We explored the cell-cycle expression pattern of one of the four gene pairs with overlapping 3′-UTRs ( ADE1/KIN3 ) when its orientation was switched from ‘convergent’ to ‘tandem’ . We created two genetically modified strains through homologous recombination , the tandem KIN3 and the tandem URA3 , as illustrated in Figure 5A . The modifications were confirmed by sequencing ( the sequence data is listed in Table S5 ) . Figure 5B illustrates the cell cycle expression of the three genes ADE1 , KIN3 and URA3 in the wild type strain . It is clear that cell-cycle expression of the tested convergent gene pair was repeatedly confirmed in this independent assay , showing the expression pattern of one-rising and the other falling , as shown in Figure 4A . When the convergent pair was converted into ‘tandem’ orientation , the cell-cycle expression patterns of the convergent pair changed in comparison to that of the genes in wild type background , but the pattern of one rising and the other falling remained ( Figure 5C ) . Moreover , we observed the expression pattern of ADE1 was also markedly altered when its downstream partner gene was replaced with a tandemly oriented gene , URA3 ( Figure 5D ) . These observations strongly support the anti-regulation in the cell cycle expression of the convergent pair , which is independent of genomic orientation of the convergent genes . A natural question arises whether each gene in the convergent gene pairs tested is expressed in the same cell or instead is expressed mainly in different cells in the cell population . To answer this question we profiled expression of the convergent pair , KIN3 and ADE1 , in single cells across different stages of the cell cycle ( Figure S4 ) . It shows a highly significantly negative correlation in expression between the convergent genes ( Pearson's correlation coefficient r = −0 . 38 , P<0 . 01 ) , indicating that anti-regulation between convergent genes is also observed within individual cells . It is well established that gene expression in the budding yeast Saccharomyces cerevisiae changes in response to nutrient availability [8] , [21] . For example , ADE1 expression is responsive to adenine availability in the culture medium; when yeast cells are cultured in adenine rich ( synthetic complete , SC ) medium , the expression of ADE1 is suppressed , while its expression is stimulated in an adenine barren medium ( SC-A ) [22] . We examined expression of the convergent pair ADE1/KIN3 in the modified strains D-K1 and D-K2 ( Table 2 ) , cultured in either adenine rich or barren medium . In both strains , ADE1 expression was suppressed in the SC medium , but enhanced in the SC-A medium . Conversely , KIN3 expression was increased in the SC medium and decreased in SC-A , even in the presence of the strong prompter PADH in the test strain D-K2 ( Figure 6 ) . These observations indicate that alterations in gene expression in response to nutrient availability can be achieved through anti-regulation of expression between partners of a convergent gene pair , mediated by their 3′-UTR . We further tested the role of anti-regulation between convergent genes using a growth assay of the wild type strain ( YL1CWT ) and seven strains ( D-K1 to D-K7 , Table 2 ) genetically modified for the convergent gene pair ADE1 and KIN3 . In an adenine rich ( SC ) medium , the growth phenotype of all the strains largely reflects the change in the inoculation concentration ( Figure 7 , right panel ) . In the adenine-barren medium ( SC-A ) , growth phenotype of the wild type strain is comparable to that in the rich medium , given that the ADE1 gene is expressed at normal levels , as shown previously [22] . Growth of the strain with the KIN3 gene knocked out ( D-K3 ) is comparable to that of the wild type strain , agreeing with the fact that expression level of the ADE1 gene is comparable between these two strains ( group III , Figure 1A ) . However , growth is clearly repressed in the SC-A medium for strains in which ADE1 expression was suppressed , either directly ( D-K1 ) or indirectly by over-expression of its convergent partner gene KIN3 , in situ ( D-K2 and D-K4 ) or ectopically ( D-K5 ) . Ectopic over-expression of the KIN3 3′-UTR alone ( D-K7 ) clearly repressed growth in comparison to the wild type , but no repression was observed with ectopic over-expression of the KIN3 ORF alone ( D-K6 ) . Combining this result with our earlier observations suggests that ectopic over-expression of either the KIN3 3′-UTR or of the entire gene suppressed the expression of its convergent partner gene ADE1 , which in turn , led to suppressed growth in the nutrient limited medium . We conclude that the convergent organization of genes with overlapping 3′-UTRs in the yeast genome constitutes an effective mechanism for regulating gene expression and ultimately for controlling cell growth .
The mechanisms of transcriptional regulation are highly conserved in eukaryotic species [23] . However , the yeast genome is distinct in several ways [24] . First , by the complete absence of RNA-dependent regulatory systems ( including miRNAs and siRNAs ) present in higher eukaryotes . Second , by its highly compact structure , for example , over 20% of ORFs in the yeast genome are arranged in cis as sense and antisense gene pairs ( S/AS ) that overlap and are transcribed from opposing DNA strands . To explore the expression pattern of genes arranged in this way , we analyzed a complete set of over 600 convergent gene pairs with overlapping 3′-UTRs . The convergent genes showed a significant and consistent pattern of negative correlation in expression across a broad range of growth conditions , though this negative correlation could only explain a limited fraction of total gene expression variability . The mechanism underlying this widespread transcriptional interference was explored in detail for four representative pairs of convergent genes . We analyzed the expression patterns of the four convergent gene pairs in wild type yeast and its derived strains with various genetic modifications . The data revealed that convergent genes regulate each other's expression such that an increase in expression of either gene produces a decrease in expression of the other . We showed that this pattern of ‘anti-regulation , ’ or transcriptional interference occurs in single cells and does not require interaction between the proteins encoded by the convergent genes , since the effects persisted when the downstream gene was mutated so that a fully functional protein could not be made . Furthermore , we demonstrate that transcriptional interference between convergent genes is reflected in the abundance of the proteins encoded by the partner genes , showing that it has far-reaching effects in yeast cellular networks beyond the immediate changes at the transcript level . The functional importance of ‘anti-regulation’ in gene expression was demonstrated using a time course analysis of the cell cycle for three pairs involved in this process . In general , a rise in the expression of one gene occurred in parallel with falling levels of expression of the partner gene . This led to peaks in expression of one gene corresponding with troughs in the expression of the other gene , at various stages in the cycle . Moreover , we demonstrate that this cell-cycle expression pattern remains even when the convergent orientation of the pair is artificially converted into a tandem orientation . We have noted that the Proudfoot group conducted a systematic survey of the regulatory mechanism of convergent genes in Schizosaccharomyces pombe fission yeast . They demonstrated induced heterochromatinization for convergent genes occurring in a short period of G1-S phase , leading to down-regulation of the genes . This heterochromatinization event is mediated by the RNA inference ( RNAi ) pathway . In addition , most RNAi genes are themselves in convergent arrangement , resulting in auto-regulation between the convergent pairs [12] , [25] . They modified arrangement of a pair of convergent genes by inserting an extra gene between the convergent pair , causing loss of the G1-S down regulation [25] . In contrast , here we have shown that modification of the convergent pair into a tandem arrangement does not result in loss of anti-regulation in budding yeast . This is likely due to fundamental differences in the biology of budding yeast ( studied here ) compared with fission yeasts [12] , [25] , as well as to differences in experimental procedures . First , as in metazoa , Dicer , Argonaut and RdRP , which are essential in the RNAi pathway , are conserved in S . pombe fission yeast , but lost in S . cerevisiae budding yeast [26] . Additionally , RNAi mediated regulation of convergent genes in fission yeast occurs through heterochromatinization , which requires several proteins including Clr4 , RdRP , Tas3 , Chp1 and Swi6 , for which there are no known orthologs in S . cerevisiae budding yeast [26] , [27] . These findings suggest there may be different mechanisms regulating the expression of convergent genes in S . cerevisiae budding yeast and S . pombe fission yeast . Second , we created a truly tandem arrangement of the gene pair by switching the orientation of one gene in the pair . Moreover , in the present study , the convergent genes had intact 3′-UTRs even after their orientation was converted into tandem . Third , the present study has focused on only those convergent genes with overlapping 3′-UTRs . We have also demonstrated that the well-known environmental response of yeast cells to changes in nutrient availability is mediated through changes in expression of both the environment-responsive gene ADE1 , and its convergent partner , KIN3 . We conclude that the convergent organization of genes in the highly compact genome of S . cerevisiae is functionally significant and speculate that it represents a mode of gene expression regulation that may compensate for the absence of RNA-dependent regulatory systems . The most widely accepted explanation for transcriptional interference between convergent gene pairs with opposite transcriptional direction is the transcriptional collision model [13] . The model proposes that during transcription , RNA polymerase progresses towards the 3′ end of each gene , and so the two processes on opposite strands will eventually clash; this will result in a negative correlation in transcript abundance between the convergent genes [13] . One prediction given by the model is that the transcriptional interference between convergent genes should be released when the genes are no longer in their native convergent arrangement . For the four pairs of convergent genes studied here , we have demonstrated for the first time that transcriptional interference between the partner genes can persist even when one of those genes is no longer expressed from its original location . Specifically , we have shown that ectopically over-expressing either the entire downstream gene , or its 3′-UTR alone , leads to suppressed expression of the convergent partner gene; this suppression is dependent on the 3′-UTR and is not observed when the ORF alone is ectopically expressed . This indicates that the 3′-UTR sequences are both necessary and sufficient to mediate transcriptional interference of convergent genes in the S . cerevisiae genome . Furthermore , transcriptional interference , at least for these four gene pairs , cannot be explained by the transcriptional collision model and must involve other , unexplored mechanisms .
The combination of high-resolution yeast transcriptome sequencing data [17] and genome sequence data enabled us to predict both 5′ and 3′ untranslated regions ( UTRs ) on a genome-wide basis . From all ORFs with either 5′ ( 4 , 835 ) or 3′ ( 5 , 212 ) UTRs , we identified UTRs overlapping by at least 1 base pair . From the mRNA sequencing dataset , we identified 645 ‘convergent’ ORF pairs with overlapping 3′-UTRs , 53 ‘divergent’ pairs with overlapping 5′-UTRs and 65 ‘consistent’ pairs with the same transcriptional direction . We also analyzed nascent transcript ( NET-Seq ) sequencing data [18] and strand specific RNA ( ssRNA-Seq ) sequencing data [19] . These three datasets were summarized in Table S2 . Haploid yeast strain YL1C was used as the wild type strain and is described in detail elsewhere [20] . Genetically modified strains shown in Table 2 were constructed from the wild type following PCR-based protocols as follows . The upstream inhibited ( group I ) or downstream over-expressed ( group II ) strains were constructed by inserting an inhibitor or promoter segment accordingly into the upstream region of the target ORF . The terminator TTEF and constitutive promoter PADH were amplified from the widely used plasmids pAG36 and pAG32 respectively . The downstream gene knock out ( group III ) or three ectopically expressed groups ( V , VI and VII ) were constructed through exchanging the relevant gene segments . The knocked out segments were extracted from the plasmid pAG36 , and the downstream ORF and its 3′-UTR were amplified from the YL1C genomic DNA . For frame shift mutagenesis ( group IV ) , one or two nucleotide bases were inserted into the coding sequence to generate premature termination in translation . In the terminator changed groups ( VIII , IX and X ) , the 3′-overlapping regions of the convergent pairs were replaced by a TADH-TAOX1 segment constructed through PCR-based fusion assembly [28]; the upstream gene inhibited ( group IX ) or downstream gene over-expressed ( group X ) strains were then constructed using the same methods used to construct group I and II strains . Finally , strains for the dual-luciferase assay ( Table S4 ) were created as follows . Firstly , we used the reporter gene Fluc ( firefly luciferase ) vectored in the plasmid pGL3 ( Promega ) to replace the non-overlapping ORF regions of upstream genes of the convergent pairs , and then inserted Rluc ( Renilla luciferase ) vectored in the plasmid pRL-SV40 ( Promega ) into the genome as the internal control . Fluc and Rluc sequences can be found elsewhere [29] , together with the promoter and terminator sequences used for constructing the internal control . All the genetic modifications constructed here were confirmed by Sanger sequencing . Primer sequences for molecular cloning are shown in Table S6 . Unless specified , both wild type and engineered strains were grown in the standard rich medium ( YPD: 1% yeast extract , 2% polypepton , 2% glucose , plus 2% agar if necessary ) . In the adenine starvation environment , strains were grown in synthetic dropout medium ( the adenine barren medium , SC-A: 0 . 67% yeast nitrogen base w/o amino acids , 2% glucose , 0 . 2% yeast synthetic dropout mixture without adenine , plus 2% agar if necessary ) . The synthetic complete medium ( SC: 0 . 67% yeast nitrogen base w/o amino acids , 2% glucose , 0 . 2% yeast synthetic complete mixture , 2% agar if necessary ) was used for the adenine rich environment control . Synthetic dextrose minimal medium ( SD: 0 . 67% yeast nitrogen base w/o amino acids , 2% glucose ) was used for the cell synchronization experiment . Total RNA was extracted according to the hot acid phenol method as described in [30] , followed by DNase I ( Promega ) cleanup to remove contaminating genomic DNA as described in [31] . The fractionated RNAs were used in 3′-RACE and real-time quantitative PCR . After reverse transcription , 1 µl cDNA templates were used for the quantitative PCR assay to compare expression levels of relevant genes [32] . For every tested strain , we took 3 independent clones as biological replicates for the PCR analysis . For each of the biological replicates , there were 3 technical replicates . Expression level was presented as the ratio of normalized target concentrations ( ΔΔCt ) , as suggested elsewhere [33] , [34] . Protocols for 3′-RACE were implemented as described previously in [35] . In detail , the fractionated RNA was reverse transcribed using the Oligo ( dT ) anchor primer . The cDNA was then amplified with the 3′-end PCR anchor primer and gene-specific primers . After gel-purification , a nested PCR with the second anchor primer was conducted as necessary . We inferred the sequence reads to be the 3′-end of transcripts whenever poly-A appeared . The length of 3′-UTRs was counted up to and including the last nucleotide base before the poly-A . The sequence of the anchor primers is available from the commercial protocol ( 3′-RACE System for Rapid Amplification of cDNA Ends , Invitrogen ) . Before newly transcribed RNA undergoes maturation processing ( i . e . nascent RNA ) , the transcripts contain a segment which will be cleaved during the process of RNA maturation . Based on this structural feature of nascent RNA , together with the mRNA sequence and genomic sequence data , we designed nascent RNA specific reverse transcriptional primers to profile the expression of the nascent RNA . We tested for nascent expression of two pairs of convergent genes in the yeast genome using their nascent RNA specific reverse transcriptional primers as follows: ADE1-nascent-RT1 ( 5′-CACTGGCAAACAAGATATCG-3′ ) , APT1-nascent-RT1 ( 5′-ATATTACTAT TGCATATGCAGGTC-3′ ) , KIN3-nascent-RT1 ( 5′-AGAGACTGGCTTACTGCTAATAAG-3′ ) , and UNG1-nascent-RT1 ( 5′-AAATGATATGTTTCACGTCCTG-3′ ) . Luciferase assays followed the protocol described previously [36] , [37] using the dual luciferase reporter ( DLR ) kits ( Promega ) . In detail , the tested cells were grown in rich medium ( YPD , 30°C ) until the logarithmic phase ( OD600 = 0 . 7–0 . 9 ) . After washing and re-suspension in 1×PBS , the cultured cells were maintained in 100 µl 1× passive lysis buffer for 15 s . An aliquot of 5 µl was then extracted from the buffer for scoring luminescence measurements with 25 µl LAR II reagent from a Lumat LB 9507 ( Berthold Technologies ) set with 2 s delay time and 10 s measurement time . The same procedure was implemented after 25 µl Stop & Glo reagent was added . The protein expression level was recorded as the ratio of the firefly luciferase activity to the Renilla luciferase activity ( Fluc/Rluc ) . For each test strain , at least three independent cultures were assayed . The protocol for cell-cycle synchronization was implemented as described previously [38] . The α pheromone-responsive strain YL1A ( MATa , bar1Δ ) was used to achieve cell-cycle synchronization in exactly the same genetic background as YL1C , which differs only in the mating type . The BAR1 gene of YL1A was knocked out so that the strain would respond to a low density of α-factor [39] , [40] . The logarithmic phase cells ( OD600 = 0 . 5 ) were arrested and incubated for 1 . 5 hours once the α pheromone level reached 50 ng/ml . The cells were subsequently released from arrest by pelleting after repeated washing with pre-warmed ddH2O ( 30°C ) . The cells were then suspended in pre-warmed SD medium with Pronase E ( 0 . 1 mg/ml Pronase E , pH 6 . 4 , 30°C ) for 10 min . Subsequently , 15 ml of the prepared cell samples was taken every 20 minutes over the next two hours ( approximating a complete cell-cycle ) for extracting RNA , while the cell mass was kept at 30°C . In total , expression levels were measured at 6 time points . Cells from the tested strains ( D-K1 through to D-K7 ) were first cultured in rich medium ( YPD ) overnight , and then diluted to 3×105 cells/ml ( OD600 = 0 . 01 ) . The diluted cultures were dropped on the adenine barren medium ( synthetic dropout medium , SC-A ) or the adenine rich medium ( synthetic complete , SC ) respectively . The cultures were further diluted in a gradient and 5 µl of every diluted culture was dropped on the test plates , which were incubated at 30°C for 48 hours . 5 ml yeast cells of wild-type YL1C were grown to the logarithmic phase in SD medium . Cells were then washed twice with sterilized water , and re-suspended in 5 ml ultrapure water . Cells were counted using a hemocytometer and diluted with reverse transcription buffer ( Invitrogen ) to only one cell per 7 µl . 7 µl aliquots were deposited into each well of a 384-well cell plate ( Corning ) . We identified wells containing only a single cell ( single-cell well ) using an inverse microscope . The method used to profile gene expression in a single cell was slightly modified from the documented protocol [41] , briefly described as follows . 1 µl mixture of lyticase ( 2 µg/µl , Sigma ) , DNase I ( 1 unit/µl , Promega ) and RNase OUT ( 2 uint/µl , Invitrogen ) were added to the single-cell well , and these wells were incubated for 15 min at 30°C , then for 15 min at 37°C to lyse the cells . Another 1 µl mixture of Proteinase K ( 0 . 1 µg/µl ) and human total RNA spike-in control ( 10 ng/µl ) was added to the single-cell lysate and incubated for 10 min at 65°C . Each microliter of the reverse transcriptase mixture ( 1 mM dNTP , 5 µM oligo-dT , 5 µg/µl BSA , and 20 unit/µl SuperScript III ) was used to initiate the aforementioned single-cell reverse transcription . To enhance the template level for quantitative PCR assay , we first performed a 15-cycle nested PCR for KIN3 and ADE1 genes , with human ACT1 as the spike-in control . The nested PCR primers in 5′ to 3′ direction were GCCACAACATACGTCGGTACA and AGGATTTTTTCAATGTTTGTCAGC for KIN3 , TCTTCACCCCATCGACCAA and CAGTAAGCCAGTCTCTTAAAAATTGC for ADE1 , GCACAGAGCCTCGCCTTT and CGTGCTCGATGGGGTACTTC for ACT1 . A 0 . 5 µl aliquot of PCR product was used as the template for the next round of RT-PCR , using the following primers: GCCACAACATACGTCGGTACA and GGGAGTATGGTTGGTCCATCA for KIN3 , TCTTCACCCCATCGACCAA and GGGCAGGAGAGATGTTTTCG for ADE1 , GCACAGAGCCTCGCCTTT and GTTGTCGACGACGAGCG for ACT1 . Microarray expression datasets were downloaded from the NCBI Gene Expression Omnibus ( GEO ) database under the series accession numbers GSE19213 , GSE13684 and GSE5185 ( http://www . ncbi . nlm . nih . gov/geo ) . These datasets were collected from an S . cerevisiae wild type strain BY4741 cultivated in seven different environments ( Table S3 ) . The tested cells were harvested at the early log ( or exponential ) phase ( OD600 = 0 . 3–0 . 4 ) . Total RNA was extracted and processed according to the manufacturer's instructions ( www . affymetrix . com ) . The Yeast Genome 2 . 0 ( YEAST 2 . 0 ) microarray , which contained 5 , 744 probe sets interrogating all annotated ORFs in the S . cerevisiae genome , was employed to profile transcript abundance of the ORFs . Transcript abundance was extracted from raw hybridization signal intensities of each probe set using the Robust Multichip Average ( RMA ) method implemented in R [42] . The expression value for each ORF was log2-transformed prior to analysis . | In the compact genome of the budding yeast Saccharomyces cerevisiae , genes are frequently organized into convergent pairs that are transcribed from opposing DNA strands in opposite directions and have overlapping 3′-UTRs . Here we explore the negative correlation in expression levels between convergent genes using a set of 645 convergent pairs in the yeast genome , identified from published genomic and transcriptomic sequence data and accounting for ∼20% of total yeast genes . Analysis of published microarray experiments confirmed that the negative correlation in expression between convergent genes occurs across a broad range of growth conditions . This implies that such transcriptional interference is an important means of regulating gene expression in yeast , especially in the absence of other eukaryotic RNA-dependent mechanisms such as micro RNAs . We focused on profiling the expression of four pairs of convergent genes in wild type yeast and its genetically modified strains , to explore the causes and mechanisms of transcriptional interference . We demonstrate that the 3′-UTR sequence alone plays the essential and causal role in interference between convergent genes . Intriguingly , transcriptional interference occurs even when one of the convergent genes is expressed from elsewhere in the genome ( in trans ) , raising new questions about how transcriptional interference operates . | [
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"cere... | 2014 | 3′ Untranslated Regions Mediate Transcriptional Interference between Convergent Genes Both Locally and Ectopically in Saccharomyces cerevisiae |
Health is a multidimensional landscape . If we just consider the host , there are many outputs that interest us: evolutionary fitness determining parameters like fecundity , survival and pathogen clearance as well as medically important health parameters like sleep , energy stores and appetite . Hosts use a variety of effector pathways to fight infections and these effectors are brought to bear differentially . Each pathogen causes a different disease as they have distinct virulence factors and niches; they each warp the health landscape in unique ways . Therefore , mutations affecting immunity can have complex phenotypes and distinct effects on each pathogen . Here we describe how two components of the fly's immune response , melanization and phagocytosis , contribute to the health landscape generated by the transcription factor ets21c ( CG2914 ) and its putative effector , the signaling molecule wntD ( CG8458 ) . To probe the landscape , we infect with two pathogens: Listeria monocytogenes , which primarily lives intracellularly , and Streptococcus pneumoniae , which is an extracellular pathogen . Using the diversity of phenotypes generated by these mutants , we propose that survival during a L . monocytogenes infection is mediated by a combination of two host mechanisms: phagocytic activity and melanization; while survival during a S . pneumoniae infection is determined by phagocytic activity . In addition , increased phagocytic activity is beneficial during S . pneumoniae infection but detrimental during L . monocytogenes infection , demonstrating an inherent trade-off in the immune response .
Infected fruit flies get sick in ways that human patients would recognize; bacterial infections in Drosophila induce changes in feeding , metabolism and circadian rhythm , and conversely changes in these pathways influence susceptibility to infection [1]–[3] . Many responses affect survival during infection , but this work remains splintered as the field primarily focuses on individual mechanisms in isolation , offering glimpses of the whole picture . Here we use mutations in two genes , ets21c and wntD , to examine their effect on immune responses and survival during infections with two bacteria , Listeria monocytogenes and Streptococcus pneumoniae . Both genes affect multiple arms of the immune system and we wanted to understand how immunity offers protection against pathogens with different lifestyles in Drosophila . We chose these two microbes because they produced dissimilar phenotypes in previous Drosophila immunity assays [4] . Together these mutants and microbes demonstrate how there can be no perfect immune response , as there are responses that are beneficial during one infection and actively detrimental during another . The Drosophila immune response can be divided into categories based on the speed at which they act following pathogenic challenge . The fast-acting immune responses , which respond within seconds to minutes , are phagocytosis and melanization [5] . Hemocytes are phagocytic cells in the fly and they are concentrated in adherent groups on the dorsal side of the abdomen and the anterior abdominal segment of the heart in adult flies . Inhibition of phagocytosis increases susceptibility to a number of bacteria [6]–[9] . Insects produce melanin from tyrosine using the enzyme phenoloxidase , which is activated by an immune triggered proteolytic cascade . This process is hypothesized to produce reactive oxygen species , which can harm the host in addition to harming the pathogen , and to physically encapsulate the invaders [10] , [11] . In Drosophila , some bacterial pathogens ( L . monocytogenes , Salmonella typhimurium , and Staphylococcus aureus ) induce visible melanization , and flies defective in the melanization activation pathway are less resistant to these infections [4] . Though these relatively quick responses presumably remain active through the whole infection there is at least one response that takes several hours to reach full force . This slow response is the induction of anti-microbial peptides which peak in transcript expression six to 24 hours post infection [12] . We do not know when actual antimicrobial activity peaks as this is seldom assayed directly , but presumably this takes even longer than the increase of transcripts . While it may be simplest to examine the effect of immune components individually , in order to effectively control immunity clinically we need a better understanding of the full immune network; each response doesn't exist in a vacuum . Knowing which immune responses strongly associate with a positive outcome for a given pathogen and which physiological systems are impacted by infection will allow doctors to more effectively treat disease . Patients normally do not have a single pathway or gene responsible for their entire pathology , and we need to develop the tools to deal with these levels of complexity . To probe changes in the immune response , we turned to two pathogens that previously exhibited opposing phenotypes: Listeria monocytogenes and Streptococcus pneumoniae . When injected into the hemocoel , L . monocytogenes causes lethal infections in Drosophila melanogaster at doses as low as ten bacteria , and death from infection occurs on the order of one week . L . monocytogenes lives both intracellular and extracellular in the fly and causes robust disseminated melanization [4] , [13]–[15] . S . pneumoniae can also cause lethal infections; however , there are sub lethal doses , which prime the fly to become resistant upon subsequent challenges [16] . S . pneumoniae infection kills flies rapidly , within two to four days , and flies surviving past four days have likely cleared the pathogen . S . pneumoniae is an extracellular pathogen and bead inhibition of phagocytosis increases susceptibility to infection [6] . In contrast to L . monocytogenes , flies deficient in melanization are more resistant to S . pneumoniae infection , although the mechanism is unknown [4] . Ets21c ( CG2914 ) , a putative transcription factor characterized by its DNA binding ets-domain , was previously implicated in Drosophila immunity . Ayres et al . found that et21c mutants died more rapidly during L . monocytogenes infection with similar bacterial loads compared to wild-type , but were no different from wild type flies when challenged with S . typhimurium or S . aureus [17] . Studies of immune signaling in Drosophila S2 cells and hemocyte cell lines used ets21c transcript as a read out of the early immune response and showed that ets21c induction depends on the imd pathway and one of its transcription factors , basket [18] , [19] . WntD ( CG8458 ) is a negative regulator of dorsal signaling in Drosophila , and wntD mutants are more susceptible to L . monocytogenes infection than wild type flies [20] . Previous work measured the signaling and transcriptional effects of wntD on antimicrobial peptides [21]; however , it remains unknown , whether wntD impacts bacterial load during L . monocytogenes infection and how it affects melanization and phagocytosis . The effect of a given bacterial load has previously been used to categorize genes as either impacting tolerance or resistance [2] , [4] , [17] . Resistance genes and mechanisms directly impact how well the bacteria grow or are killed , while tolerance genes and mechanisms affect the host's ability to deal with the effect of infection ( e . g . energy strain , accumulated damage ) . While both of these mechanisms are functionally distinct , they way they impact bacterial load cannot be as easily separated and there is a full spectrum of phenotypes possible , from genes that do not impact bacterial load at all to genes that increase bacterial load by hundred-fold in just a day . Determining where in this spectrum our mutants fall helps inform the possible responsible mechanisms . In this paper , we show that ets21c and wntD mutants are both more susceptible to L . monocytogenes and more resistant to S . pneumoniae , but differ in their ability to control L . monocytogenes bacterial loads . At the levels of specific immune responses , these mutants share an increase in phagocytic activity and a shift in anti-microbial peptide induction , but differ in their melanization capabilities . By examining these differences , we establish the relative contributions of the immune pathways to these outcomes - survival during L . monocytogenes infection depends on multiple factors: melanization and phagocytic ability while phagocytic ability alone predicts survival to S . pneumoniae infection .
Ets21c is a putative transcription factor therefore we assessed the impact of an ets21c mutation on the transcriptome by performing a microarray analysis on infected flies . Complete microarray data is available in the online supplemental materials ( Dataset S1 ) . A familiar gene emerged in our list of infection induced genes in the parental line: wntD . WntD is a negative regulator of the dorsal pathway and wntD mutants die more quickly during L . monocytogenes infections [20] . Ets21c mutant flies do not upregulate wntD during L . monocytogenes infection and we confirmed this using real-time qRT-PCR ( Figure 1A , p<0 . 001 ) . S . pneumoniae induces expression of wntD in both the ets21c mutant and its parental line , but only 25-fold , which is lower relative to the hundred-fold induction during L . monocytogenes infection . The reciprocal effect of wntD mutants on ets21c expression was examined both in the microarray published by Gordon et al . and by qRT-PCR , but the levels of ets21c were so low in total fly RNA preparations that results were highly variable and therefore not significant ( data not shown ) . WntD is a good candidate effector for ets21c's immune phenotypes , due to wntD's ability to impact survival to L . monocytogenes . Ayres and colleagues published that ets21c mutants have increased susceptibility to L . monocytogenes with no significant increase in bacterial load causing them to conclude that the gene affected tolerance [17] . This mutant did , however , show an insignificant increase in L . monocytogenes bacterial load two days post-infection . Upon retesting , we found that these mutants exhibit a small but significant increase in bacterial load at 48 hours post infection ( Figure 2A , E ) . We call this a small effect since there is no change at 24 hours and a nine fold increase in bacterial load at 48 hours whereas a mutation in another gene , gr28b , increases bacterial growth 100 fold at both 24 and 48 hours . This relatively small increase in bacterial growth rates in ets21c mutants was confirmed with flies that had ets21c expression knocked down by RNAi in the fatbody ( Figure 3 A , B ) . Gordon and colleagues showed that wntD mutants were more susceptible to L . monocytogenes , but did not report bacterial loads [20] . We confirmed that wntD mutants die faster than parental flies ( Figure 2B ) , and found that wntD mutants remain able to control bacterial growth to at least 48 hours post-infection ( Figure 2F ) . We stopped measuring CFU at this point as over half the mutant flies die the next day and we worried that we would skew results if we were looking at survivors that may be more resistant or potentially received a smaller infectious dose . Knockdown of wntD in the fatbody confirmed wntD's effect on tolerance to L . monocytogenes ( Figure S1 ) . Mutants in ets21c and wntD are both more susceptible to L . monocytogenes infection , but fall on different parts of the tolerance-resistance continuum indicating that there may be mechanistic difference at play in these lines . Drosophila has an excellent tool for knocking down gene expression in a tissue specific manner; tissue specific expression of the transcription factor GAL4 can be used to drive gene specific RNAi constructs . A large number of these tissue specific GAL4 lines and RNAi lines are publically available and one simply has to cross the driver line to the RNAi line and test the appropriate offspring . However , these lines are not perfectly tissue specific and can have significant expression levels in a variety of tissues . We tested a panel of GAL4 drivers to determine where ets21c was required during infection and found it difficult to interpret the data because all drivers tested produced a similar phenotype ( Figure S2 ) . We reasoned that the problem was that driver localizations are primarily determined by ability to drive GFP expression in tissues; however , we worried that low expression levels of an RNAi construct might be sufficient to produce a phenotype while registering as background in a fluorescent microscopy assay that measured the induction of GFP . Instead of using the published localization for each driver , we assumed that the driver strength matched the expression data for each driver gene as reported by FlyAtlas , a database of tissue specific gene expression results from both larvae and adults . For each tissue , we used JMP Software ( http://www . jmp . com ) to determine whether there was a significant correlation between the expression level of the driver gene with the strength of immune phenotype as measured by median time to death ( MTD ) of RNAi×Driver/MTD of the driver control . As shown in Figure 4 , higher expression of the driver genes in both the heart and fatbody correlated with increased sensitivity to L . monocytogenes infection . However , correlation of sensitivity to infection and driver strength in the heart was not as strong and also had a significant p-value for the lack of fit test indicating that the linear model may not be appropriate for this tissue . Driver expression values in the heart correlate with those in the fatbody ( data not shown ) , so it is unsurprising that they would both exhibit a similar relationship with the sensitivity to L . monocytogenes infection . This data does not rule out a role for hemocytes as they are not reported in FlyAtlas and could potentially have adhered to other tissues , particularly the heart , during the isolation used for the FlyAtlas data . The closest available approximation for adult hemocytes was the expression data available for growing S2 cells in culture , which are known to have phagocytic properties , and microarray data published on larval hemocytes . Both of these cultured cells revealed a significant correlation between strength of driver expression and sensitivity to L . monocytogenes infection by ANOVA , but also had significant p-values for the lack of fit test indicating that the model may be inappropriate ( data not shown ) . While it is unsurprising that the fatbody and/or hemocytes are important for our immune phenotype , this technique can be broadly applied to any phenotype that can be quantified and will allow quantitative and methodical use of drivers . Ets21c mutants had been previously shown to have no impact during S . typhimurium or S . aureus infection , while published studies on wntD mutants only examined the impact during L . monocytogenes infection [17] , [20] , [21] . We chose to further test the specificity of ets21c with Streptococcus pneumoniae because it behaves uniquely in some other Drosophila immunity mutants [4] , [22] . Mutants in both ets21c and wntD survive S . pneumoniae challenge better ( Figure 2C–D ) and with decreased bacterial loads compared to parental strains ( Figure 2G–H ) . This phenotype was also confirmed using RNAi knockdown in the fatbody as described above ( Figure 3B , D and Figure S1B , D ) . These results indicate that mutants in ets21c and wntD both have an increased resistance to S . pneumoniae infection , whereas they exhibited differences in their type of susceptibility to L . monocytogenes . To examine which immune responses are responsible for the differences between ets21c and wntD mutants , we performed assays of each immune pathway to assess the strength of both fast acting and slow acting immune responses . Melanization and phagocytosis begin to act within seconds to minutes of infection , while anti-microbial peptide induction takes hours . It can be difficult to measure the strength of immune responses against pathogens; pathogens often evolve mechanisms to beat the immune response . To assess the strength of the fast acting responses we injected flies with Escherichia coli , which is non-pathogenic to D . melanogaster at this dose and is rapidly cleared from circulation . By focusing on the first hour post-injection , we assayed the fast acting responses of phagocytosis and melanization . While this is a very different microbe from both L . monocytogenes and S . pneumoniae , basic immune responses are likely conserved between infections and this is a first approximation of fast-acting responses . Ets21c mutants do not clear E . coli more quickly ( Figure 5A ) . WntD mutants , however , have significantly increased E . coli clearance ( Figure 5B ) . This result does not distinguish whether shifts in melanization or phagocytosis are occurring , and does not rule out the possibility that ets21c mutants have shifts in both melanization and phagocytosis for an overall neutral effect on E . coli clearance . To determine the potential contribution of each of the fast acting immune responses to the observed phenotypes , we wanted to test both phagocytic ability and melanization capabilities separately . We determined the strength of phagocytic ability by assaying the ability of our mutants to phagocytose dead bacteria . We imaged flies injected with a pHrodo labeled E . coli , which only fluoresces upon encountering a low pH like that found in phagosomes; while pHrodo labeled L . monocytogenes would be the more perfect tool it isn't commercially available and the labeled E . coli provides an initial approximation for phagocytic ability . Quantifying the amount of fluorescence revealed that each mutant phagocytosed more bacteria than their parental line ( Figure 6 ) . There was also a dramatic difference in the phagocytic activity of the two parental lines , as evidenced by the fact that we had to use twice as long an exposure to take images of w1118 and ets21c mutants as compared with yw and wntD mutants . No direct comparison between all four lines was done because exposures which can capture w1118 and ets21c mutant differences completely over-exposes the other lines . These four lines offer a spectrum of phagocytic abilities giving us a broad dynamic range to assess the importance of phagocytosis during infection . L . monocytogenes is a facultative intracellular bacterium that is capable of escaping the phagosome and living within the cytosol of phagocytic cells [15] , [23] , [24] . While an increase in phagocytosis may help clear extracellular pathogens such as S . pneumoniae , this increase may give additional access to a niche for L . monocytogenes . We tested this by determining how many intracellular L . monocytogenes were found in infected flies . Gentamicin is an antibiotic that is unable to cross cell membranes and injecting this antibiotic into the fly's circulation allows us to measure intracellular and extracellular populations . Figure 7 shows that both wntD and ets21c mutants have increased intracellular bacterial loads compared with their parental line . The water injected control , which reports total bacteria , further confirms that ets21c mutants also have an overall increase in bacterial load at 48 hours post-infection while wntD mutants have no significant change . When comparing the intracellular populations for all four lines at once , we noticed that the intracellular population was positively associated with phagocytic ability ( Figure 7C ) . We assayed the second fast immune response , melanization , by looking at the capability of the flies to show disseminated melanization after infection . L . monocytogenes causes visible melanotic spots within 3–4 days after infection , and flies defective in melanization are more susceptible to infections that cause this disseminated melanization [4] . When flies were scored as positive or negative for melanization , a significantly lower proportion of ets21c mutants exhibited visible disseminated melanization ( Figure 8A ) , while a higher proportion of wntD mutants exhibited disseminated melanization ( Figure 8B ) . We examined anti-microbial peptide ( AMP ) gene induction in these two mutants . Gordon et al . reported that wntD mutants had increased induction of diptericin upon L . monocytogenes infection but saw no change in the induction of drosomycin [20] . We also found that ets21c mutants also have a four-fold increased induction of diptericin ( p<0 . 05 ) ( Figure 9A ) and no change in drosomycin induction ( data not shown ) . We also tested attacin , metchnikowin , defensin , drosocin and cecropin for changes during L . monocytogenes infection and found that ets21c only affected cecropin expression in that it was poorly induced during infection , about 10-fold lower than its parental line ( p<0 . 001 ) ( Figure 9B ) . In the microarray , ets21c mutants also showed up-regulation of most anti-microbial peptides , and did not show significantly different induction than the parental line ( Figure S3 ) . We suggest that this is a minor but complex impact on anti-microbial peptide expression as the majority of transcripts do not change and when they do change they can go up or down . While individual anti-microbial peptides have been shown to impact survival to infection in Drosophila , their effect was only visible in an otherwise immune compromised mutant with forced high expression of the anti-microbial peptide [25] . We do not understand the impact of modest changes in AMPs in a background where many AMPs are highly expressed and are unchanging .
The diverse and often opposing strengths and weaknesses of different pathogens leads to inherent trade-offs in immunity . In order to observe these trade-offs , one must infect with a range of pathogens and explore multiple arms of the immune response . This research used mutants in two genes to explore the contribution of two immune components: phagocytosis and melanization to the survival during infections with two bacteria: L . monocytogenes and S . pneumoniae . The line most resistant to S . pneumoniae dies the fastest when faced with L . monocytogenes and the reciprocal also holds true ( Figure 10 ) . The differences between these pathogens make them useful tools with which to probe the immune system . This paper focuses primarily on the immune contribution of the fly's phagocytic ability . Due to differences in pathogen lifestyle , the increased phagocytosis in our mutants has unique consequences for each bacterium . S . pneumoniae is an extracellular bacterium , and phagocytosis is a death knell . L . monocytogenes can harness phagocytosis as an entry way to a protected niche . Our work shows that flies with the most phagocytosis are most susceptible to L . monocytogenes and have the correspondingly highest intracellular populations of L . monocytogenes while also being most resistant to S . pneumoniae . This presents a perplexing dilemma for the design of a robust immune system – what is the most advantageous amount of phagocytosis ? This will depend on the frequency of pathogens encountered that will take advantage of this potential niche . While it might be tempting to explain the range of phenotypes simply by the amount of phagocytic ability , the approximately equivalent survival of the ets21c mutant and yw parental line in spite of a difference in phagocytic ability suggests that additional factors might be at work . A second ingredient , melanization , is potentially that additional factor . The differential impact that ets21c and wntD have on melanization offers an explanation for why ets21c affects resistance while wntD affects tolerance to L . monocytogenes . Too little melanization is detrimental during a L . monocytogenes infection and causes increased extracellular bacteria [4] . Ets21c mutants decrease , but do not obliterate of the ability to melanize and have a corresponding significant but mild difference in their resistance to L . monocytogenes . WntD mutants , however , have an increased proportion of flies showing melanization compared to their parental line . The effects of hyper-melanization during infection are unknown , but production of melanin results in the production of reactive oxygen species ( ROS ) which can potentially harm the host as well as the microbe . If this increase in melanization in WntD mutants harms the host through ROS production , it could help explain the mutant's defect in tolerance to L . monocytogenes . The flies may become more dependent on this immune response and cause futile damage in their efforts to contain the bacteria . Our results relegate anti-microbial peptides to a supporting role , primarily because we saw transcriptional changes in very few AMPs . Knockout of a single AMP has never been reported to produce a survival phenotype and we observed both increases and decreases of individual AMP induction . However , our data does not rule out the possibility that there is an AMP which can specifically affect either S . pneumoniae or L . monocytogenes and influence the phenotype . We believe this is unlikely because of the negative association between the two phenotypes and over-expression of a single AMP would have to be capable of producing the opposite effect on the other bacteria . Another potential factor that this paper does not directly address is energy investment . Implicit in a stronger response is the energetic cost of mounting that response . While we do not measure this cost in our mutants , this is still a factor that could be influencing their survival outcome , especially of wntD mutants . These flies have a hyper-melanization response and increased phagocytic ability which may restrict the fly's access to a crucial amino acid – tyrosine , which is the precursor for melanin production . This could be a contributing factor to why these flies die so quickly compared to the other lines , while having the “strongest” of each of the immune responses . A robust immune system must have an appropriate balance of immune responses to account for the diversity of pathogens it will encounter; however , even a well designed immune system will contain tradeoffs . A better appreciation of the natural and inevitable antagonism will help us gain a more in-depth appreciation for the evolutionary history behind our immune systems . Encouraging scientists to embrace pathogens which reveal distinct and even opposite phenotypes is necessary to fully explore the robustness and complexities of the immune response .
For ets21c experiments , a piggybac allele ( Bloomington 18678 ) was compared to its parental strain , white1118 ( Bloomington 6326 ) and an RNAi fly line ( Vienna 106153 ) was crossed with GAL4 driver lines to elicit knockdown of ets21c . For wntD experiments , the knock out strain WntDKO1 was compared to its parental line yw and an RNAi fly line ( Vienna 15146 ) was crossed to GAL4 driver lines to elicit knockdown of the WntD . [20] For RNAi experiments the following GAL4 driver lines from Bloomington were used: collagen25c ( 7011 ) , mef-2 ( 27390 ) , daughterless ( 8641 ) , act5c ( 4414 ) , elav ( 8765 ) , lsp2 ( 6357 ) , hemese ( 8700 ) , and hemolectin ( 6395 ) , appl ( 32040 ) . RNAi experiments were conducted by crossing virgins from the RNAi line to males from driver line and collecting the progeny . If driver lines or RNAi lines contained a balancer , the progeny without the balancer were selected . Two control crosses were used for each RNAi experiment; an RNAi control with RNAi line virgins crossed to w1118 and a driver control with w1118 virgins crossed to males from the driver line . Listeria monocytogenes ( strain 10403S ) cultures were grown in 4 ml brain heart infusion ( BHI ) broth at 37°C without shaking after inoculation from L . monocytogenes grown overnight on a Luria Bertani ( LB ) agar plate . L . monocytogenes stocks were stored at −80°C in BHI broth containing 15% glycerol . Streptococcus pneumoniae ( strain SP1 ) cultures were grown standing at 37°C 5% CO2 in BHI broth to an OD600 of 0 . 15 , and aliquots were frozen at −80°C in 10% glycerol . For infection , an aliquot of S . pneumoniae was thawed , diluted 1∶3 in fresh BHI broth and allowed to grow at 37°C 5% CO2 for 3–4 hours . Escherichia coli ( strain DH5a ) cultures were grown in 4 ml LB broth at 37°C with shaking after inoculation from bacteria grown overnight on a Luria Bertani ( LB ) agar plate . E . coli stocks were stored at −80°C in BHI broth containing 15% glycerol . For infection , 50 nL of the bacterial cultures were injected at the following optical densities ( OD600 ) : L . monocytogenes , 0 . 01 ( approx . 1 , 000 CFU/fly ) ; S . pneumoniae , 0 . 05–0 . 3 ( approx 2 , 000–10 , 000 CFU/fly ) ; E . coli , 0 . 1 ( approx . 3 , 000 CFU/fly ) . Five to seven day post-eclosion male flies were used for injection . The flies were raised at 25°C , 65% humidity on yeasted dextrose food in a light cycling incubator ( 12 hours dark , 12 hours light ) . Flies were anesthetized with CO2 . A picospritzer ( Parker Hannin , http://www . parker . com ) was used to inject 50 nL of liquid into each fly with pulled glass capillary needles that were individually calibrated by measuring the size of the expelled drop under oil . About 20 flies were placed per vial and then experiments were kept at 29°C , 65% humidity in a light cycling incubator . Mutant flies and the parental control or RNAi crosses and RNAi/Driver controls were injected with 50 nL of the bacterial culture or medium . About sixty flies were assayed for each condition and placed in three vials of 20 flies each . Death was recorded daily . Survival curves are plotted as Kaplan-Meier plots and statistical significance is tested using log-rank analysis using Prism software ( http://www . prism-software . com ) . All experiments were performed at least three times and yielded similar results . Colony forming units ( CFUs ) were determined using both spot-plating and an autoplate spiral plater ( Spiral Biotech http://www . aicompanies . com ) . For spot-plating , eight individual flies were collected at each time point . These flies were homogenized , diluted serially and plated onto the appropriate media ( blood agar for S . pneumoniae and LB agar for L . monocytogenes and E . coli ) and grown overnight at 37°C ( 5% CO2 for S . pneumoniae ) . Some L . monocytogenes experiments were completed using the Spiral Biotech plater and for these six individual flies were homogenized and diluted . 50 µL of liquid was plated exponentially on a LB plate , grown overnight at 37°C and then counted using QCount , which back calculates the original number of CFU per fly . For statistical analysis , if CFU/fly did not approximate a Gaussian distribution we analyzed the log ( 10 ) transform of the data . Most CFU experiments were assessed for sources of variation using a two-way ANOVA and followed with Bonferroni post-tests for specific comparisons of interest . Gentamicin chase assays were performed as described by Ayres et al . 2008 . [4] For the zero hour time point , flies were pre-injected with 50 nL of water or gentamicin . Flies were then injected with 50 nL of L . monocytogenes and put at 29°C . The flies were incubated for three hours and then plated to determine CFUs as described above . For 24 and 48 hour time points , flies were injected with 50 nL of water or gentamicin , incubated for three hours at 29°C and similarly plated . The statistical significance of specific comparisons of interest was assessed using a two-tailed t-test . Flies with either injected with 50 nL of L . monocytogenes or BHI broth , simply stabbed with an empty needle or left unmanipulated . They were placed at 29°C for 6 hours . Groups of 20 flies were flash frozen in a dry ice/ethanol bath and then homogenized in TriZOL . Additional flies were injected and monitored for survival and CFUs to ensure adequate infection . RNA was isolated using a standard TriZOL preparation and then labeled cDNA was generated and hybridized to the Genome Drosophila Array ( 2 . 0 ) as described in the Affymetrix protocol ( Affymetrix , http://www . affymetrix . com ) . Gene lists were assembled using comparisons done with dCHIP ( http://biosun1 . harvard . edu/complab/dchip ) . Anti-microbial peptide heatmap ( Figure S3 ) created in Genespring 12 . 0 . Select genes were confirmed by qRT-pCR . Flies were injected with 50 nL of the indicated microbes or kept unmanipulated . Following injection , the flies were placed in dextrose vials and incubated at 29°C for six hours . Groups of 12 flies were homogenized in TriZOL and stored at −80°C until processed . RNA was isolated using a standard TriZOL preparation , and the samples were treated with DNase ( Promega , http://www . promega . com ) . Quantitative RT-PCR was performed as described previously by Schneider et al . using a Bio-Rad icycler and the following primer sets: WntD , Diptericin , Cecropin , and RpS15Aa ( for primer sequences see Table S1 ) [26] . These assays were performed as described by Shirasu-Hiza et al [6] . Briefly , flies were injected with 50 nL of 1 mg/ml pHrodo labeled E . coli ( Molecular Probes , cat# P35361 ) and allowed to phagocytose at room temperature for 30–60 minutes . The wings of the flies were removed and the flies pinned onto a silicon pad with a minutien pin . Fluorescent images were taken of the dorsal surface using epifluorescent illumindation with Leica MZ3 microscope fitted with an ORCA camera . Images were captured with Openlab ( Improvision ) , and exposures were set so that the brightest images showed no saturated pixels . Each experiment was repeated three times with 6–12 flies with similar results . These assays were performed as described by Ayres et al [2] . Briefly , four days after injection flies were visualized by light microscopy and scored for a disseminated melanization response . Flies that melanized beyond the initial site of injection were scored positive for melanization response . Flies that only melanized at the site of injection were scored as negative for a melanization response . | The importance of individual immune responses is incredibly infection dependent , and this paper harnesses the variability in two mutant lines to explain the relative importance of two aspects of fly immunity: melanization and phagocytosis . Increased phagocytic activity is beneficial during S . pneumoniae infection due to increased clearance of the extracellular microbe and detrimental during L . monocytogenes infection as it increases the intracellular niche for L . monocytogenes . Outcomes during L . monocytogenes infection are also dependent on melanization capability , which impacts the ability to control extracellular bacteria . | [
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] | 2012 | How the Fly Balances Its Ability to Combat Different Pathogens |
Cells rely on a network of conserved pathways to govern DNA replication fidelity . Loss of polymerase proofreading or mismatch repair elevates spontaneous mutation and facilitates cellular adaptation . However , double mutants are inviable , suggesting that extreme mutation rates exceed an error threshold . Here we combine alleles that affect DNA polymerase δ ( Pol δ ) proofreading and mismatch repair to define the maximal error rate in haploid yeast and to characterize genetic suppressors of mutator phenotypes . We show that populations tolerate mutation rates 1 , 000-fold above wild-type levels but collapse when the rate exceeds 10−3 inactivating mutations per gene per cell division . Variants that escape this error-induced extinction ( eex ) rapidly emerge from mutator clones . One-third of the escape mutants result from second-site changes in Pol δ that suppress the proofreading-deficient phenotype , while two-thirds are extragenic . The structural locations of the Pol δ changes suggest multiple antimutator mechanisms . Our studies reveal the transient nature of eukaryotic mutators and show that mutator phenotypes are readily suppressed by genetic adaptation . This has implications for the role of mutator phenotypes in cancer .
Accurate DNA replication ensures the faithful transmission of genetic information between mother and daughter cells . To accomplish this important task , organisms have evolved a network of conserved pathways that govern DNA replication fidelity ( reviewed in [1] ) . Polymerase proofreading and postreplication mismatch repair ( MMR ) are key determinants of fidelity , functioning to correct errors introduced by DNA polymerases during cell division ( reviewed in [2]–[4] ) . Defects in these and other DNA repair pathways produce mutator phenotypes , which are characterized by increased rates of spontaneous mutation . Mutator phenotypes arise spontaneously in nature and have mixed biological consequences ( reviewed in [5]–[10] ) . In Escherichia coli and other bacteria , changing environmental conditions favor high mutation rates , which increase the likelihood of genetic adaptation [11]–[16] . However , after adaptation , mutator bacteria progressively lose fitness as they accumulate deleterious mutations in other genes [14] , [17] , and clones with lower mutation rates can evolve from mutator populations [14] , [16] , [18]–[20] . Thus , mutation rates in E . coli rise and fall as populations cycle through periods of adaptive and non-adaptive growth . Mutators also impact eukaryotes . In mammals , mutator phenotypes fuel oncogenesis by providing the genetic diversity necessary for emergence of malignant clones [21] , [22] . Many sporadic human tumors show signs of an elevated mutation rate [23] , and inherited defects in polymerase proofreading [24]–[26] or MMR ( reviewed in [27] , [28] ) confer mutator phenotypes and increase cancer risk . In the budding yeast Saccharomyces cerevisiae , loss of proofreading or MMR also elevates spontaneous mutation [29]–[34] , and defective MMR can facilitate adaptation to changing environments [35] , [36] . Similar to bacteria , eukaryotic mutator alleles are detrimental in the long-term . Deleterious mutations accumulate faster in mutator compared to non-mutator yeast strains [37] , [38] , and mutators eventually become extinct in a mutational meltdown process after serial passage through population bottlenecks [39] . This decline is accelerated in yeast with extreme mutation rates . Diploids that are homozygous defective for both proofreading and MMR grow slowly and have mutations rates that are elevated 10 , 000-times above wild-type levels [40]–[42] . Double-mutant spores germinate but arrest at various cell-cycle stages after 6–7 mitotic divisions [40] , suggesting that the accumulation of DNA replication errors drives the extinction of haploid mutator strains . Here , we experimentally define the threshold of error-induced extinction in haploid S . cerevisiae and show that cells readily escape extinction via genetic suppression . These escape mutants emerge rapidly and carry second-site mutations that suppress the mutator phenotype . Our findings show that mutators are intrinsically unstable and that spontaneous suppressors moderate high mutation rates in yeast .
To obtain a range of mutator strains suitable for defining the maximal mutation rate in yeast , we conducted a mutagenesis screen of the POL3 gene , which encodes the catalytic subunit of DNA polymerase δ ( Pol δ ) . We used a plasmid shuffling strategy [29] , [43] to introduce mutated pol3 alleles into strains proficient ( MSH6 ) or deficient ( msh6Δ ) for MMR ( Figure S1 ) . The screen focused on conserved residues in the proofreading exonuclease domain of Pol δ [44] , [45] that , when mutated in msh6Δ cells , are expected to preferentially increase base-substitution and ±1 frameshift mutations [40] , [46]–[49] . Our analysis identified 21 amino acid substitutions in the Pol δ proofreading domain that individually conferred a range of increased spontaneous mutation frequencies ( Figure S2 ) . These alleles had no observable effect on colony formation in MSH6 cells . However , four alleles ( pol3-01 , pol3-F406A , pol3-D407A and pol3-Y516F ) did not yield visible colonies when shuffled into msh6Δ cells . This result is consistent with previous reports of synthetic lethality between the proofreading-deficient pol3-01 allele and MMR-defective alleles pms1Δ , msh2Δ , or msh6Δ [40] , [41] , [50] . To determine whether the loss of growth capacity correlated with mutator strength , we quantified the spontaneous mutation rates of a subset of pol3 alleles in the presence or absence of MSH6 ( Figure 1A ) . Alleles that imparted a 2- to 8-fold increase in the mutation rate of MSH6 cells ( R459A , G400A , Y401A , D396A , Y410A , K491R and D463A ) were compatible with survival when MSH6 was deleted . These pol3 msh6Δ double-mutants had mutation rates that were 15- to 150-times greater than the corresponding pol3 MSH6 strains , consistent with synergy between pol3 mutators and msh6Δ . In contrast , pol3 alleles that increased the mutation rate 25- to 50-fold in MSH6 cells ( D407A , pol3-01 , Y516F and F406A ) conferred a loss of colony-forming capacity in msh6Δ cells ( <1 colony/105 cells plated; Figure 1B ) . Thus , the transition to no colony formation occurred over a narrow range of increasing mutation rates . This abrupt loss of growth capacity implies the existence of a threshold for error-induced extinction . During our shuffling experiments , we observed occasional colonies that escaped pol3 msh6Δ synthetic lethality ( Figure 2A ) . We speculated that second-site changes in Pol δ might rescue yeast from error-induced extinction by increasing DNA replication fidelity and thereby reducing the spontaneous mutation burden . To test this idea , we sequenced pol3 plasmids from error-induced extinction ( eex ) mutants that escaped synthetic lethality between msh6Δ and pol3-01 , pol3-F406A or pol3-D407A . The plasmids retained the original pol3 mutator alleles , but also harbored additional second-site mutations in each pol3 sequence . Our initial experiment yielded three eex mutants encoding single amino-acid substitutions in Pol δ ( E594G or W821C in pol3-01; T711P in pol3-D407A ) and two mutants with multiple substitutions ( K689E , S725L and I1076V in pol3-F406A; R470C and T655A in pol3-D407A ) . Another mutation ( A894G ) was found in a large-colony variant of pol3-D463A msh6Δ cells . When the second-site eex mutations were re-engineered into new plasmids together with their corresponding mutator alleles ( pol3-01 , pol3-F406A or pol3-D407A ) , they rescued colony-forming capacity in msh6Δ cells and decreased the mutation rate of MSH6 cells 10- to 33-fold ( Figure 2B ) . The eex mutations appeared to be functionally interchangeable; T711P ( the pol3-D407A suppressor ) also rescued pol3-01 msh6Δ lethality , and either T711P or E594G ( a pol3-01 suppressor ) restored normal growth to pol3-D463A msh6Δ cells . Considered together , these initial findings suggested that eex mutations within POL3 confer escape from error-induced extinction by exerting an antimutator phenotype . To obtain a broader view of escape mechanisms , we performed a large-scale screen for mutants that suppress the synthetic lethality between pol3-01 and msh6Δ ( Figure 2C ) . Mutants emerged from nearly every pol3-01 msh6Δ parent clone , and there was wide fluctuation in the number and size of mutant colonies , suggesting that escape variants arise randomly prior to selection on FOA . We isolated 113 independent eex mutants ( Table S1 ) . Seventy-four of these eex mutants carried pol3-01 plasmids that still conferred lethality in a fresh msh6Δ strain . We infer that these mutants harbor mutations in chromosomal genes that influence DNA replication fidelity . The remaining 39 eex mutants carried pol3-01 plasmids that did not cause synthetic lethality when isolated and independently re-shuffled into msh6Δ cells . DNA sequencing of these plasmids showed that , in addition to the pol3-01 allele , each plasmid contained a different secondary mutation in pol3 . These secondary mutations encoded single amino-acid changes in Pol δ ( Figure 3 and Figure 4 ) and rescued colony-forming capacity when engineered de novo into pol3-01 plasmids and shuffled into naïve msh6Δ cells . Consistent with our initial experiment , all of these intragenic eex mutations suppressed the pol3-01 mutator phenotype , as measured at two different genetic loci ( Figure 2D and Table 1 ) . The weakest eex alleles suppressed mutation rates three-fold , while the strongest suppressors lowered mutation rates to wild-type levels ( Figure 2D ) . Thus , cells escape pol3-01 msh6Δ lethality by acquiring any one of a variety of second-site mutations that suppress the mutator effect of Pol δ proofreading deficiency . The eex mutants provided an opportunity to assess the proportion of Pol δ errors that are repaired by proofreading and MMR in vivo . Taking advantage of the viability of pol3-01 , eex msh6Δ cells , we first compared the mutation rates of isogenic strains that lack Pol δ proofreading and differ only in their MMR activity . The average increase in mutation rate in pol3-01 , eex strains after deletion of MSH6 was 157-fold ( Table 1 ) , consistent with Msh6-dependent repair of greater than 99% of the errors generated by proofreading-deficient Pol δ . As expected from the mutation biases conferred by pol3-01 or msh6Δ alone [40] , [46]–[48] , spontaneous mutations in pol3-01 , eex msh6Δ strains were almost exclusively base substitutions ( Figure S3 and Table S2 ) . Thus , our estimate primarily reflects the efficiency of base-base mismatch repair . This estimate is an average across multiple scoreable sites in CAN1; MMR efficiencies at individual sites may vary widely [51] . To similarly estimate the efficiency of Pol δ proofreading in vivo , we initially determined the influence of eex alleles on mutation rates in the presence of proofreading . Most MMR-proficient pol3-eex strains had no discernable mutator phenotype ( Figure 2D , Table 2 ) . However , in the absence of MSH6 many of the pol3-eex alleles produced slightly higher mutation rates than the POL3 msh6Δ control ( Table 2 ) . These alleles were excluded from our analysis , because their weak mutator phenotypes may result from altered partitioning or other defects that reduce proofreading efficiency [4] , [52] . Eight pol3-eex msh6Δ strains exhibited mutation rates within two-fold of POL3 msh6Δ: G204D , H620Y , T711A , E594G , Y808C , W821C , H879Y , and S968R . The mutation rates of these pol3-eex msh6Δ strains were compared to rates of the corresponding pol3-01 , eex msh6Δ cells . This strategy allowed us to examine isogenic strains that differ only in their Pol δ proofreading activity and lack the masking effects of Msh6-mediated MMR . Proofreading deficiency increased mutation rates an average of 163-fold , indicating that the Pol δ exonuclease corrects greater than 99% of polymerase errors across the CAN1 reporter gene . Assuming Pol δ proofreading and Msh6-dependent MMR act in series [40] , we estimate their combined contribution to DNA replication fidelity in yeast at greater than 104 . Proofreading and MMR contribute similarly to replication fidelity in bacteria [53] . The pol3-01 , eex MMR-proficient strains formed colonies with similar size and efficiency as the POL3 control ( Figure 5A , left ) . Thus , the corresponding eex mutant polymerases must suffice for the essential functions of Pol δ in replication [54] . However , in the absence of MSH6 , pol3-01 , eex alleles with the strongest mutator phenotypes impaired growth ( Figure 5A , center ) . We used these mutants , together with synthetic-lethal alleles , to estimate the maximal mutation rate compatible with haploid yeast proliferation . The upper and lower limits of the maximal rate were determined as follows . First , we calculated the predicted mutation rates of msh6Δ strains harboring synthetically lethal pol3 alleles ( pol3-01 , pol3-F406A , pol3-D407A or pol3-Y516F ) as the mutation rate of each pol3 MSH6 strain ( Figure 1A ) times 157 ( the average increase in rate observed upon deletion of MSH6; see preceding section ) . These predicted rates ranged from 2×10−3 Canr mutants per cell division for pol3-01 msh6Δ and pol3-D407A msh6Δ to 4×10-3 for pol3-F406A msh6Δ ( Figure 5B ) . Second , we determined the growth capacities of all mutator and suppressor strains in our collection using a semi-quantitative scale based on colony size . Wild-type colony-forming capacity ( +++ ) was consistently observed at rates as high as 5×10−5 Canr mutants per cell division ( Figure 5B ) . As the mutation rate exceeded 5×10-5 , several strains exhibited a slow-growth phenotype ( ++ ) , and a single strain ( pol3-01 , H879Y msh6Δ ) showed a severe growth deficit ( + ) at a mutation rate of 1×10−3 . These results demonstrate that the maximal mutation rate is reached when there are ∼10−3 inactivating mutations in CAN1 per cell division ( Figure 5B ) . Rates exceeding this maximum result in a failure to form visible colonies ( i . e . , error-induced extinction ) . If the observed decline in viability is due to an error threshold , additional mutation stress should exacerbate the growth defect . We introduced pol3-01 , eex alleles into msh2Δ cells , which are defective in both Msh6- and Msh3-mediated MMR and thus have Canr mutation rates that are 2- to 3-fold higher than msh6Δ cells [3] , [41] , [47] , [48] . Colonies were observed only in pol3-01 msh2Δ strains with the strongest mutator suppressor alleles ( Figure 5A , right ) . Collectively , these data suggest that pol3-01 msh6Δ cells with weak mutator suppressors are on the edge of error-induced extinction and that eliminating MSH2 increases mutation rates beyond an extinction threshold . Although pol3-01 msh2Δ strains with strong mutator suppressors formed distinct colonies , these colonies were generally smaller and less uniform than the POL3 msh2Δ control . This variability in colony size suggests that viable pol3-01 , eex msh2Δ cells quickly accumulate deleterious mutations that compromise replicative fitness . The observation that growth is impaired at mutation rates 10-times less than the 10−3 threshold ( Figure 5B ) suggests that accumulation of random mutations can impose a loss in fitness and shows that the growth capacity of haploid yeast declines even under conditions of non-lethal mutation burden .
We observed loss of growth capacity when the CAN1 mutation rate exceeds ∼10−3 inactivating mutations per cell division ( Figure 5 ) . The yeast genome is comprised of ∼6000 genes ( http://www . yeastgenome . org ) . Thus , a mutation rate of 10−3 corresponds to the random inactivation of ∼6 genes per cell per replication cycle ( assuming CAN1 is typical ) . On average , one of these six mutations will involve a gene required for haploid cell viability [55] , [56] . Thus , there is a high probability that cells above the maximal mutation rate will acquire a lethal mutation after a few cell divisions . The restoration of cell growth via antimutator alleles ( Figure 2 ) supports this hypothesis . Stalled DNA synthesis at nascent 3′ mispairs [57] and S-phase checkpoint signalling [58] could also contribute to growth arrest in strong mutators . However , it is not evident how MMR defects would exacerbate 3′ mispair extension by Pol δ , and simultaneous loss of proofreading and MMR does not halt growth specifically in S-phase [40] . Rather , proofreading/MMR double mutants arrest with varied cell morphologies [40] that resemble those observed in a systematic promoter-repression screen of essential genes [59] ( Figure S4 ) . Considered together , the evidence suggests that random mutations in essential genes are a primary cause of error-induced extinction . Synthetic cooperative interactions of non-lethal alleles will also contribute as cells accrue multiple mutations [60] , [61] . In a similar manner , bacteria exhibit a replication error threshold that correlates with the number of indispensable genes [62] , suggesting that maximal mutation rates can be used to estimate the genetic complexity of vital pathways in other organisms . Error thresholds are also evident in diploids . Although diploid genomes generally buffer cells against the deleterious effects of mutation accumulation [63] , haploinsufficient alleles still pose a significant threat to fitness . In a comprehensive library of diploid yeast heterozygotes , up to 20% of the hemizygous mutant strains exhibit reduced fitness during growth competition [64] . Observations of mutation meltdown in MMR-deficient cells [39] and lethality conferred by a hyper-mutator Pol δ variant [65] argue that diploid yeast are subject to an error threshold . The combined loss of Pol δ proofreading and MMR is also synthetically lethal in mice [25] . Similar to the situation in yeast [40] , mouse cells defective for both proofreading and MMR are initially viable but arrest after a limited number of mitotic divisions [25] . This cessation of growth presumably results from an accumulation of mutations in genes required for cell propagation and embryo development . Although cells eventually succumb to error-induced extinction , they tolerate substantial increases in mutation rate before losing viability . This mutational robustness is apparent in yeast , E . coli and mouse cells ( Figure 6 ) . We show that haploid yeast tolerate more than a 1 , 000-fold increase in mutation rate before exhibiting overt loss of colony-forming capacity ( Figure 5B ) , and a comparable increase in mutation rate is required to cause catastrophic errors in E . coli [62] , suggesting that prokaryotes and haploid eukaryotes share similar degrees of robustness toward DNA polymerase errors . In comparison , diploid yeast and mouse cells retain replication capacity at mutation rates 10 , 000-times higher than wild-type levels ( Figure 6; [25] , [40] , [66] ) . Thus , diploidy extends the threshold of error-induced cell death by five- to tenfold . These data suggest that cells can survive and persist during periods of high mutational loads . The maximal mutation rate will likely vary , depending on environmental conditions [67] , [68] , genetic redundancy [63] , [69] , [70] , the plasticity of genetic interactions [71] , [72] , and the ability of cells to buffer deleterious changes in essential proteins [73] . We observed that escape mutants readily emerge when moderate mutators are pushed above the error threshold ( Figure 2 ) . One-third of the escape mutants resulted from second-site changes in Pol δ that suppress the proofreading-deficient mutator phenotype . Recent structural studies of S . cerevisiae Pol δ [74] provide insight into potential mechanisms of mutator suppression by these intragenic eex alleles ( Figure 4 , Figures S5 and S6 ) . Many eex mutations alter amino acids around the polymerase active site that are predicted to influence dNTP binding or catalysis ( Figure 4B ) . Effects may be mediated by direct interactions of mutated residues with the metal•dNTP substrate or via packing interactions that indirectly affect the substrate binding pocket . Other eex mutations map to a stretch of amino acids that bind the template near the active site and buttress the fingers domain , which contains residues that contour the template•dNTP base pair ( Figure S5 ) . Amino-acid changes affecting active-site geometry , positioning of the template nucleotide , or stability of the catalytic conformation may act as antimutators by increasing selectivity for correct dNTPs or by slowing the rate of catalysis so that mispaired template•primers have more time to dissociate from Pol δ . A model of dissociation and subsequent editing by an alternative enzyme [20] , [75] may best explain eex mutations that change amino acids along the DNA binding track ( Figure 4C ) . Similarly , eex mutations in the exonuclease domain may impart structural changes that promote Pol δ dissociation during failed proofreading attempts ( Figure S6 ) . Intriguingly , two eex amino-acid substitutions ( E642K and D643N ) are located on the solvent-exposed surface of Pol δ ( asterisk in Figure 4A ) , suggesting that changes in protein-protein interactions influence mutagenesis . Proteins encoded by eex loci extragenic to POL3 ( Table S1 ) are candidate interacting partners . Several alternative enzymes may function to edit Pol δ errors in the eex mutants . One candidate is proofreading by Pol ε . Yeast with deficiencies in both Pol δ and Pol ε proofreading exhibit a synergistic increase in mutation rate , suggesting one or both polymerases may proofread for the other [46] . Other candidates include the 3′→5′ exonuclease activities of MRE11 [76] and Apn2 [77] , or endonucleases such as Rad1/Rad10 or Mus81/Mms4 that cleave 3′ flap structures during replication fork restart [78]–[82] . An important consideration is that such alternative editing pathways may be redundant , with multiple activities masking the contributions of any one nuclease . The locations of several eex substitutions in Pol δ resemble those of antimutators previously identified in bacteriophage T4 polymerase [4] , [83]–[86] and in herpes simplex virus polymerase [87]–[89] , two B-family DNA polymerases similar to Pol δ ( Figure 3 ) . Genetic screens have also identified E . coli DNA polymerase I and III antimutator variants , and similar to our findings , these E . coli antimutators result from diverse amino-acid substitutions throughout the polymerase structures [19] , [20] , [90]–[92] . Some amino-acid substitutions in T4 pol are thought to increase polymerase fidelity by promoting ‘hyper-editing’ of the primer terminus by the integral proofreading exonuclease ( reviewed in [4] ) . However , the eex mutations we describe mediate their antimutator effects without the aid of an active exonuclease domain , similar to previously isolated E . coli antimutators [19] , [20] , [90] . Taken together , this structural analysis suggests two general antimutator mechanisms for Pol δ eex mutations: 1 ) increased dNTP discrimination , thereby making Pol δ more accurate , and 2 ) increased dissociation from mispaired primer-templates , thereby allowing other enzymes to proofread Pol δ errors . eex mutations could also decrease errors at Okazaki fragment junctions by suppressing the strand-displacement activity of proofreading-deficient Pol δ [52] , [93]–[95] . Our study took advantage of synthetically lethal interactions between Pol δ proofreading and MMR alleles to select for antimutators . Several lines of evidence indicate that mutator suppressors also arise under non-lethal conditions and are not restricted to the Pol δ proofreading – MMR pathway . Morrison and Sugino observed mutator suppression in a yeast clone defective for Pol ε proofreading and MMR [46] , and an engineered second-site mutation in Pol ε suppresses the mutator effect of Pol ε proofreading deficiency [96] . In E . coli , suppressors of diverse mutator pathways ( MMR , proofreading and DNA damage repair ) emerge spontaneously in strains that are well below the error threshold [14] , [18]–[20] . In our studies , large-colony variants of slow-growing mutators were frequently observed ( see , for example , Figure 5A ) , and in the one variant we pursued , we found the A894G suppressor mutation . Collectively , these studies show that many defects in DNA replication fidelity can be genetically suppressed and suggest that both moderate and strong mutators are intrinsically unstable . The facile emergence of mutator suppressors that we observed in yeast suggests that similar pathways of suppression exist in multicellular eukaryotes . This has implications for the role of mutator phenotypes in cancer [22] , [97] . During neoplastic transformation , mutator alleles that promote the formation of tumor cells are likely to incur a fitness cost due to an increase in mutational load . To offset this cost , suppressor alleles that reduce the mutation rate may emerge during the later stages of oncogenesis after genetic barriers to immortalization and metastasis have been overcome . Although recent findings suggest that a mutator phenotype persists in at least some types of human tumors [23] , our results raise the prospect that mutator phenotypes may be transient during tumor progression due to genetic suppression . An analysis of mutation rate dynamics in cancer is warranted .
Yeast were grown at 30°C using YPD , synthetic complete ( SC ) media or SC drop-out media deficient in specific amino acids as needed to select for prototrophy [98] . Pre-formulated nutrient supplements for SC and SC lacking uracil and leucine were purchased from Bufferad . All other drop-out supplements were made as described [98] . URA3-deficient cells were selected on SC medium containing 1 mg/ml 5-fluroorotic-acid ( FOA; Zymo Research ) and an additional 50 mg/L uracil [43] . TRP1-deficient strains were selected on FAA selection media containing 0 . 5 mg/ml 5-fluroanthranillic acid ( FAA ) made as described [99] . Canavanine-resistant mutants were scored on SC plates lacking arginine that contained 60 µg/ml of canavanine . Reagents were obtained from Sigma-Aldrich or Fisher Scientific unless otherwise indicated . Plasmid shuffling with pGL310-containing strains was carried out essentially as described [29] , [43] ( Figure S1 ) . Cells transformed with YCplac111pol3 plasmids , YCplac111POL3 ( positive control ) , or YCplac111 ( negative control ) were plated on SC lacking uracil and leucine . Cells transformed with pRS414pol3-01 , pRS414POL3 ( positive control ) , or pRS414 ( negative control ) were plated on SC lacking uracil and tryptophan . After three days at 30°C , individual colonies were picked and resuspended in sterile H2O , and serial dilutions containing approximately 105 , 104 , 103 , or 102 cells were plated onto SC or SC FOA to select for clones that spontaneously lost the URA3 plasmid pGL310 . A similar approach was used for shuffling in strains carrying the TRP1 plasmid pRS414POL3; BP4001 transformants containing both pRS414POL3 and YCplac111-based plasmids were selected on SC lacking tryptophan and leucine and then plated onto SC FAA to select for clones that spontaneously lost the TRP1 plasmid . For the systematic isolation of spontaneous pol3-01 , eex mutant alleles ( Figure 2C ) , a pol3-01 plasmid was transformed into pol3Δ msh6Δ + pGL310 yeast , and 1–5×106 cells from independent transformants were plated separately on SC FOA . FOA-resistant clones were isolated in the MP4 strain carrying YCplac111pol3-01 or in BP1506 carrying YCplac111pol3-01 or pRS414pol3-01 . Bona fide eex mutants were distinguished from FOA-resistant clones that result from ura3 mutation or pol3-01→POL3 gene conversion by using a genotyping assay described in Text S1 . pol3-01 plasmids from individual eex mutants were recovered and reshuffled into naïve pol3Δ msh6Δ + pGL310 cells to identify suppressors intragenic to pol3-01 . Plasmids that conferred consistent survival upon reshuffling were purified , and the pol3 genes were sequenced ( primer sequences available on request ) . Intragenic eex alleles thus identified were individually re-engineered into fresh YCplac111POL3 and YCplac111pol3-01 vectors and re-transformed into MP4 or BP1506 stock strains as a final confirmation of the ability of each allele to confer the eex phenotype . The re-engineered mutants were used to assess the effects of eex alleles on mutation rates and plating efficiencies . For the scanning mutagenesis screen ( Figure S2 ) , sequence-verified YCplac111pol3 plasmids were shuffled into YP6 or MP4 cells immediately prior to each experiment . Twelve to thirteen independent FOA-resistant colonies of each genotype were streaked onto SC plates in ∼1-cm patches , grown two days at 30°C , and then replica-plated to canavanine plates to qualitatively assess mutant frequencies based on the number of canavanine-resistant colonies [47] ( Figure S2A ) . To measure mutation rates at the CAN1 locus , freshly streaked YP6 or MP4 strains were transformed with YCplac111POL3 or YCplac111pol3 plasmids , and multiple independent transformants were shuffled on SC FOA plates to obtain well-isolated single colonies . For each genotype , seven to eleven independent colonies , 1–2 mm in diameter , were excised as an agar plug , resuspended in 1 ml of dH2O , and sonicated briefly . To estimate the number of cell divisions ( Nt ) during colony formation , serial dilutions were plated on SC media , and the number of colony-forming units was counted after two days at 30°C . To determine the number of mutants for wild-type and weak mutator strains , all of the remaining cells were plated on canavanine plates; for stronger mutators , the cell suspension was diluted 1∶10 to 1∶200 in dH20 prior to plating . The numbers of canavanine-resistant colonies on each plate were scored after three to four days at 30°C . To measure URA3 mutation rates , BP4001 was transformed with YCplac111POL3 , YCplac111pol3-01 , or their respective eex mutant derivatives . Four FAA-resistant colonies from independent transformants with each plasmid were inoculated into separate 100-µl SC overnight cultures . The following morning the cultures were diluted to 1000 cells/ml and , for each of the four isolates , 12 parallel 100-µl cultures ( 100 cells/culture ) were set up in 96-well microtiter plates . The plates were sealed with adhesive PCR plate sealers ( Abgene , AB-0558 ) to minimize evaporation [108] . After two days of growth at 30°C , the cells were re-suspended by vigorous vortexing , and nine of the replicate cultures were spot-plated in 200-µl volumes on SC FOA plates . To estimate the total number of cell divisions , the remaining three replica cultures from each isolate were combined , diluted , and plated on SC plates . Colony numbers were scored after 3–4 days . We confirmed that spot plating accurately determines the number of FOA-resistant colonies for the strongest mutator by dividing test cell suspensions in half and comparing colony counts in a 100-µl spot with 100 µl of the same suspension spread over an entire SC FOA plate . Mutation rates were determined from the number of mutant colonies in each replica by calculating an estimate for m by maximum likelihood [109] using newtonLD in Salvador 2 . 1 [110] with Mathematica 6 . 0 ( Wolfram Research ) and dividing by the number of cell divisions inferred from colony forming units . Where values for Nt from independent experiments differed by less than 2-fold , the data sets were combined for the mutation rate calculations [109] . In some instances , Nt values from independent experiments differed by more than 2-fold . In most cases , the independently-derived mutation rates were similar and a single value was reported ( noted in Table 1 ) . Confidence intervals were calculated in Salvador 2 . 1 using LRIntervalLD , which relies on likelihood ratios [110] . From these mutation rates , the efficiency of Msh6-dependent MMR ( em6 ) , expressed as the percentage of errors corrected , was calculated using equation 1: ( 1 ) where Mr is the relative mutation rate of the strain indicated in the subscript . The efficiency of Pol δ proofreading ( eδexo ) , expressed as a percentage of errors corrected , was calculated similarly from equation 2: ( 2 ) For each strain , we isolated up to 48 canavanine-resistant mutants from 48 independent shuffling experiments . Cells were treated with Zymolyase ( ICN Biomedicals; 50 u/ml in 10 mM Tris•HCl/0 . 1 mM EDTA , pH7 . 5 at 37°C for 30 min then 95°C for 10 min ) , and the can1 coding sequence was PCR-amplified in 50-µl reactions with Phusion polymerase ( NEB ) using primers can1F1N ( 5′-GGTTAAGATAAGTAGATAAGAGAATGATACG-3′ ) and can1S1 ( 5′-GCGTGGAAATGTGATCAAAGG-3′ ) with the following PCR conditions: 98°C , 1 min . ; 35× ( 98°C , 10 sec . ; 45°C , 30 sec . ; 72°C , 90 sec . ) ; 72°C , 1 min . The samples were treated with 5 units each of Antarctic phosphatase and Exo1 ( New England Biolabs ) to degrade excess primer and dNTPs , heated at 80°C for 20 min to inactivate the enzymes , and then sequenced with primers can1S1 , can1S2 ( 5′-CCAAAGCGCCAAATGCAGCAG-3′ ) , can1S3 ( 5′-TCCAATAACGGAATCCAACTG-3′ ) and can1S4 ( 5′-GGGCAATCATACCAATATGTC-3′ ) . Mutation spectra were tabulated and compared using iMARS [111] . Phenotypic mutation rates were converted to per-base-pair rates using the approach of Drake [112]–[114] according to equations 3 – 5: ( 3 ) ( 4 ) ( 5 ) C and C′ ´ ( equations 3 and 4 ) are correction factors to adjust for undetected ( phenotypically silent ) base-substitution mutations in a reporter gene . BCT = the number of chain-terminating base substitutions ( 3 possible codons ) , B = the number of all base substitutions ( 64 possible codons ) , and I = the number of insertions+deletions ( indels ) in representative mutation spectra from M mutants sequenced . The mutation rate per base pair ( μb ) is calculated using equation 5 from the experimentally determined phenotypic mutation rate ( μT ) multiplied by the correction factor C′ ´ and divided by the number of base pairs in the mutation-reporter target sequence ( T ) . The effective target size ( τ ) is estimated by T / C ´′ . In our collection of 484 Canr mutants from proofreading- and MMR-deficient yeast ( Figure S3 and Table S2 ) , there were 442 base substitutions ( 101 chain-terminating + 341 missense ) and 42 indels ( including infrequent complex mutations ) in CAN1 ( T = 1773 ) . Thus , C = 4 . 87 and τ = 391 base pairs . These values from mutator yeast strains are similar to those previously determined by others scoring spontaneous mutation in wild-type yeast ( C = 4 . 73 , [113]; τ = 236 , [108] ) . The per-base-pair rates for haploid yeast plotted in Figure 6 were calculated from our Canr μT values ( Table 1 and Table 2 ) with C = 4 . 87 , C′ ´ = 4 . 53 and T = 1773 . For diploid pol3-01/pol3-01 pms1/pms1 yeast , we used the FOAr mutation rate ( μT ) of 3 . 5×10-4 reported by Morrison et . al . [40]; C = 8 . 18 ( determined from the data of Lang and Murray [108] ) , C′ ´ = 6 . 79 and T = 804 base pairs for the URA3 target gene . Thus , τ is 118 base pairs , and the per-base-pair mutation rate of pol3-01/pol3-01 pms1/pms1 diploids at the URA3 locus is [ ( 3 . 5×10−4 ) ×6 . 79 / 804] = 3 . 0×10−6 . In Figure 6 we multiply this rate by 1 . 8 to adjust for the lower intrinsic mutation rate of URA3 compared to CAN1 ( Table 1 and Table 2 and [108] ) . For mouse cells , per-base-pair mutation rates were calculated from ouabain-resistance ( Ouar ) rates determined in our laboratory using spontaneously immortalized mouse embryo fibroblasts ( [25] and unpublished data ) . The effective target size ( τ ) is estimated as follows . Base substitution mutations in any one of sixteen codons in the Na , K-ATPase α1 gene ( Atp1a1 ) are known to confer genetically dominant resistance to µM concentrations of ouabain in human cells [115] . Mouse cells , however , are naturally resistant to µM concentrations of ouabain due to differences at 2 of these 16 codons ( Q111R and N122D; [116] , [117] . Our fluctuation assays were conducted with 2 mM ouabain [25] , conditions expected to only detect mutations that confer exceptionally high ouabain resistance . We estimate the target size to be ∼5 base pairs per allele , corresponding to two Atp1a1 codons ( D121 and T797 ) known to effect >50-fold ouabain-resistance when mutated [115] , [118] . Mouse fibroblast cell lines are typically tetraploid [119] . Therefore τ = 5 base pairs per allele×4 alleles = 20 base pairs . Pold1+/e Mlh1Δ/Δ cells , which are heterozygous defective for Pol δ proofreading and nullizygous for MMR , exhibited a mutation rate of 65×10−7 Ouar mutants per cell division ( 95% confidence interval = 56–75×10−7 ) . This phenotypic rate corresponds to a per-base-pair rate of 65×10−7 / 20 base pairs = 3 . 3×10−7 . Mouse cells that are homozygous deficient for both Pol δ proofreading and MMR ( Pold1e/e Mlh1Δ/Δ ) are viable but divide slowly up to embryonic day E9 . 5 [25] . Based on the relative mutation rates of MMRΔ/Δ diploid yeast with +/- or −/− Pol δ proofreading alleles [40] , we estimate the per-base-pair rate of Pold1e/e Mlh1Δ/Δ mouse cells to be 5×10−6 . | Organisms strike a balance between genetic continuity and change . Most cells are well adapted to their niches and therefore invest heavily in mechanisms that maintain accurate DNA replication . When cell populations are confronted with changing environmental conditions , “mutator” clones with high mutation rates emerge and readily adapt to the new conditions by rapidly acquiring beneficial mutations . However , deleterious mutations also accumulate , raising the question: what level of mutational burden can cell populations sustain before collapsing ? Here we experimentally determine the maximal mutation rate in haploid yeast . We observe that yeast can withstand a 1 , 000-fold increase in mutation rate without losing colony forming capacity . Yet no strains survive a 10 , 000-fold increase in mutation rate . Escape mutants with an “anti-mutator” phenotype frequently emerge from cell populations undergoing this error-induced extinction . The diversity of antimutator changes suggests that strong mutator phenotypes in nature may be inherently transient , ensuring that rapid adaptation is followed by genetic attenuation which preserves the beneficial , adaptive mutations . These observations are relevant to microbial populations during infection as well as the somatic evolution of cancer cells . | [
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] | 2011 | Mutator Suppression and Escape from Replication Error–Induced Extinction in Yeast |
There is no currently licensed vaccine for respiratory syncytial virus ( RSV ) despite being the leading cause of lower respiratory tract infections in children . Children previously immunized with a formalin-inactivated RSV ( FI-RSV ) vaccine exhibited enhanced respiratory disease following natural RSV infection . Subsequent studies in animal models have implicated roles for CD4 T cells , eosinophils and non-neutralizing antibodies in mediating enhanced respiratory disease . However , the underlying immunological mechanisms responsible for the enhanced respiratory disease and other disease manifestations associated with FI-RSV vaccine-enhanced disease remain unclear . We demonstrate for the first time that while CD4 T cells mediate all aspects of vaccine-enhanced disease , distinct CD4 T cell subsets orchestrate discrete and specific disease parameters . A Th2-biased immune response , but not eosinophils specifically , was required for airway hyperreactivity and mucus hypersecretion . In contrast , the Th1-associated cytokine TNF-α was necessary to mediate airway obstruction and weight loss . Our data demonstrate that individual disease manifestations associated with FI-RSV vaccine-enhanced disease are mediated by distinct subsets of CD4 T cells .
Respiratory syncytial virus ( RSV ) is the leading cause of hospitalization in infants and young children [1–3] . There is currently no licensed RSV vaccine available . An initial trial in the late 1960's with a formalin-inactivated RSV ( FI-RSV ) vaccine ended in failure . FI-RSV vaccination not only failed to induce sterilizing immunity against RSV infection , but also resulted in an increased rate of hospitalization and disease severity after a natural RSV infection in the majority of the volunteers including two cases of fatal disease [4–8] . A study examining the two children that died revealed a significant increase in the number of eosinophils present in the lung parenchyma [4] . Mirroring the results of the FI-RSV vaccine trial , FI-RSV immunization also induces a Th2-biased immune response resulting in pulmonary eosinophilia following RSV challenge in multiple animal models [9–12] . Since the presence of an elevated number of eosinophils in both the lung and peripheral blood was highlighted in the initial vaccine trial reports , the development of pulmonary eosinophilia has become a hallmark of the enhanced respiratory disease ( ERD ) associated with FI-RSV vaccine-enhanced disease ( VED ) [4–7] . However , re-examination of the human autopsy specimens from the initial FI-RSV vaccine trials revealed only 1–2% of the total cellular infiltrate in the airways were eosinophils [12] . This observation , in conjunction with similar findings in lung sections from FI-RSV-immunized cotton rats , an alternative model of FI-RSV ERD , has raised questions concerning the role eosinophils play during FI-RSV VED [12] . Therefore , it remains unclear if eosinophils directly contribute to the severe immunopathology associated with FI-RSV ERD . Multiple disease manifestations are associated with FI-RSV VED including weight loss , pulmonary inflammation , mucus hypersecretion and airway obstruction . In addition to eosinophils , previous studies have also implicated a pathogenic role for antibodies induced following FI-RSV immunization in mediating VED following a RSV challenge [13 , 14] . FI-RSV-immunized mice deficient in the complement component C3 exhibit a significant amelioration of pulmonary histopathology after RSV challenge , implicating a role for immune complexes in VED [13] . In addition , non-neutralizing antibody responses correlate with increases in lung histopathology and airway hyperreactivity associated with FI-RSV VED [14] . Supplementation of TLR agonists during FI-RSV-immunization improves affinity maturation of B cell responses and prevents ERD following RSV challenge [14] . However , it remains unclear which immunological factors directly contribute to critical disease parameters associated with FI-RSV VED . The lack of a detailed mechanistic understanding of the causes of FI-RSV VED has made it difficult to appropriately assess the safety of new RSV vaccine candidates . In order to address this critical knowledge gap , we sought to determine the specific immunological factors responsible for mediating the individual disease parameters most associated with FI-RSV VED . In contrast to the prevailing notion , we demonstrate that eosinophils are not required to mediate any of the characteristic disease manifestations associated with FI-RSV VED . In vivo depletion of CD4 T cells prior to RSV challenge led to significant reductions in all disease parameters assessed . Our results show that a Th2-biased immune response is necessary to mediate airway hyperreactivity and mucus hypersecretion disease parameters . In contrast , the Th1-associated cytokine TNF-α was found to be critical for the induction of airway obstruction and weight loss associated with FI-RSV VED . Our studies demonstrate for the first time that distinct subsets of CD4 T cells orchestrate individual disease parameters associated with FI-RSV VED .
ERD was an important clinical manifestation of FI-RSV VED [5–7] . Whole body plethysmography has been previously utilized for the assessment of baseline respiratory patterns that correlate with pulmonary function following viral infections [15 , 16] . Therefore , we used unrestrained whole body plethysmography to evaluate pulmonary function daily following RSV challenge [17 , 18] of FI-RSV-immunized BALB/c mice . FI-RSV-primed mice exhibited increased airway obstruction , measured as enhanced pause ( Penh , Fig . 1A ) , during the first five days following RSV challenge when compared to mock-primed controls . Mice vaccinated with FI-RSV also exhibited significantly ( p<0 . 05 ) increased weight loss ( Fig . 1B ) compared to the mock-immunized control group between days 1–6 following RSV challenge . The numbers of eosinophils , macrophages and lymphocytes were significantly ( p<0 . 01 ) increased in the lungs of FI-RSV-immunized mice on days 4 and 6 following RSV challenge ( Fig . 1C ) . Consistent with the induction of pulmonary eosinophilia , FI-RSV-immunized animals exhibit significantly ( p<0 . 001 ) increased protein amounts of the Th2-associated cytokines IL-4 and IL-13 as compared to mock-immunized controls at days 3 and 4 following RSV infection ( Fig . 1D ) . In addition , there was a significant ( p<0 . 001 ) increase in both lung IFN-γ and TNF-α protein amounts indicating that the CD4 T cell response consisted of a mixture of Th1 and Th2 cells . A significant difference in IL-17A production was not detected by either ELISA of lung homogenates or following CD4 T cell restimulation and therefore only the Th1- and Th2-associated immune responses were evaluated for the remainder of the study ( S1 Fig . ) . The development of pulmonary eosinophilia following RSV challenge of FI-RSV-immunized hosts has become a defining characteristic of RSV VED [4–7] . The increased airway obstruction and weight loss coincide with time points when there is a significant increase in the number of eosinophils in the lung ( Fig . 1A-C ) . To determine the contributions of eosinophils to respiratory disease and weight loss associated with FI-RSV VED , we utilized eosinophil-deficient dblGATA-1 mice [19] . Quantification of eosinophil numbers revealed virtually undetectable numbers of eosinophils in the lung parenchyma ( Fig . 1E ) of FI-RSV-primed dblGATA-1 mice on day 4 and 6 following RSV challenge . Assessment of viral titers on days 4 and 6 following RSV challenge also showed no difference in lung RSV titers between FI-RSV-immunized WT and eosinophil-deficient mice ( Fig . 1F ) . Airway obstruction ( Fig . 2A ) was not significantly altered between eosinophil-deficient and wild-type ( WT ) FI-RSV-immunized mice following RSV challenge . Weight loss through day 6 following RSV challenge was also largely unaffected in FI-RSV-vaccinated dblGATA-1 mice . On day 7 post-infection , weight recovery was slightly delayed in FI-RSV-immunized eosinophil-deficient mice . We also compared the histopathology between vaccinated WT and eosinophil-deficient mice on day 4 following RSV challenge . Following RSV infection of mock-immunized mice , an increase in leukocytic aggregates around airways and mucus hypersecretion was noted as compared to naive mice ( Fig . 2B and C ) . However , neither histopathology nor mucus levels were significantly altered in the absence of eosinophils in FI-RSV-immunized mice . To further assess lower airway function , we evaluated airway hyperresponsiveness ( AHR ) during mechanical ventilation following a methacholine challenge of vaccinated mice . Airway resistance was significantly ( p<0 . 05 ) increased and tissue compliance was significantly ( p<0 . 05 ) reduced in FI-RSV-immunized mice following RSV challenge at day 4 post-infection ( Fig . 2D ) . However , airway resistance and compliance were similar following RSV challenge of FI-RSV-vaccinated WT and eosinophil-deficient mice ( Fig . 2D ) . Taken together , these data demonstrate that , in contrast to the current prevailing notion , eosinophils are not required to mediate any of the characteristic disease parameters that are associated with FI-RSV VED . Previous work has shown that antibody-mediated depletion of CD4 T cells in FI-RSV vaccinated mice prior to RSV challenge ameliorates pulmonary histopathology suggesting a vital role of CD4 T cells in mediating pulmonary inflammation following RSV challenge [20] . We observed a significant ( p<0 . 001 ) increase in the number of CD4 T cells in the lung on days 4 and 6 post-RSV challenge ( Fig . 3A ) of FI-RSV-immunized mice compared to the mock-immunized control group . Importantly , the number of CD4 T cells in the lung was not significantly altered in the absence of eosinophils ( Fig . 3A ) . Consistent with the notion that inactivated vaccines are poor at eliciting CD8 T cell responses , we have previously reported that FI-RSV immunization fails to induce an RSV-specific CD8 T cell memory response [21 , 22] . In agreement with our previous results , we observed no significant increase in the CD8 T cell response of FI-RSV-vaccinated mice ( Fig . 3B ) . In contrast , we observed a robust secondary CD4 T cell response . We next evaluated subsets of CD4 T cells by intracellular cytokine staining following PMA and ionomycin restimulation ( S2 Fig . ) . By day 6 following RSV challenge there was a significant ( p<0 . 01 ) increase in the number of CD4 T cells that produced either IFN-γ or IL-13 following restimulation in FI-RSV-immunized mice as compared to mock control groups regardless of whether or not eosinophils were present ( Fig . 3C and D ) . Our results indicate that independent of the presence of eosinophils , FI-RSV immunization primes both a Th1 and Th2 memory CD4 T cell response that may promote disease associated with FI-RSV VED . We next questioned if Th2-associated responses were responsible for mediating all disease parameters associated with FI-RSV VED . The transcription factor STAT6 is crucial for the differentiation of naive CD4 T cells to Th2 cells and ultimately the induction of Th2-associated immune responses [23] . Therefore , we utilized STAT6-deficient mice to determine if Th2-biased immune responses were necessary to mediate all disease symptoms associated with FI-RSV VED . FI-RSV-immunized mice on day 3 post-infection exhibited a significant ( p<0 . 01 ) reduction in both IL-4 and IL-13 protein amounts in the lung ( Fig . 4A ) . STAT6 deficiency did not impact lung viral titers in either mock- or FI-RSV-immunized mice ( Fig . 4B ) . The total number of CD4 T cells remained similar between WT and STAT6-deficient FI-RSV-immunized mice ( Fig . 4C ) . However , on day 7 following RSV challenge FI-RSV-immunized STAT6-deficient mice exhibited a significant ( p<0 . 001 ) reduction in the number of eosinophils in the lung ( Fig . 4D ) . Moreover , the number of IL-5- and IL-13-producing CD4 T cells was significantly ( p<0 . 01 ) reduced in FI-RSV-immunized STAT6-deficient mice at day 7 post-infection ( Figs . 4E and S3 ) . However , the number of IFN-γ-producing CD4 T cells remained similar in the lungs between FI-RSV-immunized STAT6-deficient and WT mice ( Fig . 4E ) . These data demonstrate that the overall Th2-associated immune response in STAT6-deficient mice is severely diminished . We next sought to determine if the impaired Th2-associated immune response in STAT6-deficient mice would ameliorate disease associated with FI-RSV VED . The absence of STAT6-signaling did not significantly alter either the airway obstruction or weight loss ( Fig . 5A ) in FI-RSV-immunized mice following RSV challenge . In contrast , assessment of histopathology revealed multiple changes to the lung environment ( Figs . 5B and S4 ) . Specifically , FI-RSV-immunized STAT6-deficient mice exhibited significantly ( p<0 . 001 ) reduced perivascular leukocytic aggregates as compared to the WT group ( Fig . 5C ) . Mucus hypersecretion was also significantly ( p<0 . 001 ) reduced in STAT6-deficient FI-RSV-immunized mice as compared to the WT control group . Assessment of AHR also revealed a significant ( p<0 . 001 ) reduction in airway resistance and a significant ( p<0 . 001 ) improvement in compliance ( Fig . 5D ) of FI-RSV-immunized STAT6-deficient mice as compared to WT control group . Overall , our results demonstrate that the Th2-associated immune response does not substantially contribute to either airway obstruction or weight loss associated with FI-RSV VED . However , the Th2-associated immune response is required to induce lower airway pathology , mucus hypersecretion and AHR . We next investigated other immunological factors related to the CD4 T cell response that could mediate either the severe weight loss or airway obstruction associated with FI-RSV VED . Since there is a significant ( p<0 . 01 ) increase the number of IFN-γ- and TNF-α-producing CD4 T cells in FI-RSV-immunized mice following RSV infection ( Figs . 3C and D , and 4E ) , we assessed the roles of IFN-γ and TNF-α during FI-RSV VED . Despite a significant increase in IFN-γ protein amounts in the lung at day 3 post-infection of FI-RSV-vaccinated mice ( Fig . 1D ) , FI-RSV-immunized IFN-γ-deficient mice displayed no alteration in either airway obstruction or weight loss ( Fig . 6A ) as compared to WT controls . In contrast , neutralization of TNF-α in FI-RSV-immunized mice significantly ( p<0 . 05 ) reduced both the increase in airway obstruction and weight loss ( Fig . 6B ) following RSV infection . To determine if TNF-α also contributed to both AHR and mucus hypersecretion , or if distinct immunological mechanisms mediate separate disease manifestations , we evaluated both histology and airway hyperreactivity . The scores for degree of perivascular leukocytic aggregates , mucus , and total histopathology were significantly ( p<0 . 01 , 0 . 001 , and 0 . 001 respectively ) increased in FI-RSV-immunized mice following TNF-α neutralization as compared to FI-mock-immunized controls ( Fig . 7A and B ) . However , histopathology was similar in FI-RSV-immunized mice following either IgG or anti-TNF-α treatment ( Fig . 7A and B ) . Furthermore , neutralization of TNF-α did not significantly alter either lower airway resistance or compliance following methacholine administration ( Fig . 7C ) . The similar histopathology and AHR in FI-RSV-immunized mice following TNF-α neutralization was not due to a difference in viral clearance as titers remained similar at day 4 p . i . ( Fig . 7D ) . TNF-α neutralization led to a significant ( p<0 . 001 ) decrease in the number of eosinophils ( Fig . 7E ) in the lung of FI-RSV-immunized mice . In addition , TNF-α neutralization caused a significant ( p<0 . 001 ) increase in CD4 T cell numbers ( Fig . 7E ) that correlated to an increase in the number of IFN-γ-producing , but not IL-13-producing CD4 T cells ( Fig . 7F ) . Moreover , FI-RSV-immunized mice deficient in STAT6 signaling exhibit a similar amount of TNF-α protein in the lungs following RSV challenge as compared to WT controls ( S5 Fig . ) . Overall , these data suggest that distinct immunological mechanisms affect different disease manifestations . Specifically , the Th2-biased immune response mediates AHR and mucus hypersecretion while TNF-α contributes to both airway obstruction and weight loss . Previous work has indicated that the induction of non-neutralizing antibody responses and immune complex deposition may contribute to the development of FI-RSV ERD [13 , 14] . In contrast , our data indicate that both Th1- and Th2-associated cytokines play a critical role in mediating individual disease parameters associated with FI-RSV VED . CD4 T cell responses are significantly ( p<0 . 01 ) increased in FI-RSV-immunized mice following RSV challenge and consist of a mixture of Th1 and Th2 cells ( Fig . 3C and D ) . In addition , it has been shown that CD4 T cells are required for the histopathology associated with FI-RSV VED [20] . Therefore , we sought to evaluate whether or not CD4 T cells were solely responsible in mediating pulmonary dysfunction and weight loss associated with FI-RSV VED . Depletion of CD4 T cells prior to RSV challenge , led to a significant ( p<0 . 05 ) amelioration of both airway obstruction and weight loss ( Fig . 8A ) in FI-RSV-immunized mice . Furthermore , the absence of FI-RSV epitope-specific CD4 T cells significantly ( p<0 . 001 ) reduced the changes in airway resistance and compliance ( Fig . 8B ) observed in FI-RSV-immunized mice after RSV challenge . Importantly , the amelioration of disease in FI-RSV-immunized mice occurred despite similar antibody levels between IgG- and α-CD4-treated groups for total IgG ( Fig . 9A ) , IgG1 ( Fig . 9B ) , and IgG2a ( Fig . 9C ) . Our results show that CD4 T cells are necessary to orchestrate an immune response that mediates all facets of disease associated with FI-RSV VED .
The morbidity and mortality associated with the failed FI-RSV vaccine trial has significantly hampered the development of an RSV vaccine . Here we dissected the underlying immunological mechanisms that mediate individual disease parameters associated with FI-RSV VED . In contrast to the widely held belief that eosinophils play a critical role in the pathogenesis of FI-RSV VED [4] , our results indicate that eosinophils are not required to mediate the crucial disease parameters associated with FI-RSV VED . This was an unexpected result given the more defined pathogenic role of eosinophils in other diseases such as hypereosinophilic syndrome and asthma [24–27] . Upon re-examination of the human autopsy specimens from the initial FI-RSV vaccine trials , Prince et al . found that only 1–2% of cells in the bronchial lumen and peribronchiolar region were eosinophils [12] . The vast majority of inflammatory cells in the bronchial epithelium were neutrophils and lymphocytes . Similar findings were observed in lung sections from FI-RSV-immunized cotton rats , another animal model that exhibits FI-RSV ERD [12] . However , one of the reports from the original FI-RSV vaccine trials also noted elevated numbers of eosinophils in the peripheral blood of 56% of the vaccine recipients indicating that eosinophilia was likely a common feature of the response [5] . Our vaccinated mice also develop pulmonary eosinophilia in greater numbers as compared to an acute infection setting following RSV challenge , but they do not exhibit an increase in neutrophil numbers . The number of neutrophils is significantly increased in the peripheral blood leukocytes of infants with a severe RSV infection [28] . It is unclear if the number of lung neutrophils in FI-RSV-immunized humans and cottons rats would be increased as compared to mock-immunized control groups . Thus , the potential role neutrophils may have played in FI-RSV VED remains unclear . It has also been suggested that eosinophils may play a beneficial role during the innate immune response to aid in RSV clearance as observed in a hypereosinophilic , IL-5 transgenic mouse model [29] . However , we observed no significant alteration in viral titers during FI-RSV VED in eosinophil-deficient mice on either day 4 or 7 post RSV infection . These differences in viral clearance are likely due to the transgenic murine strain used by Phipps et al . in which there is constitutive production of IL-5 in the periphery . Therefore , these transgenic mice would have a supraphysiologic number of eosinophils in the lung prior to viral challenge . Our data indicate that eosinophils are recruited to the lung as a consequence of the increased levels of Th2-associated cytokines and chemotactic agents such as IL-5 , IL-13 , and eotaxin [30 , 31] . It is conceivable that eosinophils may play a beneficial role by contributing to airway remodeling of the lung tissue with minimal impact on respiratory function [32 , 33] . However , based on our results it is clear that other cell types are primarily responsible for mediating FI-RSV VED . Our data demonstrate that a Th2-biased immune response is necessary to induce AHR and mucus hypersecretion . Th2 cells can mediate pulmonary inflammation during allergic responses through several methods including induction of mucus hypersecretion , indirectly promoting airway smooth muscle contraction , and mediating chemotaxis of other inflammatory cells such as eosinophils and mast cells [34–36] . Th2-biased immune responses play a critical role in orchestrating chronic disease manifestations associated with asthma including both pulmonary inflammation and bronchoconstriction [37] . However , while a Th2-biased immune response was essential to promote AHR and histopathology , it did not have a significant impact on airway obstruction and weight loss . The increased airway obstruction associated with FI-RSV immunization in the absence of STAT6-signaling suggests that baseline pulmonary function remains impaired despite reduced AHR . In contrast , the neutralization of TNF-α led to the significant reduction in airway obstruction and no alteration to AHR . TNF-α plays a critical role in numerous respiratory diseases including asthma , chronic obstructive pulmonary disease , acute lung injury , and acute respiratory distress syndrome primarily through the induction of a proinflammatory environment , but can also directly cause apoptosis of human bronchial epithelium [38 , 39] . The difference in baseline pulmonary function and AHR indicates that these parameters may represent two separate disease manifestations . In agreement , previous studies have shown that baseline measurements in pulmonary function often do not correlate with airway hyperreactivity in asthmatic patients [40–43] . Overall , this indicates that specific disease manifestations are regulated by distinct immunological mechanisms . Multiple cytokines have been documented to promote weight loss during either cancer or chronic infections including IL-1 , IL-6 , IFN-γ and TNF-α [44 , 45] . Due to the increased number of CD4 T cells that produced IFN-γ and TNF-α even in the absence of eosinophils or STAT6-signaling , we assessed the role of these cytokines in FI-RSV-immunized mice . Although IFN-γ protein amounts were significantly increased in FI-RSV-immunized mice , IFN-γ-deficient mice did not exhibit an alteration in either airway obstruction or weight loss suggesting a negligible role for IFN-γ in mediating disease . However , the neutralization of TNF-α led to a significant reduction in both airway obstruction and weight loss in FI-RSV-immunized mice . This is in agreement with previous work showing that prolonged TNF-α production promotes weight loss during an acute RSV infection [46] . As weight loss was not completely abolished in FI-RSV-immunized mice , this suggests that other proinflammatory cytokines are induced following RSV infection [47] , such as IL-1 or IL-6 , that may contribute to the weight loss associated with FI-RSV VED . Our results show that CD4 T cells are necessary to mediate all disease parameters associated with FI-RSV VED including airway obstruction , weight loss , and AHR . The depletion of CD4 T cells led to a significant amelioration of all disease parameters as we hypothesized due to the above defined roles of both the Th1- and Th2-associated immune response in mediating distinct disease manifestations . In agreement with our results , antibody-mediated depletion of CD4 T cells [20] or antibody-mediated neutralization of the Th2-associated cytokine IL-4 in conjunction with neutralization of IL-10 [48] was previously shown to result in decreased histopathology in FI-RSV-immunized mice following RSV challenge . Taken together , these data indicate that the CD4 T cell response is critical to mediate the increased disease severity associated with RSV VED . Our data illustrates that individual disease parameters are mediated by distinct subsets of CD4 T cells . The demonstration that Th2-associated cytokines as well as TNF-α production by Th1 cells drives the induction of FI-RSV VED should serve as areas of focus for evaluation of new RSV vaccine candidates for disease potentiation . Overall our results highlight the necessity to evaluate future RSV vaccine candidates by the careful examination of several disease parameters . Evaluation of only one or two disease parameters such as eosinophilia , histopathology or AHR , may overlook disease exacerbation in terms of weight loss or airway obstruction that is mediated by a distinct subset of memory CD4 T cells . Such actions could lead to undesirable results in vaccine trials and hamper further RSV vaccine development . Our results should provide a framework to thoroughly assess the safety of future RSV vaccines through the careful evaluation of critical disease parameters most associated with RSV VED .
Female BALB/cAnNCr mice between 6–8 wk old were purchased from the National Cancer Institute ( Frederick , MD ) . Eosinophil-deficient dblGATA-1 ( C . Cg-Gata1tm6Sho/J ) [19] and STAT6-deficient mice ( C . 129S2-Stat6tm1Gru/J ) on the BALB/c background were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . IFN-γ-deficient mice ( C . 129S7 ( B6 ) -Ifngtm1Ts/J ) on the BALB/c background was obtained from John T . Harty ( University of Iowa ) . All experimental procedures utilizing mice were approved by the University of Iowa Animal Care and Use Committee . The experiments were performed under strict accordance to the Office of Laboratory Animal Welfare guidelines and the PHS Policy on Humane Care and Use of Laboratory Animals . The A2 strain of RSV was a gift from Barney S . Graham ( National Institutes of Health , Bethesda , MD ) and was propagated in HEp-2 cells ( ATCC ) . Mice were infected intranasally ( i . n . ) with 1 . 5–1 . 7 x 106 PFU RSV . FI-RSV was prepared from RSV infected Vero cells grown in OptiPROTM SFM media ( Invitrogen ) . Growth of RSV in Vero cells produces virions that express a truncated form of the G protein lacking the C-terminus [49] . However , it has been previously shown that mice immunized with FI-RSV produced from a recombinant RSV lacking the entire G protein grown in Vero cells still exhibit FI-RSV vaccine-enhanced disease following RSV challenge [50] . Virus-infected cells were removed by scraping and sonicated for eight 1-sec pulses and subsequently centrifuged at 10 , 000 rpm for 10 min . Supernatant was inactivated with 10% formalin at 1:400 dilution for 72 hr at 37°C followed by centrifugation at 50 , 000 x g for 1 hr at 4°C . The pellet was resuspended in OptiPRO SFM at 1:25 dilution with 4 mg/mL of Imject Alum adjuvant ( Thermo Fisher Scientific ) . The solution was centrifuged at 1000 x g for 30 min at 4°C and pellet was resuspended at 1:4 dilution in OptiPROTM SFM . The FI-RSV prep was sonicated in water bath sonicator for 15 sec on ice and stored in amber glass vials at 4°C . A mock preparation was also created using the same protocol from a lysate of Vero cells mock infected with PBS . Mice were vaccinated intramuscularly ( i . m . ) with 100 μl of a 1/200 dilution of FI-mock or FI-RSV in the lower right-hind flank . Mice were challenged with RSV 21 days following immunization . For CD4 T cell depletions , mice were treated with 250 μg of α-CD4 ( clone GK1 . 5 ) antibody every 4 days i . p . starting at day -2 prior to RSV challenge . CD4 T cell frequencies were assessed in the peripheral blood leukocytes on day 0 prior to infection ( anti-CD4 clone RM4–4 ) and found to be >99% depleted . For TNF-α neutralization , mice were administered 200 μg of anti-TNF-α ( clone MP6-XT22 ) antibody both i . n . and i . p . at day -1 prior to infection . Every other day thereafter , mice were treated with an additional 200 μg dose of anti-TNF-α antibody i . p . As controls , mice were given a matching dose of control IgG antibody at similar timepoints and route of administration . The lung function of mice was evaluated utilizing two methods of unrestrained whole-body plethysmography and forced oscillations via mechanical ventilation [51] . Enhanced pause ( Penh ) was measured using a whole-body plethysmograph ( Buxco Electronics , Wilmington , NC ) as previously described [52] . Penh was calculated based on pressure and volume changes over 5 min . While Penh is not a surrogate for lower airway resistance , it can be correlated to changes in baseline respiratory patterns [15 , 16 , 53] . Penh can also be used as an indication of airway obstruction and has been validated previously [17 , 18] . To investigate changes in lower airway hyperreactivity , mice were assessed on day 4 post-infection using a flexiVent mechanical ventilator ( Scireq , Plattsburgh , NY ) . Mice were anesthetized with 100 mg/kg dose of pentabarbital and tracheotomized using a blunted 18 gauge needle . Respiratory mechanics were measured using the forced oscillation technique following saline and 25 mg/mL methacholine challenges administered using Aeroneb nebulizer . Methacholine is a bronchoconstrictor agent that induces airway constriction . Therefore , mice that exhibit greater airway reactivity will experience increased changes to parameters of pulmonary function , i . e . airway resistance and compliance , following methacholine challenge . Significant alterations in respiratory mechanics have been observed previously in murine models with RSV infection [54–57] . Airway resistance and compliance were expressed as percentage change over baseline measurements from saline treatment . Lung and bronchoalveolar lavage ( BAL ) were harvested from mice as previously described [52 , 58] . Lung homogenates and BAL cells were surface-stained with mAbs specific to CD11c ( clone N418 ) , Siglec F ( BD Biosciences , clone E50-2440 ) , F4/80 ( clone BM8 ) , MHCII ( clone M5/114 . 15 . 2 ) , Ly6c ( clone HK1 . 4 ) , Ly6g ( clone 1A8 ) , CD90 . 2 ( clone 53-2 . 1 ) , CD4 ( clone GK1 . 5 ) and CD8 ( clone 53-6 . 7 ) for 30 min at 4°C and fixed with FACS lysing solution ( BD Biosciences and eBioscience ) for 10 min at room temperature . For intracellular cytokine staining ( ICS ) , cells were stimulated for 5 hr at 37°C with 50 ng/mL PMA ( Sigma-Aldrich ) and 500 ng/mL ionomycin ( Sigma-Aldrich ) in the presence of 10 μg/mL brefeldin A ( BFA , Sigma Aldrich ) in 10% FCS-supplemented RPMI . Cells were then surface-stained for CD90 . 2 and CD4 , fixed in FACS lysing solution , and stained intracellularly with mAbs specific to IFN-γ ( clone XMG1 . 2 ) , IL-10 ( clone JES5-16E3 ) , IL-17A ( clone TC11-18H10 . 1 ) , IL-5 ( clone TRFK5 ) , and IL-13 ( eBioscience clone eBio13A ) in FACS buffer containing 0 . 5% saponin ( Sigma-Aldrich ) for 30 min at 4°C . The total number of cytokine producing cells was calculated after subtraction of background staining using BFA only controls . All monoclonal antibodies were purchased from BioLegend unless otherwise stated . Stained cells were run on BD FACSCanto or LSRFortessa and analyzed with FlowJo ( Tree Star , Ashland , OR ) software . Mouse serum was collected prior to immunization , 23 days following immunization with CD4 T cell depletion ( 2 days post-depletion ) , and 4 days following RSV challenge ( CD4 depletion on days -2 and 2 ) . Flat-bottom 96-well plates ( Nunc MaxiSorp , Thermo Scientific ) were coated with 1 x 104 PFU/well of RSV overnight at 4°C . Plates were blocked with 5% non-fat dry milk in PBS for 2 hours at 37°C . Supernatants were serially diluted 1:2 starting at 1:64 over 6 total dilutions , and plates were incubated overnight at 4°C . RSV-specific antibody was detected using biotinylated goat anti-mouse antibody specific for IgG , IgG1 , IgG2a , or IgE ( Southern Biotech , Birmingham , AL ) at a dilution of 1:500 for 2 hours at 37°C . Plates were incubated with 1:400 dilution of streptavidin-horse radish peroxidase conjugate ( Sigma Aldrich ) for 30 minutes at room temperature . Plates were developed in 0 . 1 mg/mL 3 , 3' , 5 , 5'-tetramethylbenzidine solution for 10 minutes and reaction was stopped with 2M sulfuric acid . Absorbance values ( 560 nm ) were measured and assessed using Gen5 software ( BioTek , Winooski , VT ) . Lungs were prepared for cytokine analysis as previously described [52 , 59] . Supernatants were analyzed for cytokines levels of IL-4 ( eBioscience ) , IL-13 ( R&D Systems , purified clone 38213 . 11 and biotinylated polyclonal goat anti-mouse IL-13 ) , IL-17A ( R&D Systems DuoSet ELISA Kit ) , and IFN-γ ( eBioscience ) . Whole lungs were harvested on day 4 following RSV challenge and fixed in 10% neutral buffered formalin ( Fischer Scientific ) . Lungs were processed as previously described [30] and stained for H&E for routine evaluation and PAS staining of amylase-treated tissue for mucus . Each sample was assessed for degree of interstitial disease , edema , perivascular aggregates of leukocytes ( PVA ) , mucus , and a total score , a composite average of all disease parameters . Tissues were examined and scored in a manner masked to experiment groups [60] . Histopathologic scoring was similar to that previously described [52] and based on an ordinal scale in which a score “1” represented within a normal or naive range whereas a score of “4” represented extensive or severe processes . Specifically , the scoring definitions are as follows: PVA; 1—normal , within naïve parameters , 2—focal to uncommon numbers of solitary cells with uncommon aggregates , 3—multifocal moderate aggregates , 4—moderate to high cellularity and multifocal , large cellular aggregates that may be expansive into adjacent tissues , mucus; 1—no mucus , 2—epithelial mucinous hyperplasia with none to rare luminal mucus , 3—epithelial mucinous hyperplasia with luminal mucus accumulation in airways , 4—severe mucinous alterations , some airways may be completely obstructed by mucus . Lung viral titers were determined as previously described [22 , 59] . Briefly , whole lungs were harvested from infected mice 4 days following infection , weighed , mechanically homogenized , and supernatant was stored at −80°C until further use . 1:10 serial dilutions of supernatants were performed and incubated on Vero cells ( ATCC ) in 6-well plates for 90 minutes at 37° C . Plates were rocked every 15 minutes and overlaid with a 1:1 mixture of Eagle minimum essential medium ( EMEM , Lonza , Walkersville , MD ) and 1% SeaKem ME agarose ( Cambrex , North Brunswick , NJ ) . Following 5 days of incubation at 37° C , 5% CO2 , plates were stained with a 1:1 mixture of EMEM and 1% agarose containing 1% neutral red ( Sigma-Aldrich ) . Plaques were counted after 24–48 hours . All statistical analyses were performed using Prism software ( GraphPad Software , San Diego , CA ) . Data was compared using unpaired , two-tailed Student t tests between two groups or one-way ANOVA with Tukey-Kramer post-test analyses for more than two groups , to determine if there was a statistical significance of at least α = 0 . 05 . Asterisks are used to define a difference of statistical significance between the indicated group and its respective control group unless otherwise indicated by a line . | RSV is a significant healthcare burden and is the leading cause of bronchiolitis and pneumonia during childhood . The failure of the 1960's FI-RSV vaccine trial to not only elicit protection against RSV infection , but also provoke enhanced morbidity and mortality in vaccinees has significantly hampered development of new RSV vaccines for fear of disease potentiation . Therefore we sought to determine the specific immunological mechanisms that mediate FI-RSV VED to provide a framework to evaluate factors associated with disease exacerbation . Work presented herein demonstrate for the first time that individual disease manifestations associated with FI-RSV-immunization are mediated by distinct CD4 T cell subsets and not by eosinophils . Our results stress the need to evaluate multiple disease parameters for future RSV vaccine candidates . Failure to thoroughly assess the immune response and disease manifestations associated with new candidate vaccines may lead to undesired results in vaccine trials and further hinder future vaccine development . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | RSV Vaccine-Enhanced Disease Is Orchestrated by the Combined Actions of Distinct CD4 T Cell Subsets |
Palmitoylation is a key post-translational modification mediated by a family of DHHC-containing palmitoyl acyl-transferases ( PATs ) . Unlike other lipid modifications , palmitoylation is reversible and thus often regulates dynamic protein interactions . We find that the mouse hair loss mutant , depilated , ( dep ) is due to a single amino acid deletion in the PAT , Zdhhc21 , resulting in protein mislocalization and loss of palmitoylation activity . We examined expression of Zdhhc21 protein in skin and find it restricted to specific hair lineages . Loss of Zdhhc21 function results in delayed hair shaft differentiation , at the site of expression of the gene , but also leads to hyperplasia of the interfollicular epidermis ( IFE ) and sebaceous glands , distant from the expression site . The specific delay in follicle differentiation is associated with attenuated anagen propagation and is reflected by decreased levels of Lef1 , nuclear β-catenin , and Foxn1 in hair shaft progenitors . In the thickened basal compartment of mutant IFE , phospho-ERK and cell proliferation are increased , suggesting increased signaling through EGFR or integrin-related receptors , with a parallel reduction in expression of the key differentiation factor Gata3 . We show that the Src-family kinase , Fyn , involved in keratinocyte differentiation , is a direct palmitoylation target of Zdhhc21 and is mislocalized in mutant follicles . This study is the first to demonstrate a key role for palmitoylation in regulating developmental signals in mammalian tissue homeostasis .
Palmitoylation ( or protein S-acylation ) is a reversible post-translational lipid modification which involves addition of the fatty acid palmitate onto specific cysteine residues [1] . Some post-translational lipid modifications such as myristoylation and prenylation serve to localize otherwise soluble proteins to the cytoplasmic surfaces of cellular membranes . In contrast , palmitoylation substrates are proteins that are already membrane associated , and the modification acts to increase or stabilise membrane affinity or to traffic the protein to specific membrane domains . In particular , palmitoylation results in localization of the protein to lipid rafts; domains of the plasma membrane rich in cholesterol and sphingolipids . Furthermore , as palmitoylation is reversible , it allows for membrane localization or trafficking to be dynamically regulated . This has best been demonstrated in synapses , where palmitoylation regulates membrane localization and activity of the AMPA receptor [2] and GABAA receptor [3] . Palmitoylation of the post-synaptic density protein PSD95 permits clustering of the protein at synapses and regulates synaptic strength [4] . A recent global study of the neural palmitoyl-proteome highlights the breadth of targets that are rapidly modulated by palmitoylation [5] , further emphasizing the importance of this modification in dynamic biological processes . Members of the zinc finger , DHHC containing ( ZDHHC ) protein family have recently been shown to promote palmitoylation of intracellular proteins in yeast and in mammalian cells [6]–[8] . These palmitoyl-acyl transferases ( PATs ) are predicted membrane proteins possessing a cysteine-rich domain and a putative zinc finger with a characteristic Asp-His-His-Cys ( DHHC ) motif , required for activity . This family is encoded by 24 genes in both mouse and humans , of which 23 are orthologous pairs . Assaying individual target proteins against the entire repertoire of PATs indicates that there is substrate specificity; each substrate is primarily modified by a subgroup of structurally similar ZDHHC proteins [9] . Although some human ZDHHC genes have been implicated in cancer [10] , [11] , genetic evidence for function of these genes is limited to neurological disorders . ZDHHC8 shows association with schizophrenia in humans and neurophysiological deficits in mice [12]–[14] . X-linked mental retardation is associated in a few patients with loss of expression of ZDHHC15 [15] and in others with frameshifts , splice or missense mutations of ZDHHC9 [16] . Recently , the Drosophila ortholog of Zdhhc8 ( App ) was shown to play a key role in patterning and growth control of imaginal discs [17] . However , very little is known about specific palmitoylation functions during normal mammalian development . Several lineage-restricted stem cell populations exist in the adult skin and contribute to renewal of only their own specific niche under normal steady-state conditions [18] . Their progeny proliferate , migrate and terminally differentiate along the lineages of the interfollicular epidermis ( IFE ) , hair follicle and sebaceous gland [reviewed in 19] . The cornified layer of postnatal skin is constantly shed and replenished by progeny of the epidermal stem cells in the basal IFE , which proliferate , differentiate and migrate suprabasally . Similarly , hair shafts are shed and replaced in a cycle of regression ( catagen ) , rest ( telogen ) and regeneration ( anagen ) . During each anagen , stem cells , residing in the permanent bulge region , are mobilized to provide hair follicle progenitors , which differentiate into eight different lineages that make up the hair shaft ( consisting of the medulla , cortex and hair shaft cuticle ) , the inner root sheath ( IRS ) ( consisting of the inner root sheath cuticle , Huxley's and Henle's layers ) , the companion layer cells and the outer root sheath ( ORS ) . Also within the permanent portion of the follicle is the sebaceous gland which produces lipid-rich sebum to lubricate the skin and hair , in addition to providing antibacterial activity . Sebum is released by disintegrating sebocytes that are continuously replaced from progenitors in the periphery of the gland . These three stem cell lineages require a precise balance of self-renewal and differentiation of their committed progeny . However under certain experimental conditions or genetic manipulations , stem cells from one niche can contribute to hair , IFE and sebaceous gland lineages [20] , [21] , highlighting the interdependence of these epidermal compartments in maintaining homeostasis . The depilated mutation ( dep , MGI:94884 ) results in a recessive phenotype characterized by variable hair loss , with thinner and shorter hairs remaining in a greasy coat . Recombination experiments show that the phenotype is due to a defect in the epidermis , rather then the dermis [22] . Here , we genetically map and further characterize the dep mutant and show that it carries a single amino acid deletion in Zdhhc21 , resulting in loss of PAT activity . A detailed study of the phenotype demonstrates that lack of palmitoylation by Zdhhc21 results in hyperplasia of the IFE and sebaceous glands and delayed differentiation of the hair shaft . Furthermore , we identify Fyn , a member of the Src family of tyrosine protein kinases required for keratinocyte differentiation , as a direct palmitoylation target of Zdhhc21 and demonstrate its mislocalization within dep mutant follicles .
The location of the dep mutation has previously been defined by complementation against a set of chromosomes bearing deletions centred on the Tyrp1 gene [23] . The endpoints of those deletions defining the proximal and distal boundaries of the candidate interval were further refined using polymorphic markers on mice carrying the deletion chromosome opposite a Mus spretus chromosome [24] , [25 , data not shown] . The candidate location of dep , defined by the deletions 46UThc proximally and 1OZ distally , is only 160kb in length and contains all or part of just 3 genes: Zdhhc21 , Cer1 and Frem1 ( Figure 1A ) . Two of these have existing established mutations . Frem1 is associated with 2 ENU-induced alleles and the classical mutation head blebs ( heb ) [26] which result in an embryonic blebbing phenotype , and is a mouse model for Frasers Syndrome . Furthermore , a genetic complementation analysis between a Frem1 mutant ( bfd ) and dep produces normal mice ( personal communication , Monica Justice ) , indicating Frem1 is not allelic to dep . There are several knockout mutant alleles of Cer1 but none of these exhibit the dep phenotype [27]–[29] . We have sequenced all known exons of both Frem1 and Cer1 in dep DNA have found no mutations . Additionally no non-coding RNAs are annotated or predicted within this interval ( miRBase: microrna . sanger . ac . uk , Ensembl: www . ensembl . org , VEGA: vega . sanger . ac . uk ) . However , sequencing of the 7 exons of Zdhhc21 ( MGI:1915518 ) in dep mutants revealed a 3-bp deletion which results in the deletion of a single , highly conserved , phenylalanine residue ( del-233F ) close to the C terminus of the protein ( Figure 1B ) . Although this deletion was the only coding alteration found in the candidate interval , it remained possible that an undetected non-coding mutation could affect expression of genes outside the interval . To establish the causative link between Zdhhc21 and the dep phenotype , we generated transgenic mice containing the bacterial artificial chromosome , RP23-76J17 , containing only Zdhhc21 and Cer1 ( Figure 1A ) . When crossed onto a dep background , this transgene rescues the hair phenotype to a smooth and shiny dorsal coat , indistinguishable from wild-type , whilst the hair of nontransgenic littermates retains the greasy and disorderly dep phenotype ( Figure 1C ) . Later in life , non-transgenic mutant littermates lose their hair , whilst the transgenic mice do not . Skin sections of transgenic rescued mice show a normal histological appearance , confirming that the dep phenotype is fully rescued ( Figure 1D and 1E ) . Zdhhc21 has previously been demonstrated to have palmitoyl transferase ( PAT ) activity . Among 23 Zdhhc members tested , endothelial nitric oxide synthase ( eNOS , Nos3 ) [30] and lymphocyte-specific protein tyrosine kinase ( Lck ) [31] were found to be robustly palmitoylated by Zdhhc21 . Using these substrates , we examined whether the dep mutant Zdhhc21 protein retains PAT function . To test PAT activity , plasmids encoding tagged wild-type and mutant Zdhhc21 proteins were cotransfected with plasmids expressing Lck or eNOS ( Nos3 ) . Palmitoylation of substrates was assessed by metabolic labeling with [3H]palmitate followed by SDS-PAGE and fluorography [8] , [9] . Wild-type Zdhhc21 protein enhanced both eNOS and Lck palmitoylation , whilst the del233F protein showed no enhancement over background palmitoylation . A second mutant protein , C120S , in which the cysteine residue in the conserved DHHC motif was mutated , was also inactive in this assay ( Figure 2A ) . As mislocalisation of the mutant protein could affect its function in vivo , we examined the cellular localization of tagged variants of Zdhhc21 proteins in cell culture . In primary keratinocytes , HA-tagged wild type Zdhhc21 localizes to highly specific cytoplasmic structures , which co-localise with the cis-Golgi marker GM130 , consistent with previous studies showing localization of other Zdhhc proteins to the Golgi ( Figure 2C ) [8] , [30] . In contrast , Zdhhc21-del233F colocalizes with the endoplasmic reticulum ( ER ) marker , protein disulfide isomerase ( PDI ) , demonstrating that dep mutant protein is unable to target specifically to the Golgi and appears to be trapped in the ER . ( Figure 2I ) . We further verified these observations by transfection in NIH-3T3 cells , and demonstrated the mislocalisation and lack of PAT activity of additional mutant forms of the protein ( Figure S1 ) To define the target tissue in which PAT function is required for normal hair development , Zdhhc21 mRNA and protein expression were analyzed at embryonic and postnatal time-points related to hair follicle morphogenesis and cycling . In the developing skin , Zdhhc21 expression could not be detected prior to hair follicle induction ( E13 . 5 ) or early morphogenesis ( E14 . 5 ) ( data not shown ) . Expression of Zdhhc21 is initially detected in the inner root sheath ( IRS ) of developing vibrissae follicles at E16 . 5 ( Figure S2 ) and later in the developing IRS of E18 . 5 pelage follicles ( data not shown ) . Postnatally , Zdhhc21 exhibits two patterns of expression in distinct layers of more distal post-mitotic lineages in the hair bulb . Strong ubiquitous cellular expression of Zdhhc21 is detected in a single layer of the IRS ( Figure 3A ) . Double immunofluorescence with antibodies against trichohyalin ( AE15 ) ( Figure 3A ) or Gata3 , which is expressed only in Huxley's layer and the IRS cuticle ( Figure 3D ) , demonstrated partial co-localization with trichohyalin but not Gata3 , indicating Zdhhc21 is expressed in Henle's layer , the outermost IRS layer . A second Zdhhc21 expression domain , marked by punctate staining , is found predominantly in the outermost layer of cells expressing hair cortex keratins ( AE13-positive ) ( Figure 3B , white arrowhead ) and Foxn1-positive cells ( Figure 3C , white arrowhead ) , indicative of the hair shaft cuticle . A less prominent but similarly punctate pattern is found in the adjacent Gata3-positive IRS cuticle cells ( Figure 3C and 3D , yellow arrowhead ) . As in cell culture , these Zdhhc21-positive punctae colocalize with cis-Golgi marker GM130 in vivo suggesting that the protein in these cells is active in palmitoylation ( Figure 3E ) . Importantly , while Zdhhc21 transcript expression is not altered in dep follicles ( Figure S2D and S2E ) , mutant Zdhhc21 protein is mislocalized in both cuticle lineages where it shows diffuse staining ( Figure 3F , Figure S3 ) . Together , the loss of in vivo Golgi localization of Zdhhc21 in dep mutants and the resulting mutant hair shaft phenotype suggest that Zdhhc21 function is primarily required in the cuticle layer . Both patterns of hair follicle expression are hair cycle dependent; expression of Zdhhc21 cannot be detected in telogen ( Figure S2J ) or very early anagen follicles , but it is first expressed in nested layers of the IRS and cuticle of anagen and catagen follicles ( Figure S2 ) . Comparable cyclic expression of Zdhhc21 during this postnatal hair cycle is also observed in dep mutant skin . Notably , the onset of expression in differentiating lineages in the anagen follicles correlates with the first sign of abnormal morphology ( Figure S3 ) . Outside the cycling portion of the hair follicle , we find specific cellular Zdhhc21 protein strongly present in the degenerated remains of the IRS surrounding the isthmus , in the permanent portion of the follicle ( Figure S2I ) . Importantly , expression of Zdhhc21 mRNA or protein cannot be detected in the bulge , IFE or in the sebaceous gland at any stage of the hair cycle . The dep phenotype can be identified macroscopically within the first postnatal week as a greasy and disorderly hair distribution , as previously reported [22] . To determine the cellular basis of the observed abnormalities , we conducted histological and molecular analyses of skin samples at a range of developmental stages . Dorsal skin from dep embryos at E14 . 5 and E18 . 5 have follicle morphology and numbers comparable to wild type ( Figure 4G and 4J and data not shown ) , indicating that Zdhhc21 function is dispensable for hair follicle patterning and morphogenesis . The first abnormalities in dep mice are observed shortly after birth where mild sebaceous gland hyperplasia and slight thickening of the IFE develop at P5 . While dep mutants appear to progress through the first hair cycle normally ( Figure 4H and 4K ) , by telogen , defects in the permanent portions of dep skin are apparent and include thickening of the IFE and a dilated infundibulum ( Figure 4I and 4L ) . By the onset of the second hair cycle around P28 , the dep follicles are growth retarded and immature compared to littermates ( Figure 4A and 4D ) coincident with the onset of Zdhhc21 expression ( Figure S3 ) . In addition , the thickening of IFE and sebaceous gland hyperplasia appear more prominent ( Figure 4D , arrowed ) . Staining for lipids reveals enlarged sebaceous glands with an excess of sebum ( Figure 4E and 4F ) , underlying the greasy appearance of the coat at this stage . In some dep animals , from P28 onwards , small epidermal cysts containing keratinized material can be observed in the upper portion of the dermis ( not shown ) . Given the hyperplastic changes observed in the upper portions of dep follicles , we asked whether the closely associate bulge stem cell niche was also perturbed . Keratin 15 ( K15 ) is a marker for these cells , and indeed , the K15-positive population is expanded in dep mutants , although its expression remains restricted to the bulge niche , suggesting that changes in the size and shape of the dep bulge during the hair cycle could impact progenitor allocation to various epidermal compartments ( Figure S5F and S5L , data not shown ) . The hyperplastic phenotype of dep IFE and sebaceous glands is most prominent during anagen in younger skin , when growth stages of the hair cycle are highly synchronized . To determine whether this hyperplastic phenotype was due to increased proliferation of these non-follicular compartments , we carried out BrdU pulse labeling cohorts of P32 gender-matched animals . These studies revealed a small but significant increase in the fraction of BrdU positive dep IFE cells ( 8 . 314±1 . 493 , n = 2 , p<0 . 005 ) compared with heterozygous ( 6 . 790±1 . 8223 , n = 2 ) or wild type controls ( 6 . 686±1 . 711 , n = 2 ) ( Figure S4L ) . A greater increase in percentage BrdU positive cells was observed in dep sebaceous glands ( 11 . 46±2 . 784 , n = 2 , p<0 . 05 ) compared to controls ( heterozygous: 6 . 881±2 . 499; wild type: 7 . 882±2 . 868 ) . A concomitant decrease of BrdU labeling is observed in dep mutant follicles during anagen ( Figure S4K ) . Additional proliferation markers , including the M-phase marker phospho-histone H3 and a general marker Ki67 identifying all phases of the cell cycle , confirm this change in proliferation is relatively small and is restricted to the basal compartment ( Figure S4A , S4B , S4C , S4D , S4E , S4F , S4G , S4H , S4I , S4J ) . We asked whether aberrant terminal differentiation of keratinocytes in the IFE also contributes to the dep phenotype , such that an expanded progenitor pool contributing to the IFE could result in an increase in cell number in the stratified layers . Immunolabelling of basal cell markers p63 and keratin 5 ( K5 ) showed an expansion of this progenitor compartment ( Figure 5A , 5B , 5C , and 5E ) . Furthermore , p63-positive cells were found in thickened K10-positive spinous layer in dep skin ( Figure 5D and 5F , arrowed ) . The terminal differentiation markers loricrin and filaggrin were only slightly expanded in dep mutants ( Figure S5A , S5B , S5G , S5H ) indicating that differentiation in dep mutants occured but was significantly delayed . Interestingly , the transcription factor Gata3 , which is normally expressed in the basal and suprabasal layers of the IFE where it directs keratinocyte and lipid based barrier differentiation programs [32] , [33] , is strongly reduced in dep IFE ( Figure 5G and 5H , arrowed ) consistent with the observed delay in differentiation . Reduced levels of Gata3 in the dep IFE during anagen may contribute to defects in lipid biosynthesis required for barrier function , which may give rise indirectly to the hyperproliferative phenotype observed [34] . However , unlike Gata3 knock-out skin [32] , delays in the establishment of embryonic barrier function by dye penetration assays were not seen and keratinocyte terminal differentiation program in embryonic skin occurred normally ( Figure S6 ) . Furthermore , phenotypes associated with impaired barrier function , including failure to thrive or red shiny skin , were not observed in dep neonates . These observations suggest that any barrier defects present in dep mutants are likely quite subtle and limited to a postnatal window . In contrast to the decrease in Gata3 and altered terminal differentiation , an increase in phospho-ERK staining , indicative of growth factor and integrin signaling linked to increased proliferation in the basal layer of the IFE [35] , is observed in dep mutants ( Figure 5G and 5H , Figure S5D , S5D′ , S5D″ , S5E , S5E′ , S5J , S5J′ , S5J″ , S5K , S5K′ , arrowed ) . These observations together suggest that the thickening of the IFE observed during anagen in dep mutants is due to continued division and delayed differentiation of the expanded basal progenitor compartment after leaving the basement membrane . The restricted expression of Zdhhc21 in the IRS and cuticle of the hair follicle is hard to reconcile with a direct effect on proliferation and differentiation in the IFE and sebaceous gland . One possibility is that the physiologically relevant palmitoylation targets are highly diffusible signals , or are regulators of such signals . Alternatively , Zdhhc21 may act locally in the follicle to indirectly impact non-follicular lineages as a consequence of hair abnormalities in dep mice . Such a phenomenon is seen in K14-Cre-induced knockout of the hair-follicle specific , transcription factor Dlx3 , where the resultant abnormal and undifferentiated hair shafts are accompanied by hyperplastic sebaceous glands [36] . As palmitoylation is usually involved in the regulation of dynamic processes , we investigated whether key signalling events throughout the postnatal hair cycle were affected in dep mutant skin . Bone morphogenetic protein ( BMP ) signalling is required for embryonic hair follicle development and postnatal hair cycling [37] . Furthermore , conditional epidermal ablation of receptor BMPR1a [38]–[40] result in a hair loss phenotype associated with poorly differentiated hair follicles , and thickened IFE . However , no difference in expression of activated phospho-Smad1/5/8 , mediators of canonical BMP signalling , was detected in mutant skin at various stages of the hair cycle ( Figure 6A and 6B , data not shown ) . Transforming growth factor beta ( TGF-β ) signalling also plays a key role in hair follicle development and cycling , as well as keratinocyte differentiation [41] , [42] . No difference in expression of activated canonical intracellular mediator phospho-Smad2 was observed in dep mutant follicles or IFE ( Figure 6E and 6F , data not shown ) . Recent studies have suggested a key role for palmitoylation in BMP- [43] or TGF- mediated signalling events [44] via the non-canonical p38 MAPK arm; however , no alterations in phospho-p38 staining could be detected in dep mutants ( Figure 6C and 6D ) . These results suggest that despite the profound follicular phenotype of dep mutant mice , these key developmental signals required for adult hair cycle are not broadly affected . The range of phenotypes seen in dep animals is reminiscent of a reduction of Wnt signalling , which plays many important roles during hair development . Precise levels of β-catenin activation are required for differentiation into specific epidermal lineages . High levels of β-catenin signaling promote hair follicle formation [45] , [46] and normal differentiation of the hair shaft [47] . Low levels of Wnt/β-catenin signaling promote terminal differentiation of the IFE and sebaceous glands [48] , [49] . To determine whether a reduction in Wnt signalling is seen in dep mutant skin , we analyzed Wnt responses in embryonic and adult skin by immunohistochemistry . Wnt responses during embryonic hair follicle morphogenesis appear normal in dep embryos ( data not shown ) . At the initiation of the first , synchronized , anagen phase ( P24 ) , prior to expression of Zdhhc21 , both control and dep littermates show nuclear Lef1 in the dermal papilla and surrounding secondary hair germ ( Figure S7A , S7B , S7C , S7D ) . However , in dep mice , propagation of this anagen response appears defective and differentiation of the hair shaft and cortex is significantly delayed . By P28 , at the onset of Zdhhc21 expression when wild type hair is well established in anagen , the delayed dep hair follicles fail to expand strong Lef1 and nuclear β-catenin expression in the matrix and precortex ( Figure 6H and 6J ) . Accordingly , the Lef1 transcriptional target , Foxn1 , which regulates expression of hair specific keratins , is strongly reduced or absent from mutant follicles ( Figure 6A and 6B , Figure S7I and S7K ) as are acidic hair shaft keratins ( AE13 ) , ( Figure S7J and S7L ) consistent with the delayed state of development . By contrast expression of homeodomain transcription factor Hoxc13 , which also regulates expression of several hair shaft keratins , is still detected in all dep mutant follicles at this stage of anagen ( Figure 6C and 6D ) . Surprisingly , unlike the profound reduction in Gata3 expression observed in the dep IFE and similarities between follicular phenotype observed in conditional Gata3 mutant mice [50] , Gata3 is still detected in all dep follicles although at slightly reduced levels throughout anagen ( Figure 6E and 6F , Figure S3 ) . By P35 , many dep follicles express levels of Lef1 and hair-shaft keratins comparable to controls , although the morphology of dep follicles remain misshapen and misoriented ( Figure S7M , S7N , S7O , S7P ) . Interestingly , some regions in dep mice continue to remain visibly “hairless” , although histological analysis reveals normal numbers of retarded follicles which fail to proceed through anagen and form functional hairs ( Figure 4D , insert ) . Our data suggest that a number of signalling pathways required for epidermal homeostasis are disrupted in the absence of Zdhhc21 PAT activity . During the anagen phase of the hair cycle there is a reduction of Wnt responses in the hypoproliferative dep follicles and an increase in phospho-ERK signalling in the hyperplastic mutant IFE . Which of these phenotypes are direct or indirect consequences of the loss of Zdhhc21 palmitoylation remains to be addressed . Given we cannot detect Zdhhc21 expression outside the follicle , we suggest the follicular phenotype observed in dep mutants is the primary cause where defects in hair shaft differentiation during anagen perturb processes at a distance in the IFE and sebaceous glands . Importantly , palmitoylation may influence the quality and the quantity of a signalling event rather than acting as an absolute ON/OFF switch . This is in keeping with the observation that the amplification of Wnt responses during early anagen is very delayed , and not completely blocked , suggesting some threshold could be operating and is eventually met in mutant follicles . At the synapse , palmitoylation mediates dynamic changes in membrane associations of pools of target proteins involved in signaling , cell adhesion and trafficking [51] . Given the rapid remodeling observed in the hair cycle , it is tempting to speculate that similar processes are involved in the skin and to ask what are the biologically relevant targets of palmitoylation . It should be noted , that while several targets have been identified for each of the 23 Zdhhc PATs , each of these target so far is palmitoylated by multiple PATs , at least in vitro . This suggests a level of functional redundancy in the palmitoylation machinery exists . It also suggests that the dep phenotype could result from the loss of palmitoylation of one or more targets . We reasoned that any direct target of PAT activity must be expressed in the same cells in which we detect Zdhhc21 expression . One possibility is the known Zdhhc21 target , eNOS [30] , which is expressed in the skin [52] . However , observation of eNOS mutant mice indicates that this is not required for normal skin and hair development [53 , data not shown] , suggesting it is unlikely to be the key palmitoylation target of Zdhhc21 in skin . Given that Zdhhc21 expression is restricted to hair follicles but multiple epidermal lineages are affected in dep mutants , we asked whether diffusible Wnt proteins could be functional palmitoylation targets for Zdhhc21 . Wnt proteins are known to be palmitoylated , and this modification is essential for their function [54] . However , this is believed to be mediated by the ER protein porcupine ( PORC ) , a PAT unrelated to the Zdhhc family [55] . Nevertheless , we tested three candidate Wnts ( Wnt3a , 5a and 10a ) , which are expressed in domains that overlap with Zdhhc21 expression [56] . Although these Wnts are predicted to have multiple palmitoylation sites ( CSS-Palm , data not shown ) [57] , none are directly palmitoylated by Zdhhc21 ( data not shown ) . Trafficking of Wnt ligands from the Golgi to endosomes requires the cargo receptor Wntless/Evi ( Wls ) , a seven-transmembrane protein expressed in the Golgi [58] . Sustained Wnt signaling also requires that this cargo receptor be recycled via the retromer complex . Similar cargo proteins have been shown to require DHHC-dependent palmitoylation for retrograde sorting [59] . However , no palmitoylation of Wls by Zdhhc21 was detected in our co-transfection assay ( data not shown ) . While it remains possible that Zdhhc21 acts locally in a subset of Wnt-responding cells in the hair follicle required for proper hair shaft differentiation ( i . e . through modulation of receptor complexes or intracellular signal transduction ) , we have been unable to establish a direct link between palmitoylation and Wnt responses in this present study . While the Src family kinase , Lck , is a known target of Zdhhc21 , it is not required for keratinocyte differentiation nor do Lck mutants have any gross skin phenotype [60] . We therefore considered whether other related kinases that are epidermally expressed could be potential palmitoylation targets . Fyn is indeed expressed in the skin where it plays a role in keratinocyte differentiation in vitro and in vivo [60] , in part through down-regulating EGFR signaling [61] . The role for Fyn in hair follicle development and cycling remains unclear . Aged Fyn−/− Fak+/− mice develop progressive hair loss with IFE and sebaceous gland hyperplasia , but this is not observed in Fyn−/− mutants [62] . We therefore tested GFP-tagged Fyn with a panel of Zdhhc PATs by co-transfection and metabolic labelling . Fyn is palmitoylated in our in vitro assay by Zdhhc2 , 3 , 7 , 10 , 15 , 20 and 21 ( Figure 7A , data not shown ) . Palmitoylation of Fyn by Zdhhc21 results in efficient targeting of Fyn to the perinuclear region in HEK cells ( Figure 7B and 7C ) . Fyn is also subject to myristoylation , an irreversible covalent lipid modification involved in membrane targeting and signaling . Interestingly , we show a mutant Fyn construct lacking the myristoylation site ( Fyn-G2A ) cannot be palmitoylated by Zdhhc21 or correctly targeted ( Figure 7D and 7E ) , suggesting palmitoylation of Fyn is downstream of the myristoylation event . To test whether Fyn was a viable in vivo target of Zdhhc21 , we examined localization Fyn in wild type and dep follicles . In wild type anagen ( P32 ) follicles , Fyn is initially expressed diffusely in the hair bulb becoming very discretely localized to membranes at junctions between cells of the IRS cuticle with differentiation ( Figure 7F and 7F′ , arrowed ) . This localization is weaker and more diffuse in dep follicles ( Figure 7G and 7G′ , Figure S8A and S8B ) . These results were confirmed using an antibody which recognizes the active , phosphorylated forms of all Src-family kinases ( SFKs ) in addition to Fyn . The active SFKs show a broader expression pattern with striking membrane localization , including the junctions between cells of the IRS cuticle ( Figure 7H , Figure S8C ) . In contrast , while general expression of active SFKs is not altered in dep mutant follicles , uniform active SFK is seen around cells of the IRS cuticle ( Figure 7I , Figure S8D ) . Given that the Zdhhc21 Golgi-localization observed in the IRS cuticle of wild type follicles is lost in dep mutants and that Golgi-localization is dependent on auto-palmitoylation via the PAT activity of wild type Zdhhc21 , our results suggest that Zdhhc21-mediated palmitoylation of Fyn is required in vivo for Fyn's discrete localization in the differentiating IRS cuticle . It is interesting to note that in the despite the proliferation and differentiation defects observed in the dep mutant IFE , and Fyn's established role in keratinocyte differentiation , the localization of Fyn in the dep IFE is normal , although it is delayed as expected given the expanded basal compartment in dep mutants ( Figure S8E and S8F ) . Our data demonstrates that Fyn is a direct palmitoyaltion target for Zdhhc21 in vitro and dysregulation of Fyn occurs in vivo in dep mutant follicles . In assessing the dep phenotype , it is worth noting that the consequences of dysregulated palmitoylation may not mirror those of gene ablation studies , as palmitoylation has the potential to modulate cell signaling in a complex manner . Furthermore , the phenotypic features of dep mice extend beyond those likely caused by the loss of Fyn activity , correlating with the broad substrate specificity of different Zdhhc proteins . To comprehensively tackle the functional requirement of Zdhhc21 , the use of global approaches , recently applied in yeast , will be necessary to compare the palmitoylated proteome of dep mutant and wild type cells [63] . This study is the first to highlight a role for palmitoylation in mammalian development and homeostasis . We have demonstrated that loss of Zdhhc21 function in dep mutants results in defects in all three epidermal lineages , including hyperplasia of the IFE and sebaceous glands with a delay in hair follicle differentiation . Given the highly restricted pattern of Zdhhc21 expression to the differentiating hair follicle , our results demonstrate that defective palmitoylation can have far-reaching effects disrupting epidermal homeostasis by altering the balance between proliferation and differentiation . Although the full identity of direct and biologically relevant palmitoylation targets in the skin remains unknown , we show Zdhhc21 can directly palmitoylate Fyn in vitro and this modification affects Fyn localization both in vitro and in vivo . Future studies into the distinct and overlapping roles of additional Zdhhc members will help to fully understand the role of palmitoylation in modulating key signals during development .
Mice were maintained in accordance with MRC Guidelines “Responsibility in the Use of Animals for Medical Research” ( July 1993 ) and research licenced by the UK Home Office under the Animals ( Scientific Procedures ) Act 1986 . Animals were maintained in SPF environment and on a C57BL/6J background . Genomic DNA extracted from ear clips or tail biopsies was used for PCR genotyping . For dep , exon 7 of Zdhhc21 was amplified by standard PCR to yield a 249bp fragment that was run on the ABI310 genetic analyzer to detect the deletion . The dep genotyping primer sequences were: 5′-FAM-AGCTGACTGAAGGGCACC-3′ ( Exon 7F ) and 5′-AAAACCTGTAACGCATTTCCA-3′ ( Exon 7R ) . For transgenic rescue , purified RP23-76J17 BAC DNA ( BAC PAC Resource Center ( BPRC ) , the Children's Hospital Oakland Research Center Institute , CA ) was injected into homozygous dep embryos . The presence of the BAC was genotyped using three markers , including CmR specific to the plasmid , as well as two markers at both ends of the BACs , amplifying the border between the BAC carrier plasmid and BAC genomic region . For timed matings for embryonic samples , the morning of vaginal plug was counted as 0 . 5 ( E0 . 5 ) . For postnatal timepoints , a set of gender-matched wild type , heterozygous and mutant littermates were aged accordingly from the day of birth 0 ( P0 ) . Mapping of the deletion endpoints defining dep was described by Smyth et al [24] . cDNA encoding wild type and mutant Zdhhc21 were transfected into HEK293 cells , along with candidate palmitoylation substrates . Cells were labelled with [3H]palmitate for 4 hours as previously described [8] , [9] . After metabolic labeling , palmitoylated proteins were analysed by SDS-PAGE . Transfection efficiency and translation of substrates was assessed by Western blotting . The mammalian expression vector of wild-type Zdhhc21 , pEFBos-HA-Zdhhc-21 ( with EF1-alpha promoter ) , was provided by Masaki Fukata . It was then modified by using the QuikChange® Site-Directed Mutagenesis Kit ( Stratagene ) , to introduce single nucleotide changes for the following Zdhhc21 alleles: L91F , C95S and C106S . For the dep mutation , del-233F , was modified from pEFBos-HA-Zdhhc-21 by subcloning dep cDNA ( Access RT-PCR System , Promega ) into the BamHI sites flanking the insert . Full-length mouse Wnt cDNAs ( kindly provided by Jeremy Nathans ( Wnt3a ) , Wnt5a ( Yingzi Yang ) and Wnt10a ( Takano Yamamoto ) ) were introduced into a pCMX2GFPFLAGSTOP vector ( kindly provided by Nick Gilbert ) to express double FLAG-tagged full-length proteins . Constructs were verified by direct DNA sequencing , using primers: 5′-CAAATGGGCGGTAGGCGTGT-3′ ( Pcmxgfp2fg-seqF ) 5′-TTGTCCAATTATGTCACACCA-3′ ( Pcmxgfp2fg-seqR ) The Myc-tagged full length Wls cDNA used in these studies has accension number BC018381 ( Catalog No: MR207034: Origene , MD ) . Human foreskin keratinocytes [64] and NIH 3T3 cells ( ATCC ) were maintained as described . DNA plasmids were transfected into cells using Lipofectamine 2000 ( Invitrogen ) as per manufacturer's specifications . Zdhhc21 cDNA product was generated using Access RT-PCR kit ( Promega ) and cloned into p-GEM-T . The RT-PCR primers used were: 5′-CATGGGCTTGATTGTCTTTGT-3′ and 5′-ACGTGATTGGCAAAGTGGTAG-3′ . DIG-labeled RNA section in situ protocol was performed , details available on request . Custom rabbit polyclonal antibodies to Zdhhc21 were generated using a peptide comprising residues 73–87 ( GRLPENPKIPHAERE+C ) ( Eurogentec ) . Pre-incubation of the Zdhhc21 antibody with its immunizing peptide blocked all signal in immunohistochemistry ( Figure S2L ) . Paraffin sections were dewaxed and rehydrated , followed by washes in TBST +0 . 5% Tx-100 ) . Microwave antigen retrieval was carried out using 1mM EDTA ( pH8 ) or citrate buffer ( 1 . 8mM citric acid , 8 . 2mM sodium citrate , pH6 ) for 20–30 minutes depending on the antigen at 900W . Cryosections samples were allowed to come to room temperature and post-fixed in acetone ( −20’C for 10 minutes ) followed by rinsing in water . No antigen retrieval step was required for cryosections . Slides were cooled to room temperature and washed in TBST . Slides were blocked in 10% donkey serum/TBST , followed by TBST washes . Primary antibodies were diluted in 1% donkey serum/TBST incubated on slides overnight at 4°C ( Table 1 ) . After TBST washes , secondary antibodies were diluted in 1% donkey serum/TBST and added to the slides for 60 minute incubation ( Table 2 ) . Following stringent TBST washes , nuclei were stained with DAPI ( Sigma ) or TOTO-3 ( Molecular Probes ) . In the case of TOTO-3 , slides were pre-incubated with RNAse A during primary antibody incubation . Slides were mounted with Vectashield ( Vector ) or Prolong Gold ( Molecular Probes ) antifade media and coverslips . Brightfield and fluorescent images were acquired using a Coolsnap HQ CCD camera ( Photometrics Ltd , Tucson , AZ ) Zeiss Axioplan II fluorescence microscope with Plan-neofluar objectives . Image capture and analysis were performed using in-house scripts written for IPLab Spectrum ( Scanalytics Corp , Fairfax , VA ) . For colocalization studies , 0 . 5–1 µm optical slice images in Z-stacks were acquired with a Zeiss LSM510 confocal microscope and Zeiss Plan Apochromat lenses ( Carl Zeiss , Welwyn Garden City , UK ) . LSM software was used for analysis ( Carl Zeiss , Welwyn Garden City , UK ) . For BrdU labeling experiments , 2 age- and gender-matched mice of each genotype were injected with 50 µg BrdU/g body weight and sacrificed after 2 hrs . Skin sections were dewaxed , subjected to proteinase K antigen retrieval , followed by HCl denaturation and neutralization , before incubation with anti-BrdU antibody ( BD ) . For indirect colorimetric visualization , a biotinylated donkey anti-mouse secondary antibody ( Jackson Labs ) and Vectastain Universal Elite ABC Kit ( Vector Laboratories ) were used , followed by NovaRed substrate ( Vector Laboratories ) according to manufacturer's protocol . A proliferative index was calculated by counting the number of positive cells divided by the total number of nuclei within the epidermal compartment , in each of ten fields at 10× magnification , and the average index per field was calculated . Statistical significance was calculated using a two-tailed Student's t-test . | During embryonic development , growth and patterning are regulated at many levels . Signals that mediate transcriptional activity , where and when genes are expressed , are a primary level of regulation . However , developmental signals can be further fine-tuned by modulating protein stability , localization , and activity via post-translational modifications . One such modification is the reversible addition of the fatty acid palmitate to proteins . This modification mediates dynamic trafficking of target proteins to specific subdomains of the cell . A large family of enzymes carries out this palmitoylation process , where each family member has specificity towards particular targets . However , the functional significance of palmitoylation during mammalian development is unclear . We present evidence of a critical role for palmitoylation during mouse development using a mutation of a specific palmitoylating enzyme , whose loss of function leads to hair loss and skin defects in depilated ( dep ) mice . Despite its restricted expression in hair follicles , loss of function of this enzyme results in developmental defects in nearby structures . We show that palmitoylation plays an important regulatory role in hair growth and epidermal homeostasis . | [
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] | 2009 | Palmitoylation Regulates Epidermal Homeostasis and Hair Follicle Differentiation |
In addition to its surface glycoprotein ( GP1 , 2 ) , Ebola virus ( EBOV ) directs the production of large quantities of a truncated glycoprotein isoform ( sGP ) that is secreted into the extracellular space . The generation of secreted antigens has been studied in several viruses and suggested as a mechanism of host immune evasion through absorption of antibodies and interference with antibody-mediated clearance . However such a role has not been conclusively determined for the Ebola virus sGP . In this study , we immunized mice with DNA constructs expressing GP1 , 2 and/or sGP , and demonstrate that sGP can efficiently compete for anti-GP12 antibodies , but only from mice that have been immunized by sGP . We term this phenomenon “antigenic subversion” , and propose a model whereby sGP redirects the host antibody response to focus on epitopes which it shares with membrane-bound GP1 , 2 , thereby allowing it to absorb anti-GP1 , 2 antibodies . Unexpectedly , we found that sGP can also subvert a previously immunized host's anti-GP1 , 2 response resulting in strong cross-reactivity with sGP . This finding is particularly relevant to EBOV vaccinology since it underscores the importance of eliciting robust immunity that is sufficient to rapidly clear an infection before antigenic subversion can occur . Antigenic subversion represents a novel virus escape strategy that likely helps EBOV evade host immunity , and may represent an important obstacle to EBOV vaccine design .
Ebola virus ( EBOV ) is an enveloped single-stranded negative-sense RNA virus in the order Mononegavirales , which along with the Marburg virus ( MARV ) forms the Filovirus family . EBOV is the etiologic agent of Ebola Hemorrhagic Fever ( EHF ) , a highly lethal hemorrhagic fever with up to 90% mortality [1] . Since its discovery in 1976 , EBOV has caused sporadic outbreaks in Sub-Saharan Africa with death tolls in the hundreds . Interestingly , while filoviruses have been only recently discovered , they are one of the few non-retrovirus RNA paleoviruses identified in mammalian genomes , suggesting an ancient relationship with mammals [2] , [3] . Growing evidence suggests that bats are the natural reservoir of EBOV in the wild today [4]–[6] . Current treatment for Ebola hemorrhagic fever is purely supportive , and the lack of effective interventions underscores the importance of developing a broadly-protective vaccine that confers long-lasting immunity . The ability to develop such a vaccine is critically dependent on our understanding of the mechanisms by which EBOV suppresses , distracts , or otherwise evades the host immune response [7] . One widely hypothesized immune evasion mechanism employed by Ebola virus is secretion of a truncated viral glycoprotein by EBOV infected cells . The EBOV surface glycoprotein ( GP1 , 2 ) mediates host cell attachment and fusion , and is the primary structural component exposed on the virus surface . For this reason , GP1 , 2 is the focus of most EBOV vaccine research , and it is generally accepted that a robust anti-GP1 , 2 antibody response is crucial for protection against lethal EBOV challenge [8] . EBOV GP1 , 2 forms trimeric spikes on virion surfaces similarly to influenza HA and HIV Env [9] . Also like HA and Env , GP is first synthesized as an uncleaved precursor ( GP0 ) which is then cleaved in the Golgi complex by the protease furin [10] into two functional subunits: The N-terminal GP1 subunit contains the putative receptor-binding domain ( RBD ) , and the C-terminal GP2 subunit contains the fusion apparatus and transmembrane domain . GP1 , 2 is encoded in two disjointed reading frames in the virus genome . The two reading frames are joined together by slippage of the viral polymerase at an editing site ( a tract of 7-A's ) to insert an 8th A , generating an mRNA transcript that allows read-through translation of GP1 , 2 [11] , [12] . However , only about 20% of transcripts are edited , while the remaining 80% of unedited transcripts have a premature stop codon , resulting in synthesis of a truncated glycoprotein product ( sGP ) which is secreted in large quantities into the extracellular space . Though its production is conserved in all EBOV species , there has been considerable debate regarding the function of sGP . Unlike GP1 , 2 , sGP forms homodimers and appears to have some intrinsic anti-inflammatory activity [13]–[17] . The recent finding that EBOV quickly mutates to synthesize primarily GP1 , 2 in cell culture , while this mutant virus reverts to a primarily sGP-producing phenotype in vivo , suggests an important role for sGP in virus survival within the host [18] . Because sGP shares over 90% of its sequence with the N-terminal region of GP1 , 2 , it was initially hypothesized that sGP functions as a decoy for anti-GP1 , 2 antibodies . Early efforts to identify such activity yielded mixed results , and the observation that antibodies often do not cross-react between sGP and GP1 , 2 had cast doubt on this hypothesis [19]–[23] . Furthermore , recent studies demonstrated that immunization against GP1 , 2 elicits antibodies largely against epitopes not shared with sGP [24]–[27] . However , most of these studies investigated monoclonal antibodies from animals immunized with vaccines containing or expressing primarily GP1 , 2 , which does not represent the state of natural infection . Of note , one early study examined monoclonal antibodies from mice immunized with a Venezuelan equine encephalitis replicon that produces both GP1 , 2 and sGP , and found that many of these antibodies cross-reacted between GP1 , 2 and sGP [28] . Further , monoclonal antibodies isolated from human EHF survivors have been shown to preferentially react with sGP [19] . These studies suggest that sGP may play an important role in altering the host antibody response . In this study , we demonstrate that sGP induces a host antibody response that focuses on epitopes it shares with GP1 , 2 , thereby allowing it to bind and compete for anti-GP1 , 2 antibodies . We describe a mechanism that we term “antigenic subversion” , which is distinct from previously proposed “decoy” mechanisms in which secreted glycoprotein simply passively absorbs anti-glycoprotein antibodies . Importantly , we demonstrate that sGP can also subvert an existing anti-GP1 , 2 immune response that was only weakly cross-reactive with sGP . Antigenic subversion represents a novel host immune evasion mechanism that has important implications for EBOV vaccine design , and may shed light on how the virus survives in its natural reservoir .
We first generated EBOV GP constructs to individually express GP1 , 2 and sGP . In natural infection , EBOV directs the synthesis of sGP and GP1 , 2 through differentially edited mRNA transcripts ( Fig . 1A ) . However , it has been observed that DNA-dependent RNA polymerases ( DDRP ) do not edit with the same efficiency as the EBOV RNA polymerase [12] . Furthermore , in addition to polymerase slippage , it is possible that the 7-A editing site can also serve as a premature poly-adenylation signal , as well as a ribosomal slippage signal [29]–[31] . We thus generated a panel of EBOV GP editing site mutants in order to control the levels of sGP and GP1 , 2 expression ( Fig . 1B ) . GP-8A was made by inserting an 8th A into the wild type ( GP-7A ) editing site , resulting in GP1 , 2 as the dominant gene product . Silent A→G mutations were introduced into the GP-8A editing site to ablate transcriptional slippage , resulting in GP1 , 2Edit , that expresses GP1 , 2 as the sole gene product . The same mutations were also introduced into GP-7A to generate sGPEdit , that expresses sGP as the sole gene product . These constructs were subcloned into a mammalian expression vector ( pCAGGS ) and protein expression was examined in both HeLa cells ( Fig . 1C ) and 293T cells ( data not shown ) . Cells transfected with GP-8A and GP1 , 2Edit expressed GP1 , 2 intracellularly and on their surfaces , and secreted GP1 , 2 into the supernatant through previously characterized TACE-dependent cleavage [32] . Interestingly , GP1 , 2Edit produced higher amounts of GP1 , 2 than GP-8A . GP-7A and sGPEdit expressed high levels of sGP , which was secreted efficiently into the supernatant . GP1 , 2 expression by GP-7A was undetectable , likely because of minimal DDRP-mediated editing [12] . These expression experiments demonstrate that mutation of the editing site has a significant effect on GP expression . We next investigated the immunogenicity of editing site mutant DNA vaccines . Female BALB/c mice were immunized with GP1 , 2 or sGP-producing constructs ( Fig . 2A ) . Mice immunized with sGPEdit , GP-7A , and GP-8A constructs developed similar titers of anti-GP1 , 2 antibodies as measured by ELISA , while mice immunized with GP1 , 2Edit developed four-fold higher titers of anti-GP1 , 2 antibodies ( Fig . 2B ) . Mice immunized with constructs expressing predominantly sGP ( GP-7A and sGPEdit ) developed much higher titers of anti-sGP antibodies than mice immunized with constructs expressing predominantly GP1 , 2 ( GP-8A or GP1 , 2Edit ) ( Fig . 2C ) . As shown in Fig . 2D , GP1 , 2-immunized mice developed much higher titers of GP1 , 2-binding antibodies than sGP-binding antibodies . On the other hand , sGP-immunized mice developed much higher titers of sGP-binding antibodies than GP1 , 2-binding antibodies , despite the fact that sGP shares roughly 95% of its linear sequence with GP1 , 2 . These results suggest that in sGP-immunized animals , either many sGP-binding antibodies are directed against conformational epitopes not shared with GP1 , 2 , or they are directed against shared epitopes that are inaccessible in GP1 , 2 . Given that animals immunized by GP1 , 2 or sGP develop antibodies that preferentially bind to different GP isoforms , we performed Western blot analysis to determine if there is a difference in the linear epitopes targeted by antibodies in GP1 , 2 versus sGP-immunized mice . As shown in Fig . 3A , antisera from GP1 , 2-immunized mice reacted strongly with GP1 , 2 but only weakly with sGP . On the other hand , antisera from sGP-immunized mice reacted strongly with sGP , but only weakly with GP1 , 2 . This suggests that most linear epitopes targeted by anti-GP1 , 2 antibodies from GP1 , 2-immunized mice are unshared with sGP . To investigate whether the GP1 , 2-binding and sGP-binding antibodies in immunized mice were cross-reactive between the two GP isoforms or were separate populations of antibodies , we performed a competition ELISA to determine if sGP could compete with GP1 , 2 for GP1 , 2-binding antibodies ( Fig . 3B ) . Similar to the Western blot data , sGP was unable to compete for binding of anti-GP1 , 2 antibodies from GP1 , 2 immunized mice ( Fig . 3C ) . On the other hand , sGP was able to efficiently compete for anti-GP1 , 2 antibodies from sGP-immunized mice . As expected , GP1 , 2 was able to compete with itself in all groups ( Fig . 3D ) . Furthermore , we observed an identical reactivity pattern with native membrane-anchored EBOV GP1 , 2 using a cell surface competition ELISA ( Supplemental Fig . S1 ) . We further examined the ability of the two GP isoforms to compete with each other for antibodies by performing competition immunoprecipitation . Purified GP1 , 2 in the presence of sGP at varying molar ratios was immunoprecipitated with antiserum from GP1 , 2-immunized or sGP-immunized mice , and analyzed by Western blot using a polyclonal rabbit antibody that reacts with both GP isoforms . Antiserum from GP1 , 2-immunized mice precipitated both GP1 , 2 and sGP , and increasing concentrations of sGP did not attenuate the amount of GP1 , 2 signal ( Fig . 3E ) , suggesting the presence of two separate populations of antibodies that do not cross-react between GP1 , 2 and sGP . However , while antiserum from sGP-immunized mice also precipitated both GP1 , 2 and sGP , increasing concentrations of sGP significantly attenuated the amount of GP1 , 2 precipitated ( Fig . 3F ) , indicating that GP1 , 2-reactive antibodies in these mice are cross-reactive with sGP . As a control , addition of recombinant HA had no effect on the amount of GP1 , 2 precipitated by either antiserum group . Taken together , these data suggest that anti-GP1 , 2 antibodies induced by GP1 , 2 are directed primarily against epitopes not shared between GP1 , 2 and sGP , whereas such antibodies induced by sGP are directed against epitopes shared between GP1 , 2 and sGP . We further investigated whether there was a difference in the ability of antisera from the immunization groups to neutralize EBOV GP1 , 2-mediated virus infection , and whether sGP could interfere with antibody-mediated neutralization . Pseudoviruses were generated using an Env-deficient HIV backbone pseudotyped with Zaire EBOV GP1 , 2 . In order to achieve consistent neutralization , we pooled sera from the four highest responders among GP1 , 2-immunized animals and among sGP-immunized animals . Antisera from both groups were able to effectively neutralize pseudoviruses as measured by a luciferase reporter assay ( Fig . 4A ) , although antisera from GP1 , 2-immunized mice exhibited more potent neutralizing activity than antisera from sGP-immunized mice , probably due to higher overall anti-GP1 , 2 titer . To determine if sGP interferes with neutralization , we used an antiserum dilution corresponding to 80% neutralizing activity in each group and preincubated antisera with different amounts of sGP . Consistent with the competition ELISA results , sGP was able to completely attenuate neutralizing activity of antisera from sGP-immunized mice , while it had no effect on neutralizing activity of antisera from GP1 , 2-immunized mice ( Fig . 4B ) . Purified influenza HA was used as a control and had no effect on neutralizing activity of either antiserum group . Similar results were observed when we used an antiserum dilution corresponding to 50% neutralizing activity ( Supplemental Fig . S2 ) . These data confirm that sGP can compete with GP1 , 2 for anti-GP1 , 2 antibodies and interfere with antibody-mediated neutralization , but can only do so in animals that have been exposed to sGP . The inability of sGP to compete with GP1 , 2 for antibodies from GP1 , 2-immunized mice was intriguing considering that GP1 , 2 shares almost half of its ectodomain sequence with sGP . We reasoned that some of these antibodies may be directed solely against GP1 , 2 epitopes not shared with sGP , while other antibodies may be directed against shared epitopes , but preferentially bind GP1 , 2 because of conformational differences between the two GP isoforms resulting from tertiary and quarternary structure and steric shielding . To address this possibility , we used quantitiative ELISA to determine the relative titers and estimate the average affinity of antibodies from GP1 , 2 and sGP-immunized animals for GP1 , 2 and sGP . We individually examined purified polyclonal IgG from the five highest responders in GP1 , 2-immunized and sGP-immunized groups , and calculated the apparent dissociation constant ( Kd ) of anti-GP1 , 2 and anti-sGP antibodies . This apparent Kd was calculated by Scatchard analysis as described elsewhere [33] , [34] and represents an estimate of the average affinity of anti-GP antibodies , with lower apparent Kd correponding to higher average affinity . Consistent with above ELISA data ( Fig . 2D ) , mice immunized against GP1 , 2 had higher titers of anti-GP1 , 2 antibodies than anti-sGP antibodies ( Fig . 5A ) . However , there was no measurable difference in the apparent Kd's of GP1 , 2-binding vs . sGP-binding antibodies ( Fig . 5B ) , indicating that preferential binding of antibodies from these animals to GP1 , 2 is not due to affinity differences for different GP isoforms . In mice immunized against sGP we again observed very high titers of anti-sGP antibodies , and very low levels of anti-GP1 , 2 antibodies . However , those antibodies that did bind to GP1 , 2 appeared to have modestly lower Kd ( higher average affinity ) than did sGP-binding antibodies ( Fig . 5B ) . Future studies with monoclonal antibodies directed against epitopes shared between sGP and GP1 , 2 will provide further information on whether specific antibodies bind to the two GP isoforms with different affinities . Nonetheless , the present data provide evidence that differences in affinity are not responsible for antibodies from GP1 , 2 and sGP-immunized mice reacting preferentially with different GP isoforms . The secretion of surface glycoproteins as a mechanism of absorbing antiviral antibodies has been hypothesized before for several viruses including vesicular stomatitis virus ( soluble G ) and respiratory syncytial virus ( secreted G ) [35] , [36] . It has been demonstrated that RSV secreted G can absorb anti-G antibodies and interfere with both neutralization and antibody-dependent cell-mediated virus clearance . However , we observed that EBOV sGP can only compete for anti-GP1 , 2 antibodies in mice immunized against sGP . This led us to hypothesize that sGP may serve a role in altering the repertoire of epitopes against which the host immune response is directed , in order to divert the host immune response towards epitopes shared between sGP and GP1 , 2 . To test this hypothesis , we vaccinated mice with a 3∶1 ratio of sGPEdit∶GP1 , 2Edit ( Fig . 6A ) to simulate antigen expression during EBOV infection . Control groups were immunized with either sGPEdit or GP1 , 2Edit plus empty pCAGGS vector to keep the total amount of DNA constant . As a proxy for in vivo antigen expression , HeLa cells were transfected with corresponding ratios of sGPEdit , GP1 , 2Edit , and pCAGGS . As measured by Western blot analysis , the levels of sGP and GP1 , 2 expression in both lysate and culture supernatant of cells co-transfected with sGPEdit and GP1 , 2Edit were similar to cells transfected with sGPEdit or GP1 , 2Edit alone ( Fig . S3 ) . All immunization groups generated similar titers of anti-GP1 , 2 antibodies ( Fig . 6B ) . However , when we performed a competition ELISA using antisera from sGPEdit+ GP1 , 2Edit-immunized mice , sGP was able to compete with GP1 , 2 for over 50% of the anti-GP1 , 2 antibodies ( Fig . 6C ) . Mice immunized with GP1 , 2Edit+vector or sGPEdit+vector displayed the same serum reactivity patterns we had observed previously in mice immunized against only one of the GP isoforms . Further , after boosting mice a second time , almost 70% of GP1 , 2-antibodies in week 12 antisera from sGPEdit+ GP1 , 2Edit-immunized mice were absorbed by sGP . Interestingly , in mice immunized with lower ratios of sGPEdit∶GP1 , 2Edit , significant sGP cross-reactivity was also observed , with almost 70% of anti-GP1 , 2 antibodies being susceptible to competition in mice immunized with a 1∶1 ratio of sGP∶GP1 , 2 , and about 25% being susceptible to competition in mice immunized with a 1∶3 ratio of sGP∶GP1 , 2 ( Figure S4 ) . Similar results were also obtained with a competition immunoprecipitation assay . As shown in Fig . 6D , antiserum from sGPEdit+GP1 , 2Edit-immunized mice was able to precipitate both GP1 , 2 and sGP , but increasing concentrations of sGP attenuated the amount of GP1 , 2 precipitated . Furthermore , while sGPEdit+GP1 , 2Edit antiserum was able to effectively neutralize pseudovirus infectivity ( Fig . 6E ) , the addition of exogenous sGP almost completely inhibited pseudovirus neutralization ( Fig . 6F ) , indicating that sGP can effectively interfere with antibody mediated neutralization in these mice . Similar observations were also made at an antiserum concentration corresponding to 50% neutralization ( Fig . S5 ) . Taken together , these data confirm that sGP can direct the host antibody response to focus on epitopes shared between GP1 , 2 and sGP , thereby allowing sGP to compete for antibodies and interfere with antibody-mediated virus neutralization . Furthermore , the observation that sGP can compete for a greater proportion of GP1 , 2 antibodies from week 12 antisera compared to week 6 suggests that iterative exposure to sGP gradually drives the host to a dominantly sGP-reactive response . In order to test the hypothesis that expression of sGP can modulate the GP1 , 2-specific antibody response , we primed and boosted mice with either sGPEdit or GP1 , 2Edit , and then boosted again at week 10 with the opposite GP isoform ( Fig . 7A ) . Control groups were boosted with the same GP isoform . As shown in Fig . 7B , anti-GP1 , 2 antibodies were induced in all groups at week 12 . However , in mice immunized with GP1 , 2Edit and then boosted with sGPEdit , sGP was able to efficiently compete for anti-GP1 , 2 antibodies in competition ELISA ( Fig . 7C ) . Furthermore , sGP was also able to efficiently compete for anti-GP1 , 2 antibodies from mice primed against sGPEdit and boosted with GP1 , 2Edit . We next investigated whether sGP is able interfere with virus neutralization by sera from cross primed and boosted mice . As shown in Fig . 7D , sGP was able to interfere with neutralization only from animals primed against sGP and boosted with GP1 , 2 . On the other hand , antisera from animals primed against GP1 , 2 and boosted with sGP maintained their neutralizing activity in the presence of sGP . To further probe this observation , we compared the antisera titers corresponding to 50% neutralizing activity ( NT50 ) in groups before ( week 6 ) and after ( week 12 ) boosting with the opposite GP isoform . As shown in Fig . 7E , neutralizing activity is not boosted by immunization with the opposite GP isoform . Thus , it appears not only that sGP can overwhelm the GP1 , 2-specific response , but also that it only boosts non-neutralizing antibodies induced by GP1 , 2 . The observation that sGP can alter the reactivity profile of the anti-GP1 , 2 response has important implications for EBOV vaccinology , since during a infection , sGP could subvert the immune response of a previously vaccinated individual if the virus is not cleared rapidly .
The role of sGP in EBOV host immune evasion has not been clearly defined . In this study , we analyzed antibody responses in mice immunized against sGP , GP1 , 2 , or both GP isoforms and present evidence that sGP serves to redirect the immune response towards epitopes that are either not present or inaccessible in GP1 , 2 , or epitopes that are shared between the two GP isoforms , thereby allowing sGP to effectively absorb anti-GP1 , 2 antibodies . We term this phenomenon “antigenic subversion” , because it is distinct from previously proposed mechanisms in which sGP passively absorbs anti-glycoprotein antibodies . In antigenic subversion , the ability of sGP to absorb anti-GP1 , 2 antibodies is critically dependent on exposure to sGP during induction of the anti-GP1 , 2 immune response . In mice immunized against GP1 , 2 in the presence of sGP , an immunization strategy designed to simulate antigen exposure during natural infection , we observed that most resulting anti-GP1 , 2 antibodies were cross reactive with and thus susceptible to competition by sGP , even though the titers of anti-GP1 , 2 antibodies in these mice were similar to the titers in mice immunized against GP1 , 2 alone . On the other hand , in mice immunized against GP1 , 2 alone , we observed only low cross-reactivity of anti-GP1 , 2 antibodies with sGP , a finding consistent with previous studies , indicating that antibodies in these mice are largely directed against epitopes not shared with sGP [23] , [24] . The model we propose for the mechanism of antigenic subversion by sGP assumes that before immunization , the host begins with a repertoire of naïve B-cells that recognize epitopes distributed throughout GP1 , 2 and sGP ( Fig . 8A ) . However , because sGP is generated in much higher quantities than GP1 , 2 , B-cells that recognize sGP epitopes and epitopes shared between sGP and GP1 , 2 are more likely to encounter their cognate antigens as compared with B-cells that recognize GP1 , 2-specific epitopes . Furthermore , as the sGP-reactive B-cell population expands , it will outcompete other B-cells for antigen and survival signals . Thus , the humoral response is skewed towards sGP , and epitopes of GP1 , 2 that are shared with sGP . Antigenic subversion represents a novel viral escape strategy that has some similarities to original antigenic sin ( OAS ) . In classical OAS , initial exposure to a pathogen results in a population of memory B-cells that recognize antigens specific to that pathogen strain . Upon subsequent exposure to a different strain of the same pathogen , cross-reactive memory B-cells will respond preferentially , producing antibodies with high affinity to the initial pathogen which may not bind to the new strain as effectively [37] , [38] . Furthermore , these memory B-cells can compete for antigen and survival signals with naïve B-cells that might otherwise produce higher affinity or more protective antibodies to the new strain . Similarly , overexpression by Ebola virus of sGP ensures that sGP-reactive B-cells preferentially expand and outcompete GP1 , 2-specific B-cells for antigen and survival signals , resulting in a suboptimal host response that is directed away from membrane-bound GP1 , 2 on the virion surface . However , unlike classical OAS , this process does not require temporal separation of antigen encounters , but can also occur during simultaneous exposure to two partly identical antigens . Our model for antigenic subversion can also explain how anti-GP1 , 2 antibodies from animals primed against sGP and then boosted with GP1 , 2 maintain cross-reactivity with sGP . In these animals , priming with sGP elicits antibodies against sGP epitopes , some of which are shared with GP1 , 2 ( Fig . 8B ) . When these animals are boosted with GP1 , 2 , memory B-cells that recognize shared epitopes vastly outnumber ( and express higher affinity receptors than ) the naïve B-cells that recognize unshared epitopes . Thus , the anti-sGP memory B-cells will be preferentially activated and expanded , boosting the anti-sGP response . This situation is analogous to one in which previously-infected individuals are vaccinated against GP1 , 2 , and raises the possibility that immunizing such individuals may simply boost an already unprotective antibody response . While filovirus infection is rare , our findings suggest that it may be necessary to devise alternate strategies for immunizing previously-infected individuals in a way that specifically boosts the anti-GP1 , 2 response and avoids subversion . Perhaps the most striking finding in this study is that boosting GP1 , 2-immunized mice with sGP could effectively subvert the anti-GP1 , 2 response and render it susceptible to competition by sGP . We hypothesize that while the majority of B-cells activated in mice immunized against GP1 , 2 are directed against epitopes not shared with sGP ( Fig . 8C ) , there is a small population of activated B-cells that react with sGP . This is supported by our observation that even though sGP cannot measurably compete in ELISA and immunoprecipitation for anti-GP1 , 2 antibodies from GP1 , 2-immunized mice , these mice still develop low titers of sGP-binding antibodies . When GP1 , 2-immunized mice are boosted with sGP , these sGP-reactive B-cells expand while the remaining GP1 , 2-specific B-cells that recognize unshared epitopes do not , shifting the anti-GP1 , 2 antibody response from mostly GP1 , 2-specific to mostly sGP-cross reactive . Furthermore , it is notable that neutralizing activity actually decreased after boosting with sGP , despite an increase in overall anti-GP1 , 2 antibodies . Thus , boosting with sGP only augmented non-neutalizing anti-GP1 , 2 antibodies that are highly susceptible to sGP competition , while the existing neutralizing antibodies previously induced by GP1 , 2 in these mice maintained resistence to sGP interference . This situation is analogous to one in which an individual is immunized against GP1 , 2 is subsequently infected with EBOV . If the individual is unable to rapidly clear the virus , the virus may replicate sufficiently to subvert the host immune response . Thus , it will be critical for vaccines to induce high enough titers of anti-GP1 , 2 antibodies to ensure that the virus is cleared before it is able to effect subversion ( Fig . 8D ) . The inability of sGP to compete for anti-GP1 , 2 antibodies from GP1 , 2-immunized mice is consistent with a growing body of evidence pointing to the immunodominance of the GP1 , 2 mucin domain , a highly glycosylated region of GP1 not shared with sGP [24] , [25] . This domain is thought to form a sterically bulky “cloak” that shields the putative receptor binding domain from host antibodies , as suggested for the HIV Env “glycan shield” [39] . The role that the mucin domain plays in host-pathogen interaction is complex and previous studies indicate that this region contains both neutralizing and infection-enhancing epitopes , and can mask epitopes on GP1 , 2 itself by steric occlusion [40] , [41] . Furthermore , the mucin domain is the most divergent region of GP1 , 2 among EBOV strains , and is dispensible for GP1 , 2 mediated virus attachment and membrane fusion [42]–[44] , strongly suggesting a role in protecting more functionally conserved regions of GP1 , 2 from immune attack . Because the linear sequence of sGP corresponds to the putative mucin-shielded receptor binding domain ( RBD ) of GP1 , it is possible that sGP works together with the mucin domain so that host antibodies are directed either to shared epitopes that are sterically shielded in the GP1 , 2 trimer , or to the mucin domain itself , which is cleaved off in the host cell acidified endosome along with any bound antibodies [45] , [46] . The possibility that GP1 , 2 epitopes shared with sGP may be shielded in the GP1 , 2 trimer is supported by our observation that very few anti-sGP antibodies in sGP-immunized mice cross-react with GP1 , 2 despite the fact that sGP shares over 90% of its linear sequence with GP1 , 2 . Furthermore , antigenic subversion allows sGP to efficiently absorb those antibodies that do recognize unshielded and shared epitopes in GP1 , 2 . The importance of sGP-mediated antigenic subversion to EHF pathogenesis remains to be elucidated . Passive immunization studies with polyclonal sera or monoclonal antibodies will reveal whether sGP-crossreactive antibodies are in fact less protective than GP1 , 2-specific antibodies . This is particularly important given that passive transfer of anti-EBOV monoclonal antibodies has gained traction recently as a post-exposure therapeutic . If sGP cross-reactivity turns out to be correlated with impaired virus clearance , it would underscore the need to elicit and produce GP1 , 2-specific antisera or monoclonal antibodies for achieving more effective treatment of EBOV infection . Moreover , our findings also suggest that EBOV vaccines should be tailored to target regions not shared between sGP and GP1 , 2 . This is particularly relevant to recent efforts to develop a broadly-protective vaccine , since these studies have centered around focusing vaccines on conserved epitopes by deleting highly variable regions of GP1 , 2 such as the mucin domain [24] , [43] , [47] . Because sGP actually corresponds to the most highly conserved region of GP1 , antibodies elicited by these constructs may be cross-reactive with sGP and therefore susceptible to sGP-mediated subversion . Candidate pan-filovirus vaccines may need to be focused on regions of GP1 , 2 that are both highly conserved and unshared with sGP , such as the membrane-proximal GP2 subunit . It will also be of great interest for EBOV vaccinology to determine whether antigenic subversion correlates with successes and failures of vaccines to protect animals against lethal challenge . It may be critical for an EBOV vaccine to elicit a long lasting immune response with high enough antibody titers so the host can clear the virus before it is able to replicate and effect antigenic subversion . This possibility is consistent with nonhuman primate lethal challenge experiments , in which survival was most closely correlated with maintenance of anti-GP1 , 2 antibody titers above a threshold level , while lower antibody titers only delayed the time to death [48] . Further , while much of EBOV vaccinology has focused on eliciting protective antibodies against the membrane-bound glycoprotein , a robust T-cell response may also improve vaccine efficacy . Immunization of nonhuman primates with a low dose of GP and nucleoprotein ( NP ) -expressing recombinant adenoviruses was demonstrated to elicit robust antibody and T-cell responses and confer protection against lethal challenge [49] . More importantly , EBOV-specific T-cells were shown to reduce the threshold of anti-GP1 , 2 antibodies needed for protection . Recombinant vectors expressing CTL epitopes have been demonstrated to confer protection to lethal EBOV challenge in mice , and GP-specific as well as nucleoprotein ( NP ) -specific CD8 T-cells can control infection even when adoptively transferred to otherwise naïve animals [50] , [51] . These studies suggest that a robust T-cell response may reduce the threshold of antibodies needed for rapid virus clearance . It is noteworthy that although the expression of sGP is conserved in Ebola viruses , sGP is not produced by Marburg virus ( MARV ) , another member of the filoviridae . There are other instances where related viruses often diverge in the mechanisms they employ to survive in their respective hosts . For example , Sendai virus ( SeV ) , a paramyxovirus that causes severe respiratory tract infections in rodents , expresses a V protein via RNA editing of the P gene . V is necessary for in vivo survival and pathogenesis of SeV , though V-deficient SeV show no defect in replication in vitro [52] . However , the closely related human parainfluenza virus type 1 ( HPIV-1 ) does not express V , even though its P gene displays a high degree of homology to SeV P , and HPIV-1 causes similar disease in humans as SeV causes in rodents [53] . Similarly , while secretion of GP has not been observed in MARV , it has likely evolved alternative strategies to survive within its host . While the precise relevance of antigenic subversion to Ebola vaccinology remains to be determined , antigenic subversion represents a novel and elegant solution to the challenge that viruses face of balancing the ability to infect host cells efficiently while evading host immune surveillance . The constraints of a very small genome neccessitate packing a great deal of functionality into a small space , and sGP-mediated subversion represents a mechanism which , along with glycan-dependent steric shielding , and immunodominance of the GP1 , 2 mucin domain , may help EBOV to survive in its host . Improving our understanding of how these mechanisms work together will eventually open the door to a more rationally designed vaccine . A vaccine directed against highly conserved regions of GP1 , 2 , such as the GP2 subunit , could induce broadly reactive antibodies while also avoiding the potential for sGP-mediated immune subversion . Such a vaccine could protect against multiple strains of EBOV , including strains that have not yet been identified .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Animal ethics approval for the immunization studies in mice was obtained from the Institutional Animal Care and Use Committee ( IACUC ) at Emory University . All animal studies were performed under approval from the Institutional Animal Care and Use Committee ( IACUC ) at Emory University . Female BALB/c mice ( 8-week old ) were purchased from the Jackson Laboratory and housed in the animal facility at the Emory University . 293T cells and HeLa cells were maintained in Dulbecco's Modified Eagle's Medium ( DMEM , Mediatech ) supplemented with 10% fetal bovine serum ( Hyclone , ThermoFisher ) and penicillin/streptomycin . All Ebola glycoprotein constructs were based on the Ebola Zaire strain ( ZEBOV ) , Mayinga Subtype ( GenBank accession# U23187 . 1 ) . Editing site mutants were generated in pBlueScript II K/S+ vector through site-directed mutagenesis using the QuickChange XL kit ( Stratagene ) . Constructs were then subcloned pCAGGS mammalian expression vector . Protein expression was carried out by transfecting 90% confluent cells in 6-well plates with 5 µg DNA+12 µL Fugene HD ( Roche ) per well , as per manufacturer instructions , and detected at 48 h post transfection . Surface expression was detected by surface biotinylation followed by immunoprecipitation with anti-EBOV GP mouse polyclonal antibody , SDS-PAGE , and Avidin-HRP blotting . Cell lysate was harvested in cell lysis buffer and cell culture supernatant was collected , spun down to remove cell debris , and concentrated 10× by a centrifugal concentrator . Cell lysate and concentrated cell culture supernatant were run on SDS-PAGE under denaturing conditions , followed by probing with anti-EBOV GP1 , 2/sGP rabbit polyclonal antibody . Mutant ZEBOV GP plasmids for DNA immunization experiments were prepared using the EndoFree Plasmid Mega Kit ( Qiagen ) as per manufacturer instructions and redissolved in pure endotoxin-free water at a concentration of 4–6 µg/µL , and purity was verified by restriction analysis and spectrophotometry . For immunization , DNA was diluted in sterile PBS to 0 . 5 µg/µL and filter sterilized . Female BALB/C mice ( Charles River Laboratory ) at six mice per group received 50 µg of DNA intramuscularly ( 25 µg/leg ) per immunization . Anesthetized mice were bled retro-orbitally two weeks after each immunization and serum samples were stored at −80°C until use . Production of purified histidine-tagged HA has been described previously [54] . Soluble histidine-tagged GP1 , 2 and sGP were generated by C-terminal addition of a single 6× histidine tag . Soluble GP1 , 2 was generated by truncation of the transmembrane domain and cytoplasmic tail . Recombinant vaccinia viruses ( rVV ) were generated as described elsewhere to synthesize soluble His-tagged GP1 , 2 ( His- GP1 , 2 ) and sGP ( His-sGP ) , as well as membrane-bound GP1 , 2 [55] . For production and purification of His-GP1 , 2 and His-sGP , rVV-infected cell supernatant was clarified and purified using a PrepEase His Purification Kit ( Affymetrix ) and purity of recombinant protein was verified by SDS-PAGE followed by Western blot or coomassie stain . Further , purified His-GP1 , 2 and His-sGP were tested for reactivity to pre-immune sera or sera from unvaccinated mice by ELISA and Western blot , and they were found to be unreactive . For ELISA , flat-bottom Immulon 4-HBX 96-well plates ( Thermo ) were coated overnight with 0 . 1 µg/well of His- GP1 , 2 or His-sGP . A standard curve was generated by coating control wells with known concentrations of mouse IgG . Plates were washed 5× in PBS+Tween ( PBST ) , blocked in PBST+2%BSA , and then incubated in duplicate for two hours with antisera diluted in PBST+2%BSA . Plates were washed again , and incubated with 1∶1000 ( pooled anti- IgG subtype ) HRP-conjugated goat anti-mouse secondary antibody . After final wash , plates were developed with 3 , 3′ , 5 , 5′-Tetramethylbenzidine ( TMB , Thermo ) and stopped at 5 minutes with 0 . 2 M HCl . Plates were read and antibody concentration was calculated using the standard curve . Competition ELISA was performed by modifying the above protocol . Plates were coated with His- GP1 , 2 . Pooled antisera were diluted in PBST+2%BSA to a concentration corresponding to an OD of 1 . 0 by anti- GP1 , 2 ELISA . Diluted antisera were then mixed with decreasing concentrations of purified His-sGP or His- GP1 , 2 and immediately added to His- GP1 , 2-coated wells . The ELISA was then developed as described above and competition was calculated as percent of signal compared to no competing antigen . Competition immunoprecipitation was performed by incubating pooled antisera ( normalized for anti-GP1 , 2 titer as determined by ELISA ) with 200 ng of purified His- GP1 , 2 and increasing amounts purified His-sGP at molar ratios of 0 . 25∶1 , 1∶1 , 4∶1 , and 8∶1 sGP∶GP1 , 2 . Antisera incubated with His-sGP alone , His-GP1 , 2 alone , or with no GP were used as controls , as well as antisera incubated with GP1 , 2 in the presence of recombinant influenza HA . Samples were incubated on ice for 20 minutes , followed by addition of protein-G coupled agarose beads ( Thermo Scientific ) to further incubate at 4°C for an additional two hr with agitation . Samples were then centrifuged and washed three times with with lysis buffer , and then mixed with 6× Laemmli SDS sample buffer with 12% β-mercaptoethanol . The samples were heated at 95°C for 5 minutes and then used for SDS-PAGE followed by Western blot analysis using antibodies gainst both sGP and GP1 , 2 . Apparent affinity of polyclonal antisera was determined by quantitative ELISA using purified IgG from immunized animals . IgG was purified using Melon Gel ( Thermo ) as per manufacturer instructions and purity of IgG was verified by ELISA and coomassie gel staining . Since quantitative affinity ELISA requires that coating antigen be incubated with increasing dilutions of antibodies until coating antigen becomes saturated , we found that high antibody concentrations can result in signals that exceed the plate reader's range of detection . Thus , we titrated the amount of coating antigen down to 0 . 05 µg/well to avoid signal saturation . Wells were coated overnight with 0 . 05 µg of purified His-GP1 , 2 or His-sGP and after washing and blocking were incubated with purified IgG diluted in PBST+2%BSA , at dilutions ranging from 1∶10 to 1∶1280 ( based on original serum volume ) . ELISAs were developed as described above and the signal converted to nM concentration of IgG by comparison to a standard curve . Apparent Kd's of polyclonal sera were calculated by nonlinear regression analysis using GraphPad Prism . These results were verified manually by analysis of linearized binding curves as detailed elsewhere [33] . EBOV-GP pseudotyped HIV was generated as described elsewhere [56] . Briefly , 293T-cells were cotransfected with Env-defective HIV backbone and ZEBOV GP in pCAGGS vector using Fugene HD ( Roche ) . Supernatants were harvested 48 h post-transfection , clarified , and filtered using a 0 . 45 micron filter . Pseudoviruses were titered by infecting JC53 cells [57] , which express β-galactosidase and luciferase under a tat-activated promoter , causing infected cells turning blue with X-Gal staining . Neutralization assays were performed as described elsewhere [56] with minor modifications . Briefly , pseudoviruses were pre-incubated with dilutions of heat-inactivated antisera , and supplemented with heat-inactivated naïve mouse sera ( Innovative Research ) so that 5% of the total volume was mouse serum . Pseudovirus-antiserum mixtures were then added to 30% confluent JC53 cells and incubated for 48 h . Virus infection and neutralization was measured by luciferase reporter assay , and neutralization was measured by decrease in luciferase expression compared to virus-only controls [57] . We performed a competition neutralization assay by selecting a fixed antisera concentration corresponding to either 50% or 80% neutralizing activity . Diluted antisera were incubated with dilutions of purified His-sGP or with soluble influenza PR8 hemagluttinin ( HA ) as a control ( GenBank Accession# JF690260 ) . Antisera mixtures were then mixed with pseudovirus and the neutralization assay was developed as described above . Interference with neutralization was determined by the percent rescue of infectivity compared to wells with pseudovirus+antisera without competing sGP , as calculated by the formula [ ( virus+antibody+sGP ) − ( virus+antibody ) ]/[ ( virus alone ) − ( virus+antibody ) ]×100 . | The function of the Ebola virus ( EBOV ) secreted glycoprotein ( sGP ) has been long debated , and the fact that sGP production is conserved among all known EBOV species strongly indicates an important role in the viral life cycle . Furthermore , the recent finding that EBOV mutates to a predominantly non-sGP-forming phenotype in cell culture , while the mutant virus reverts to an sGP-forming phenotype in vivo , suggests that sGP is critical for EBOV to survive in its infected host . Here we demonstrate that sGP can function to absorb anti-GP antibodies . More importantly , instead of simply passively absorbing host antibodies , sGP actively subverts the host immune response to induce cross-reactivity with epitopes it shares with membrane-bound GP1 , 2 . Immune subversion by sGP represents a distinct mechanism from the use of secreted antigens as antibody decoys , an immune evasion tactic previously proposed for other viruses , and should be an important consideration for future EBOV vaccine design efforts since vaccines may need to be specifically tailored to avoid subversion . | [
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"virolog... | 2012 | Antigenic Subversion: A Novel Mechanism of Host Immune Evasion by Ebola Virus |
The calpains are physiologically important Ca2+-activated regulatory proteases , which are divided into typical or atypical sub-families based on constituent domains . Both sub-families are present in mammals , but our understanding of calpain function is based primarily on typical sub-family members . Here , we take advantage of the model organism Caenorhabditis elegans , which expresses only atypical calpains , to extend our knowledge of the phylogenetic evolution and function of calpains . We provide evidence that a typical human calpain protein with a penta EF hand , detected using custom profile hidden Markov models , is conserved in ancient metazoans and a divergent clade . These analyses also provide evidence for the lineage-specific loss of typical calpain genes in C . elegans and Ciona , and they reveal that many calpain-like genes lack an intact catalytic triad . Given the association between the dysregulation of typical calpains and human degenerative pathologies , we explored the phenotypes , expression profiles , and consequences of inappropriate reduction or activation of C . elegans atypical calpains . These studies show that the atypical calpain gene , clp-1 , contributes to muscle degeneration and reveal that clp-1 activity is sensitive to genetic manipulation of [Ca2+]i . We show that CLP-1 localizes to sarcomeric sub-structures , but is excluded from dense bodies ( Z-disks ) . We find that the muscle degeneration observed in a C . elegans model of dystrophin-based muscular dystrophy can be suppressed by clp-1 inactivation and that nemadipine-A inhibition of the EGL-19 calcium channel reveals that Ca2+ dysfunction underlies the C . elegans MyoD model of myopathy . Taken together , our analyses highlight the roles of calcium dysregulation and CLP-1 in muscle myopathies and suggest that the atypical calpains could retain conserved roles in myofilament turnover .
Calpains are Ca2+-regulated neutral thiol proteases that perform a limited digestion of target substrates , and are thus considered to be regulatory , as opposed to strictly degradative [1] . Members of the calpain family are variously composed of discrete modular domains , numbered DI–DVI ( Figure S1 ) [2] , [3]; DI–DIV are domains associated with the large calpain catalytic subunit and DV–DVI are domains found in a common small regulatory subunit , named CAPNS1 ( not shown ) [4] , [5] . All calpain large subunits share a signature domain DII , which contains the core catalytic triad of cysteine , histidine and asparagine [6] , [7] . DII is further divided into subdomains , IIa and IIb , which change conformation to align the catalytic cleft upon binding of Ca2+ [8] . DIII has a C2-like domain , which is present in almost all calpains , whereas DI is usually composed of a short non-conserved sequence that can undergo autolysis [6] , [7] . As discussed below , calpains differ in their domain architecture; however , a key feature that distinguishes the typical calpains from the atypical calpains is the presence of DIV . The most extensively studied calpains are the typical CAPN1 and CAPN2 , which have a classic DIV composed of five EF hand motifs , also referred to as a penta EF hand ( PEF ) [4] , [5] . A PEF domain is also present in DVI of CAPNS1 , which heterodimerizes with CAPN1 and CAPN2 through DIV [4] , [5] , although not all typical calpains require CAPNS1 for activity [9]–[11] . Mammalian genomes also encode the endogenous inhibitor calpastatin , which is specific for typical calpains [12] , [13] . By contrast to typical calpains , there is no evidence indicating that atypical calpains lacking DIV and EF hand motifs form heterodimers . This absence of EF hands was also responsible for the initial belief that atypical calpains were insensitive to Ca2+ regulation [1] . Both typical and atypical calpains can also carry a variety of alternative domains , including an additional C2-like domain , zinc fingers , glycine rich regions and microtubule interacting and transport ( MIT ) domains ( Figure S1 ) . Genes encoding predicted calpain proteins have been identified in many organisms ranging from single-celled yeasts to higher vertebrates through the sequencing of whole genomes . Mammalian genomes encode nine typical and five atypical calpains [1] , [14] , [15] . The Drosophila genome encodes only three typical and one atypical calpain ( CALPD ) , also known as small optic lobes ( SOL ) [16] . By contrast , the genome of the nematode C . elegans encodes multiple atypical calpains , but no typical calpains . Given the paucity of typical calpains in non-mammals , phylogenetic arguments have been presented to suggest that the EF hand motifs of typical calpains were late evolutionary additions [14] . Homologs of CAPNS1 and calpastatin , proteins that interact with typical calpain subunits , are also absent in C . elegans and Drosophila [16] , [17] . Mammalian calpains participate in many cellular processes , including , but not limited to , cytoskeletal remodelling [18] , cell mobility [19] , myofibril maintenance [20] , signal transduction [21] , cell cycle progression [22] , regulation of gene expression [15] , apoptosis [23] and long term potentiation [24] . Genetic association studies have implicated calpains in disease pathologies including limb-girdle muscular dystrophy type 2A ( LGMD2A , CAPN3 ) [25] , susceptibility to type II diabetes ( CAPN10 ) [26] and gastric cancer ( CAPN8/9 ) [27] , [28] . Despite their involvement in cell maintenance and disease , it has been difficult to predict the targets of calpain proteolysis with any precision , because substrate specificity is only weakly determined by primary sequence [29] , [30] . In addition , a large number of in vitro substrates have been identified , but many await in vivo validation . Chemical calpain inhibitors have also been developed , including active site peptidomimetics and domain IV and VI non-peptidyl inhibitors [31] . Unfortunately , active site inhibitors are generally not specific for calpains , as other cysteine proteases , such as members of the cysteine cathepsin family , are also subject to inhibition [32]; in addition , DIV and DVI inhibitors would fail to inhibit atypical calpains . By contrast to the many studies focused on the activity of the typical calpains , it is clear that the specific involvement of atypical calpains in development and disease pathologies remains underexplored . We are using C . elegans as a model for exploring the function of atypical calpains . Previous work in C . elegans has highlighted the importance of the tra-3/clp-5 calpain gene in sex determination [33] , [34]; other studies have shown that the C . elegans clp-1 and tra-3/clp-5 genes are involved in neurodegeneration and necrosis of a subset of vulval cells and in the intestine [35]–[37] . In this paper , we have examined the evolution of atypical and typical calpains in metazoa and analyzed the effects of reducing or enhancing the activities of atypical calpains on C . elegans development . To gain information about the potential site of action of a subset of atypical calpain genes , we have examined their in vivo expression patterns . Given the association between calpain activity and degenerative pathologies , we have investigated factors that influence the activity of clp-1 in a C . elegans model of muscular dystrophy and reveal the importance of both sustained calpain expression and intracellular Ca2+ levels ( [Ca2+]i ) . Taken together , we hypothesize that the muscle degenerative phenotype caused by ectopic clp-1 stems from a conserved physiological role for calpain in the turnover of sarcomeric muscle proteins .
We searched the C . elegans genome sequence using the typical human CAPN1 sequence and identified 14 atypical calpain-like sequences . Seven of these genes had previously been named clp-1 to clp-7 , so we named the remaining seven genes clp-8 to clp-10 and clpr-1 to clpr-4 ( for clp-related ) for reasons explained below ( Figure S1 ) . An earlier analysis predicted the existence of 17 C . elegans calpain-like sequences [35]; however , three of these genes are not valid family members . F44F1 . 1 is now considered to be a pseudogene ( Wormbase , release WS225 ) , and both M04F3 . 4 and T21H3 . 3 lack a catalytic domain II , although they carry EF hand motifs . The domain architecture of the C . elegans atypical calpain proteins and the typical and atypical calpain proteins found in humans and Drosophila is shown in Figure S1 for comparison; a multisequence alignment is also provided to highlight the conservation of catalytic domain II and the divergence of DI and DIII among members of the calpain superfamily ( Figure S2 ) . The genome of C . briggsae , a nematode closely related to C . elegans [38] , only encodes nine predicted calpain sequences . To explain this difference , we compared the sequences of calpain proteins from C . elegans , C . briggsae , Drosophila and also human , and generated a cladogram ( Figure S3 ) . By comparison to C . briggsae , it appears that a recent gene expansion specific to C . elegans created two paralogous gene clusters . The first cluster consists of the C . elegans CLP-9 ( T11A5 . 6 ) and CLP-10 ( W05G11 . 4 ) proteins , which are homologous to C . briggsae Cbr-G19393; the proteins in this cluster are each predicted to have a SolH domain . The second cluster includes clp-8 ( F44F1 . 3 ) and four predicted clpr genes , which are missing a critical cysteine residue of the catalytic triad; together these genes are paralogous to Cbr-G04776 and Cbr-G00485 , which encode proteins with an intact catalytic triad . The existence of clpr genes raises the possibility that these predicted calpains are inactive , or that they have possibly gained novel non-proteolytic activities ( Figure S1 ) . The cladogram in Figure S3 highlights the phylogenetic relationships between the Caenorhabditis , Drosophila and human calpain proteins . Although typical calpains are absent in C . elegans , the CLP-2 , TRA-3/CLP-5 and the paralogous CLP-9 and CLP-10 proteins share extensive homology ( E-values<1e-94 ) with human CAPN7 , CAPN5/CAPN6 and CAPN15 , respectively ( Figure S3 ) . Little is known about the biological functions of these human atypical calpains; hence , an analysis of their C . elegans homologs would provide potential insights into their function . We examined the evolutionary history of the typical calpains in more depth to seek evidence that their absence in C . elegans might be due to a lineage specific loss . We restricted our analyses to genes in metazoan phyla , as an earlier study had shown that calpain sequences containing C-terminal EF hand motifs are absent in plants and fungi [14] . We were further aided by the availability of whole genome sequences , particularly those representative of more ancient phyla , such as the placozoan Trichoplax adhaerens , the cnidarians Nematostella vectensis and Hydra magnipapillata [39]–[41] , and also the sponge Amphimedon queenslandica , which diverged from early metazoans over 600 million years ago [42] . We found that homologs of typical calpains with EF-hand domains were likely to be present not only in early metazoa , but also in sponge ( Figure 1 ) . We also noted that typical calpain genes were absent from the genomes of other nematodes , including P . pacificus , B . malayi and C . remanei . Surprisingly , typical calpain genes were also absent in the tunicate C . intestinalis , a primitive branching clade of chordates [43] , further supporting the notion of lineage-specific loss of typical calpains . Genes encoding atypical calpain proteins , including those carrying SolH or PBH domains , were also found in all metazoan phyla examined and in sponge ( Figure 1 ) . When analysing the phylogeny of the typical calpains , we found that it was difficult to count EF-hand motifs in order to identify penta EF-hand ( PEF ) domains using existing models [44]; for example , PROSITE and pfam counted only four EF-hand motifs and thus failed to identify PEF domains in CAPN1 , 2 and 3 and sorcin . The number of EF hand motifs is likely to affect the ability of these proteins to dimerize , so we developed 5 separate custom profile hidden Markov models ( profile HMMs ) based on the individual EF hand motifs present within a penta EF hand domain to improve the ability to count EF hand motifs [45] ( Text S1 , see Materials and Methods ) . These profiles were tested by showing that together they detected the presence of penta EF hand domains in human CAPN1 , CAPN2 , CAPNS1 and sorcin proteins ( true positives ) , and their absence in human CAPN10 or the C . elegans atypical calpains ( true negatives ) . Application of these profiles to other typical calpain proteins showed that the calpain from Trichoplax is predicted to have a penta EF hand whereas the sponge has only four predicted motifs ( Figure 1 ) . As indicated above , many C . elegans calpain-like proteins are predicted to lack proteolytic activity because the catalytic triad has not been conserved . Such inactive calpains have also been identified in protozoa [14] . In humans , the atypical CAPN6 promotes microtubule stability despite lacking a key residue within the catalytic triad [46] . To examine the prevalence of calpain-like proteins , we selected proteins that were missing at least one catalytic residue by using Fast Statistical Alignment ( FSA ) to align 1234 proteins from the Uniprot database , which carry the signature calpain catalytic domain SAAS022684_004_001783 [47] , [48] . After removing protein fragments and splice variants from this list , a total of 344 calpain-like proteins remained ( Table S1 ) . As might be predicted , this analysis successfully identified the C . elegans , CLP-3 and CLPR-1 to CLPR-4 , Drosophila CALPC and mammalian CAPN6 proteins , which have incomplete catalytic triads . Surprisingly , putative inactive calpain proteins were conserved across the plant , animal and fungus kingdoms ( Figure S4A ) , and were also detected in protists ( Figure S4B ) . Thus , taken together , the abundance and retention of catalytically inactive calpains combined with the finding that CAPN6 is functionally active might argue that these proteins might have hitherto undiscovered functional roles . Human pathologies , such as LGMD2A , an inherited autosomal-recessive pathology caused by mutations in the typical calpain CAPN3 gene [25] , are associated with calpain dysregulation . To determine whether the atypical calpain proteases participate in physiological and degenerative processes similar to those attributed to typical calpains , we characterized C . elegans homozygous mutants carrying deletions within the calpain genes clp-1 , -4 , -6 , -7 , -8 , -9 , -10 and clpr-1; most of the deletions are predicted to disrupt the catalytic domain and to reduce relative mRNA steady-state levels by at least 70% ( Table S2 ) . In addition , we examined a clp-2 mutant carrying a Tc1 transposon inserted within an exon , and performed clp-3 ( RNAi ) [49] . Phenotypic analysis revealed that brood sizes were not significantly different from wild type for any of the calpain mutants or clp-3 ( RNAi ) treated animals , except for clp-10 ( ok2713 ) mutants in which the average brood size was reduced by approximately 50% ( 144±8; n = 4 ) without a corresponding increase in embryonic lethality . Embryonic lethality was slightly elevated in mutants carrying deletions in calpain genes , but gross developmental , mobility or morphological defects were not observed . For reasons discussed below , we also stained the clp-1 , -4 , -6 and -7 deletion mutants with phalloidin , but failed to detect disruptions to the sarcomeric structure of adult body wall muscle ( Figure S5 ) . These results indicate that most calpain genes , except for clp-10 and the previously characterized tra-3/clp-5 sex determining gene [50] , play non-essential roles in otherwise wild type animals , although we have not addressed whether these genes could be functionally redundant ( Table S2 ) . The typical CAPN1 and CAPN2 calpains are ubiquitously expressed in cells ( for review , [1] ) ; however , a temporal elevation in mouse CAPN2 mRNA levels was detected during an essential period of embryonic development [51] , [52] . Other typical calpains are capable of displaying more restricted patterns of expression; for example , transcripts corresponding to CAPN3 , the gene affected in LGMD2A muscular dystrophy , are detected only in skeletal muscle [25] . To gain insights into the potential roles of the C . elegans atypical calpain genes based on their expression patterns , we examined transgenic C . elegans carrying nuclear-localized mRFP transcriptional reporters driven from promoter regions corresponding to clp-1 to -7 . A nuclear localization signal was included to facilitate tissue-specific localization . mRFP was expressed from all of the reporters , except clp-3 and clp-6 ( Figure S6 ) ; only the clp-2 reporter showed limited expression confined to the intestine ( Figure S6 ) . These expression patterns remained unchanged over the course of larval development through to adulthood ( Figure S7 ) . To aid in the identification of tissues displaying calpain gene expression , clp ( p ) ::nls::mRFP transcriptional reporters were co-expressed in animals carrying one of five different tissue-specific GFP reporters ( for details , see Materials and Methods ) . We found that the clp-1 , -4 and -7 reporters were active in neurons and co-localized with both the pan-neural unc-119::gfp and the GABAergic unc-47::gfp reporters , whereas the tra-3/clp-5 reporter was only detected in non-GABAergic neurons ( Figure 2 and Figure S8 ) . In addition , the clp-1 and clp-4 reporters were expressed in cells of the ventral and dorsal nerve cords , whereas the tra-3/clp-5 and clp-7 reporters were only expressed in cells of the ventral nerve cord ( Figure 2 and Figure S8 ) . Given the association between human CAPN3 and LGMD2A [25] , we next co-expressed the clp reporters with the body wall muscle marker myo-3p::gfp::nls and found that only the clp-1 and clp-4 promoters were active in muscle ( Figure 3A , 3B ) . mRFP expressed from the clp-4 , tra-3/clp-5 and clp-7 gene promoters also co-localized with a pes-6::gfp reporter , a marker for the excretory cell , which serves as the renal system of the worm ( Figure 3C–3E ) . In addition , only the clp-7p::nls::mrfp transcriptional reporter co-localized with the seam cell reporter scm::gfp ( Figure 3F ) . Calpain transcriptional reporters were also detected in other tissues , including the intestine ( clp-1 , -2 , -7 and tra-3/clp-5 ) , vulva ( clp-1 , tra-3/clp-5 and clp-7 ) and hypodermis ( tra-3/clp-6 and clp-7 ) ( Figure S9 ) . The expression profiles of the clp-1 to clp-7 mRFP transcriptional reporters are summarized in Table 1 . Increased calpain activity is associated with degenerative pathologies , such as cataract , neuronal degeneration and muscular dystrophy [25] , [53]–[55] . Since a reduction in calpain activity failed to produce readily observable degenerative phenotypes in C . elegans , we next investigated the consequences of increasing calpain activity . This was first achieved by ectopically expressing full-length clp-1 , -2 , -4 , -7 and tra-3/clp-5 cDNAs under the control of the inducible hsp-16 . 41 promoter , which is activated by heat shock in almost all tissues , except the germ line [56] . At least three independent transgenic strains were generated and tested for each construct; however , despite subjecting transgenic animals to daily doses of heat-shock driven calpain expression , we failed to detect abnormal phenotypes in any of the transgenic lines ( n>1000 for each strain ) . We considered the possibility that the transient nature of heat shock induced gene expression was insufficient for the purpose of eliciting ectopic phenotypes . To test this hypothesis , the constitutively active unc-54 promoter was used to drive ectopic expression of calpain cDNAs in body wall muscle [57] . Strikingly , we observed that the unc-54p::clp-1::myc transgene crEx325 caused 1 . 5%±0 . 4% of adult animals to develop paralysis ( n>1000 ) ; similar results were obtained for two other independent transgenic lines ( data not shown ) . Affected animals displayed an uncoordinated ( Unc ) phenotype at the L4/early adult stage , which progressed to paralysis and finally to premature death as animals matured to day 2 adults ( Figure S10 ) . This effect was not observed when the other muscle-associated gene , clp-4 , was constitutively expressed from the unc-54 promoter , nor when clp-2 , -7 or tra-3/clp-5 cDNAs were similarly expressed . To understand why the hsp-16 . 41p::clp-1 transgene failed to cause paralysis , we compared the levels of CLP-1::MYC expressed from either the muscle-constitutive crEx325 [unc-54p::clp-1::myc] or the heat shock activated crEx329 [hsp-16 . 41p::clp-1::myc] transgene by western blot analysis ( Figure S11A ) . We also observed that the level of CLP-1::MYC expressed from crEx329 peaked between 4 to 12 hours post-heat shock before declining ( Figure S11B ) . By comparison , the level of CLP-1::MYC expressed from the crEx325 transgene was four-fold higher than that observed during the peak of crEx329 heat shock driven expression . These results indicate that sustained levels of elevated CLP-1 protein promote the development of paralysis . To establish that an intact catalytic triad was required for CLP-1 to cause paralysis , four independent lines were established that were predicted to express a catalytically inactive CLP-1 ( C371A ) from an unc-54p::clp-1 ( C371A ) transgene [58] , but none showed mobility defects or paralysis ( n>1200 ) . Additional lines expressing mRFP tagged CLP-1 proteins were generated to facilitate western blot analysis . In these lines , paralysis was not detected in crEx336 [unc-54p::clp-1 ( C371A ) ::mrfp] animals ( n>1200 ) , whereas paralysis developed in 1 . 9%±0 . 6% ( n>300 ) of crEx335 [unc-54p::clp-1::mrfp] animals . We also observed that the level of CLP-1::mRFP expressed from crEx336 , as measured by western blot analysis , was not reduced when compared to crEx335 , and so could not account for the difference in their activities ( Figure S11C ) . Attempts were also made to demonstrate CLP-1 proteolytic activity using casein zymography and the calpain-GLO protease™ assay ( Promega ) , which are used to measure typical calpain activity [59] . Unfortunately , neither casein nor suc-LLVY-aminoluciferin was found to be a suitable substrate for CLP-1 , although a similarly prepared recombinant rat CAPN2 was active in both assays . We next examined clp-1 activity in neurons . Previous studies have shown that RNAi inactivation of clp-1 partially suppressed the degeneration of touch receptor neurons in animals carrying gain-of-function mutations in the mec-4 or deg-1 Na+ channel subunits [35] , although degeneration was not observed when clp-1 was overexpressed in touch receptor neurons [35] . We also tested and failed to observe neurodegenerative phenotypes when either the unc-119p::clp-1 or unc-47::clp-1 reporter was expressed in other neurons . In N2 wild type animals , body wall muscle cells form striated diamond shaped bundles that are arranged into four quadrants running down the length of the animal [60] . We hypothesized that constitutively elevated expression of CLP-1 in body wall muscle cells was causing extensive myofibrillar damage , which in turn was leading to paralysis . Before examining muscle morphology , we chromosomally integrated the crEx325 [unc-54p::clp-1] array to generate crIs4 , in order to circumvent potential problems associated with mosaic expression [61] . We found that the integrated crIs4 array retained the same phenotypic characteristics as crEx325; 1 . 69±0 . 34 ( n = 352 ) of crIs4 animals displayed paralysis . We next synchronized the growth of crIs4 worms and separated crIs4 adults into three distinct classes: 1 ) phenotypically wildtype with normal sinusoidal movement 2 ) Unc and 3 ) paralyzed , and examined the integrity of body wall muscles by staining actin thin filaments with phalloidin . We found that phenotypically wildtype crIs4 animals exhibited only occasional muscle cell abnormalities ( Figure 4A , Table 2 ) , whereas Unc crIs4 animals displayed disorganized bundles of actin fibers and were also missing body wall muscle cells ( Figure 4B , Table 2 ) . These abnormalities were even more extensive in paralyzed crIs4 animals ( Figure 4C , Table 2 ) . Thus , the paralysis observed in crIs4 animals can be attributed to the loss of sarcomere integrity . Given that CLP-1 promotes the degeneration of body wall muscle , we examined the intracellular localisation of CLP-1::mRFP in qyIs43 animals co-expressing a beta-integrin subunit [pat-3::gfp] ( Figure 5 ) [62]; in muscle cells , PAT-3 is found at the base of thick filament M-lines , dense bodies ( Z-disks ) and in adhesion plaques that are formed between adjacent cells ( Figure 5A ) . We found that CLP-1::mRFP was excluded from the nucleus ( not shown ) and dense bodies , but was present at structures immediately adjacent to dense bodies ( Figure 5B ) . More specifically , CLP-1::mRFP co-localized with PAT-3::GFP at M-lines extending over the H-zone and at adhesion plaques ( Figure 5C ) . We also generated a native clp-1::gfp translational reporter , which confirmed that CLP-1::GFP displayed the same pattern of sarcomeric localization , as CLP-1::mRFP driven from the unc-54 promoter ( Figure 5D and Figure S12 ) ; CLP-1::GFP was also detected in other non-muscle tissues . Aggregates of CLP-1::mRFP were also observed , which might contribute to muscle degeneration and paralysis , although we cannot exclude the possibility that aggregation is an artifact of overexpression ( Figure 5B ) . Thus , we speculate that sarcomeric proteins enriched at the sites of CLP-1 localization are potential targets for degradation . In mammals , the absence of the large structural muscle protein dystrophin underlies the muscle degenerative disorder Duchenne muscular dystrophy ( DMD ) [63] . Similarly , a C . elegans DMD model is based on the progressive muscle degeneration observed in dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) animals [60] . In C . elegans , a null mutation in the only dystrophin-like protein gene , dys-1 ( cx18 ) , caused only occasional muscle degeneration [64] . However , inclusion of a temperature sensitive allele of the MyoD transcription factor homolog , hlh-1 ( cc561ts ) , sensitized dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) mutants to become uncoordinated ( Unc ) , but not paralyzed , and to display increased muscle degeneration [60] . We investigated whether CLP-1 contributed to the muscle degeneration associated with C . elegans DMD by constructing the dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) ; clp-1 ( tm690 ) strain . The clp-1 ( tm690 ) mutation deletes a 624 bp region of the clp-1 gene and introduces a translational frame-shift that is predicted to produce a 493 amino acid truncated protein , which lacks two of the three critical catalytic residues required for proteolytic activity . clp-1 ( tm690 ) mutants are phenotypically wild-type and do not display any obvious defects in their muscle structure ( Table 2 , Figure S5 ) . When the number of abnormal muscle cells present in dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) ; clp-1 ( tm690 ) was compared to that found in dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) animals after phalloidin staining , we found that the absence of clp-1 reduced the number of abnormal muscle cells by almost 50% ( p<0 . 001 ) . A body wall muscle cell was scored as abnormal when: 1 ) the classic striated pattern of actin filaments was disrupted 2 ) actin bundles were visible as puncta or 3 ) muscle cells were missing due to cell death . Thus , clp-1 is normally active in muscle and contributes to the muscle degeneration observed in dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) animals ( Table 2 ) . Although ectopic expression of CLP-1 led to a degradative muscle pathology , we were surprised that the penetrance of the effect was not higher . To ask what other factors might modulate clp-1 activity in muscle , we first took a genetic approach to investigate the effect of intracellular calcium [Ca2+]i . Although calpains are referred to as Ca2+-activated proteases , little is known about the effect of physiological calcium levels on the activity of atypical calpains . In C . elegans , four allelic modifiers have been identified that are understood to increase [Ca2+]i: egl-19 ( ad695gf ) , itr-1 ( sy290gf ) , slo-1 ( js379 ) and unc-24 ( e138 ) ( for descriptions of mutants , see Materials and Methods ) . We generated strains carrying each of these mutations in combination with crIs4 and scored adults for paralysis . We found that the number of animals displaying paralysis was significantly increased when crIs4 was combined with mutations in egl-19 ( ad695 ) , unc-24 ( e138 ) or slo-1 ( js379 ) ( Figure 6A ) . Inclusion of the egl-19 ( ad695gf ) mutation produced the most dramatic increase in paralysis ( p<0 . 001 ) , whereas the unc-24 ( e138 ) mutation caused a mild , but significant increase ( p = 0 . 043 ) ( Figure 6A ) . Although the slo-1 ( js379 ) mutation also increased the level of crIs4 paralysis , this effect could be attributed to either its role in Ca2+ regulation or to its participation in the Dystrophin Associated Protein Complex ( DAPC ) , discussed below [65] . The itr-1 ( sy290gf ) mutation did not significantly affect the number of paralyzed crIs4 animals; however , ITR-1 expression has not been detected in muscle [66] . We next constructed a set of double mutants between crIs4 and members of a second group of allelic modifiers that form the Dystrophin Associated Protein Complex ( DAPC ) : dys-1 ( cx18 ) , snf-6 ( ok720 ) , dyb-1 ( cx36 ) and stn-1 ( ok292 ) , which encode a dystrophin-like protein , acetylcholine transporter , dystrobrevin and syntrophin , respectively [64] , [67]–[69] . Constituents of the DAPC provide structural support to muscle by linking the cytoskeleton to the sarcolemma and extracellular matrix [70] . We hypothesized that mutations capable of destabilizing the integrity of the DAPC could sensitize muscle cells to CLP-1 induced damage . The four mutants listed above and slo-1 ( js379 ) share a similar phenotype marked by hyperactivity and exaggerated head bending [64] , [65] , [67]–[69] . An hlh-1 ( cc561ts ) temperature sensitive allele of a MyoD homolog was also examined because this mutation sensitizes dystrophin dys-1 ( cx18 ) mutants to muscle damage [60] . We found that disrupting the structural proteins of the DAPC complex did not significantly sensitize animals to the effects of ectopic clp-1 provided by crIs4 ( Figure 6B ) . By contrast , inclusion of the hlh-1 ( cc561ts ) allele substantially increased the proportion of paralyzed crIs4 animals . We next tested whether the effects of crIs4 could be further enhanced in a dys-1 ( cx18 ) ; egl-19 ( ad695gf ) genetic background by increasing [Ca2+]i . Adult dys-1 ( cx18 ) ; egl-19 ( ad695gf ) double mutants in the absence of crIs4 are Unc , but not paralyzed , and exhibit moderate muscle degeneration [71] . We found that the percentage of dys-1 ( cx18 ) ; egl-19 ( ad695gf ) ; crIs4 adults developing paralysis was vastly elevated ( 56 . 3±6 . 4% ) when compared to animals expressing crIs4 in combination with a mutation in either dys-1 ( cx18 ) or egl-19 ( ad695gf ) alone ( Figure 6B ) . Moreover , the brood size of dys-1 ( cx18 ) ; egl-19 ( ad695gf ) ; crIs4 animals was dramatically reduced and embryonic lethality elevated ( Table S3 ) . The heightened sensitivity of dys-1 ( cx18 ) ; egl-19 ( ad695gf ) mutants to crIs4 induced paralysis led us to investigate whether dys-1 ( cx18 ) ; egl-19 ( ad695gf ) mutants might also be sensitized to the effects of ectopic clp-2 , -4 , -7 or tra-3/clp-5 expression in muscle under control of the unc-54 promoter . We examined over 1000 animals from at least three independent transgenic lines for each calpain , however , we failed to detect any exacerbated defects in movement aside from those already reported for dys-1 ( cx18 ) ; egl-19 ( ad695gf ) mutants [71] . Hence , of the calpains tested , only clp-1 was found to induce paralysis when overexpressed in muscle under conditions of elevated [Ca2+]i . The EGL-19 L-type voltage gated Ca2+ channel is located along the basal membrane of muscle . To demonstrate that egl-19 ( gf ) was exerting an effect on clp-1 activity specifically by altering [Ca2+]I , we asked if the small molecule antagonist , nemadipine-A , could suppress CLP-1 mediated muscle degeneration; nemadipine-A has been shown to be a specific and highly effective inhibitor of EGL-19 [72] . We found that a dose of 5 µM nemadipine-A was sufficient to abolish the enhanced level of paralysis observed in egl-19 ( ad695 ) ; crIs4 mutants ( Figure 7A ) . We next treated hlh-1 ( cc561ts ) ; crIs4 mutants with nemadipine-A and found that paralysis was also reduced by over 50% compared to untreated animals ( Figure 7A ) . This result indicates that the hlh-1 ( cc561ts ) mutation is likely to sensitize muscle cells to the effects of crIs4 indirectly by causing an increase in [Ca2+]I , which can be suppressed by inhibiting the activity of the EGL-19 Ca2+ channel . The aspartyl proteases , asp-1 , asp-3 and asp-4 , have previously been shown to be constituents of a neural degenerative pathway involving the calpains clp-1 and tra-3/clp-5 [35] . To ask if the asp genes also promote muscle degeneration , we performed RNAi with the genes asp-1 to asp-6 on hlh-1 ( cc561ts ) ; crIs4 animals . The effectiveness of RNAi knockdown was measured by quantitative PCR ( qPCR ) ( Table S4 ) . We found that asp-3 ( RNAi ) reduced paralysis levels by over 40% and that asp-2 ( RNAi ) and asp-4 ( RNAi ) each reduced paralysis by ∼15% , whereas asp-1 ( RNAi ) had no measurable effect in muscle . We further showed that clp-1 ( RNAi ) almost completely abolished crIs4 induced paralysis in hlh-1 ( cc561ts ) ; crIs4 animals , as might be expected ( Figure 7B ) .
CAPN1 and CAPN2 are still referred to as the main or major calpains despite the rich variety and abundance of genes encoding atypical calpain proteins ( Figure 1 ) . However , phylogenetic analyses performed here and by others indicate that the C-terminal EF hand motifs in typical calpains are unlikely to represent embellishments acquired late in metazoan evolution , as might be inferred by the greater importance attached to typical calpains [1] , [14] . We detected typical calpain homologs in basal metazoan phyla , such as Nematostella , Trichoplax and Hydra [39]–[41] , as well as in sponge , a representative of an early divergent metazoan clade [42] , and found that the EF hand domain in early metazoan typical calpain proteins was already composed of 5 motifs ( PEF ) ( Figure 1 ) . The absence of typical calpain genes in C . elegans and the presence of both typical and atypical calpain genes in Schistosomes and Drosophila further suggests that C . elegans is likely to have undergone a lineage-specific loss of typical calpain genes; it is noteworthy that Drosophila and C . elegans have both been assigned to the clade Ecdysozoa [73] . A similar line of reasoning could also be used to explain the absence of typical calpain genes in the genome of the ascidian Ciona [43] . Studies directed toward understanding the function and regulation of atypical calpains have lagged , or have possibly been confounded by the presence of typical calpains . C . elegans presents an ideal model in which to examine the function of atypical calpains , because their genome encodes a range of variants that are representative of those found across phyla , including ancient metazoan lineages , and because of the availability of mutant alleles ( Figure S1 ) . Our analyses of these mutants have shown that clp-10 ( ok2713 ) mutants have a reduced brood size , but that the remaining mutants , with the exception of tra-3/clp-5 , do not display obvious phenotypes affecting viability , motility or fertility [50] . Seven of the nine characterized mutants are represented by null or strong loss-of-function alleles that have eliminated or disrupted the catalytic triad; qPCR also shows that levels of affected transcripts were reduced in mutants by at least 70% ( Table S2 ) . Despite our inability to detect single mutant phenotypes , our results demonstrated that deletion of clp-1 suppressed muscle degeneration in dys-1 ( cx18 ) ; hlh-1 ( cc561ts ) mutants ( Table 2 ) . In neurons , it was similarly shown that RNAi knockdown of either clp-1 or tra-3/clp-5 suppressed touch cell degeneration in mutants carrying a dominant-gain-of-function mec-4 allele [35] . These results indicate that the ability to detect phenotypes in atypical calpains might be dependent on the genetic background or physiological state of the animal . It also remains possible that the expansion in the family of C . elegans atypical calpain genes has made it difficult to detect single mutant phenotypes because of functional redundancy . To gain insights into the physiological roles of atypical calpain proteins , we examined the consequences of calpain overexpression , but were unable to detect any obvious phenotypic changes resulting from heat-shock driven overexpression . CLP-1 protein expressed under these conditions peaked 4–8 hours post heat-shock before declining; by contrast , the level of CLP-1 protein resulting from unc-54 promoter activity was shown to be comparable to that detected during the peak period of expression after heat shock ( Figure S11 ) . The near-absence of phenotypes resulting from calpain overexpression could also indicate that enhanced atypical calpain activity is not necessarily detrimental in a healthy cell . One could speculate that if a given atypical calpain were constitutively active , then overexpression might have limited effect . For example , although heat shock driven expression of a wildtype tra-3/clp-5 transgene was clearly sufficient to prevent the sexual transformation of a tra-3/clp-5 null mutant , rescued animals did not display any degenerative phenotypes [34] . The substrate specificity of typical calpains is determined not only by primary sequence , but also by higher order structural features [30] . Very little is known about the substrate requirements for atypical calpains , but in our hands , CLP-1 and CLP-7 failed to cleave standard typical calpain substrates in vitro ( data not shown ) . Our experiments also revealed that of all the clp genes tested only clp-1 driven from the unc-54 promoter led to muscle degeneration and paralysis ( Figure S10 ) . The inability of the other atypical calpains to cause paralysis would indicate that sarcomeric proteins are either not general substrates for atypical calpains or that they are inaccessible unless damaged . A third possibility is that an intracellular inhibitor regulates the activities of atypical calpains . In mammals , calpastatin directly inhibits CAPN1 and CAPN2 under pathological conditions [74] . The C . elegans genome lacks a calpastatin gene , but an intracellular serpin protease inhibitor , SRP-6 , has been hypothesized to inhibit TRA-3/CLP-5 and CLP-10 in the intestine in response to hypo-osmotic shock [37] . A family of srp genes has been identified , so interactions with other clp genes might remain to be uncovered . Typical calpain proteases require Ca2+ for activity , but under conditions of reduced [Ca2+]i , they can be stimulated through the interaction of the C2 domain with membrane phospholipids and through the autolysis of DI [6] , [7] , [15] . So , how does calcium influence atypical calpain activity ? We previously showed that TRA-3/CLP-5 undergoes calcium-dependent autolysis [34] , but it remains unknown if any of the other atypical calpains can undergo this process , or if autolysis enhances proteolytic activity . Structural analyses of a mini-calpain identified two Ca2+ binding sites within IIa and IIb of CAPN2 DII , which help to align the catalytic triad when Ca2+ is bound [8] . Extrapolating from this model , the conservation of key residues in DII would predict that atypical calpains would display a similar Ca2+ dependency ( Figure S2 ) . The potential influence of phospholipids on the activity of atypical calpains has yet to be examined , although DIII forms a C2 fold that could interact with Ca2+ and phospholipids [75] . It is clear that the physiological rise in [Ca2+]i achieved through the use of genetic mutants profoundly affected CLP-1 activity ( Figure 6A ) . However , it is important to emphasize that it was the synergistic increase of both CLP-1 and [Ca2+]i levels that contributed to paralysis . None of the Ca2+ channel mutants examined developed paralysis , indicating that an increase in [Ca2+]i by itself is unable to pathologically activate clp-1 . Nonetheless , removal of clp-1 substantially suppressed the muscle degeneration associated with C . elegans dys-1; hlh-1 ( cc561ts ) DMD , which is based on a mouse model of myopathy involving the combined mutation of MyoD and dystrophin ( Table 2 ) [60] , [76] . In muscle degenerative conditions , such as DMD , a rise in [Ca2+]i is also hypothesized to activate calpain proteolysis [77]–[79] . We have also shown that the C . elegans hlh-1 ( cc561ts ) allele of a MyoD homolog , sensitizes C . elegans to the effects of crIs4 , as measured by the increase in paralysis , and that treatment of these worms with nemadine-A suppressed paralysis ( Figure 7A ) . As nemadipine-A is a specific inhibitor of the EGL-19 L-type Ca2+ channel [72] , it was not surprising to find that this drug also suppressed the paralysis associated with egl-19 ( gf ) ; crIs4 mutants . Based on these results , we propose that the hlh-1 ( cc561ts ) mutation creates a sensitized background for calpain activity by indirectly elevating [Ca2+]I , although it remains unclear if EGL-19 activity is disrupted . Following this logic , we speculate that inappropriate CLP-1 activity in a sensitized background could generate a positive feedback loop whereby calpain disrupts Ca2+ channel activity , leading to increased [Ca2+]i , and further activation of CLP-1 . In support of this model , several studies have shown that typical calpains can cleave Ca2+ channels and disrupt their activities [80]–[82] . In mammals , inappropriate elevation of either CAPN1 or CAPN2 is associated with muscle degeneration [54] . Indirect evidence has also accumulated pointing to the involvement of these calpains and the skeletal muscle specific CAPN3 isoform in myofibrillar protein turnover , which promotes the replacement of damaged sarcomeric components in order to maintain efficient muscle contraction [7] , [25] , [83]–[88] . What roles do atypical calpains normally play in muscle ? In our study and in reports by others , only the clp-1 and clp-4 genes appear to be expressed in muscle ( Figure 3 ) [35] , [89] , and it was further found that a rescuing tra-3/clp-5::gfp translational fusion was not expressed in muscle [90] . Surprisingly , it has recently been reported that chronic RNAi knockdown of clp-1 , clp-4 , tra-3/clp-5 , clp-6 or clp-7 was responsible for causing myofilament disruption [91] , suggesting that calpains are involved in myofilament maintenance . By contrast , we failed to detect sarcomeric abnormalities in similarly staged adults when phalloidin was used to examine the body wall muscle of animals carrying deletion alleles in clp-1 , clp-4 , clp-6 or clp-7 ( Figure S5 ) . We are unable to account for these differences , although it has been reported elsewhere that a GFP-tagged myosin heavy chain reporter [myo-3::gfp] can independently cause an age-dependent sarcopenia in adults [92] . Nonetheless , our study independently supports a role for CLP-1 in the maintenance of muscle adhesion complexes and turnover of myofibrillar proteins . We have obtained evidence that CLP-1 has the potential to disrupt sarcomeric integrity , indicating that CLP-1 is likely to target components of muscle adhesion complexes for destruction ( Figure 4 and Figure 6 ) . The localization of CLP-1 to M-lines and to structures immediately adjacent to Z-disks shows that CLP-1 is well positioned to participate in myofibrillar turnover or possibly remodelling of integrin-based muscle attachment assemblies ( Figure 5 ) ; however , it is interesting that CLP-1 is excluded from dense bodies ( Z-disks ) . CLP-1 could also regulate muscle cell-muscle cell interactions through its localization to adhesion plaques . In C . elegans , studies based on the use of FRAP show that C . elegans sarcomeric proteins undergo dynamic exchange suggestive of protein turnover [93] . Taken together , these observations suggest that CLP-1 might normally promote myofibrillar protein turnover and help to maintain the ordered alignment of adhesion complexes , or to accommodate changes to the sarcomere due to growth or cell damage . Evidence has also been obtained from mammalian systems indicating that the typical CAPN1 and CAPN2 calpains are able to regulate the dynamics of cell adhesion complexes [18] , which are similar in composition to those found in C . elegans muscle [19] . Our data did not support the expectation that disruptions to the DAPC , an important complex that maintains muscle structural integrity in humans , would synergize with CLP-1 overexpression and lead to increased sarcomeric damage and paralysis in worms ( Figure 6B ) [70] . However , it has been reported that the C . elegans DAPC promotes , but is not essential for muscle integrity [64] , [69] , [94] , [95] , so destabilisation of the complex might not be sufficient to induce damage and heightened myofibril turnover , despite the increased availability of CLP-1 . We propose that the death of muscle cells observed in paralyzed crIs4 animals is caused by activation of a pathway promoting necrosis . In mammalian neurons , a model has been proposed whereby inappropriate increases in [Ca2+]i caused by insults such as ischemia/reperfusion injury contribute to the activation of calpains and lead to the permeabilization of lysosomes . In turn , lysosomal rupture allows cathepsin proteases to leak into the cell and cause widespread degradation and necrotic cell death [55] . In C . elegans , support for this model was obtained when it was shown that clp-1 , the aspartyl proteases asp-3 and asp-4 and to a lesser extent asp-1 were required for the necrotic death of neuronal touch receptors and vulval uv1 cells [35] , [36] . Similarly , our data also revealed the importance of asp-3 in promoting clp-1 mediated paralysis in muscle ( Figure 7B ) . By contrast to studies in neurons , we found that asp-4 has a reduced role in muscle degeneration and that asp-1 doesn't appear to be active in muscle , although we obtained evidence that asp-2 participates in this process . We , therefore , propose that a calpain-cathepsin pathway of degeneration has been conserved in C . elegans muscle . Finally , the ability of nemadipine-A to reduce the level of paralysis in hlh-1; crIs4 worms shows that physiological conditions that lead to an indirect increase in [Ca2+]i can have a destructive and possibly necrotic outcome in muscle cells by activating atypical calpain activity ( Figure 7A ) [96] . Therefore , it will be of further interest to identify physiological conditions that could lead to elevated calpain expression or [Ca2+]i as causative factors in sarcopenia , a loss in muscle contractility observed in ageing cells [97] , [98] . We propose that the identification of atypical calpain inhibitors would represent a fruitful area for pharmacological investigation . It should be possible to take advantage of the paralytic phenotype displayed by hlh-1 ( cc561ts ) ; crIs4 mutants and to perform a chemical screen to identify compounds that ameliorate muscle damage . Such a screen could potentially identify calcium channel blockers or specific inhibitors of atypical calpains . Given that atypical calpain genes are also present in mammals , our results emphasize the importance of investigating the contribution of atypical calpains to degenerative disorders , especially under conditions associated with inappropriately elevated [Ca]I , such as muscular dystrophies , neurodegeneration and cataract .
See Protocol S1 for sequence accession numbers and details about sequence similarity searches and protein alignments . A custom profile hidden Markov model was created for each of the five EF-hand motifs of the penta EF hand , using the following proteins as a training set: HsCAPN1 ( AAH75862 . 1 ) , HsCAPN2 ( NP_001739 . 2 ) , HsCAPN3/p94 ( AAI46650 . 1 ) , HsCAPN8 ( NP_001137434 . 1 ) , HsCAPN9 ( NP_006606 . 1 ) , HsCAPN11 ( EAX04252 . 1 ) , DmCALPA ( NP_001097378 . 1 ) , DmCALPB ( NP_524016 . 4 ) , DmCALPC ( AAF48591 . 2 ) and HsGRAN ( P28676 ) [45] . These full-length proteins were aligned using FSA [48] , and each of the EF hand motifs was sliced out of the alignment , based on the coordinates provided for the EF hand motifs of HsCAPN1 , to generate five multiple sequence alignment files ( MSF ) [99] . The hmmbuild program in the HMMER software package was then used to create a profile HMM for each of the five EF-hand motifs [45]; pfam HMM files are available as Text S1 . The EF-hand motif model was validated using true positive penta-EF hand domain containing proteins CAPNS1 ( AAH64998 . 1 ) and Sorcin ( AAA92155 . 1 ) ; HsCAPN10 and the C . elegans atypical calpains were shown to be true negatives . An E-value cutoff level of 0 . 01 was used . Worms were grown and maintained at 20°C as described [100] , except strains containing hlh-1 ( cc561ts ) II , which were grown at 15°C [101] . The following strains were used: wild-type Bristol N2 , HC46: ccIs4215 [myo-3::gfp::nls] I , LS292: dys-1 ( cx18 ) I , clp-8 [F44F1 . 3] ( ok1878 ) I , clp-9 [T11A5 . 6] ( ok1866 ) I , clpr-1 [W04A4 . 4] ( ok2601 ) I , LS505: dyb-1 ( cx36 ) I , LS721: stn-1 ( ok292 ) I , LS587: dys-1 ( cx18 ) I; hlh-1 ( cc561ts ) II , LS706: dys-1 ( cx18 ) I; egl-19 ( ad695gf ) IV , NL2099: rrf-3 ( pk1426 ) II , PD4605: hlh-1 ( cc561ts ) II , clp-1 ( tm690 ) III , clp-2 ( pk323 ) III , clp-4 ( ok2808 ) III , clp-10 [W05G11 . 4] ( ok2713 ) III , JR667: unc-119 ( e2498::Tc1 ) III , VC591: okIs53 snf-6 ( ok720 ) III , dpy-20 ( e1282 ) IV , DA695: egl-19 ( ad695gf ) IV , clp-6 ( ok1779 ) IV , clp-7 ( ok2750 ) IV , itr-1 ( sy290 ) dpy-20 ( e1282 ) IV , unc-24 ( e138 ) IV , NM1968: slo-1 ( js379 ) V , EG1285: lin-15 ( n765 ) ; oxIs12 [unc-47::gfp+lin-15 ( + ) ] X , IM19: urIs13 [unc-119::gfp ( IM#175; rol-6 ( su1006 ) ] , NK358: unc-119 ( ed4 ) III; qyIs43 , UG756: bgIs312 ( pes-6::gfp ) , wIs51 [scm::gfp ( seam cell ) +unc-119 ( + ) ] . The clp-1 ( tm690 ) III allele ( kindly provided by Shohei Mitani ) was sequenced using primers GGATGAGCTCTTCTATCGTG and GTCTGACCATGGTCCATTCC to confirm the presence of a 624 bp deletion , which is predicted to create a frame-shift at A ( 460 ) , and produce a truncated protein of 493 amino acids . egl-19 encodes an L-type voltage gated Ca2+ channel; an egl-19 ( ad695gf ) gain-of-function mutation delays the inactivation of the EGL-19 L-type voltage gated Ca2+ channel , and hence leads to increased [Ca2+]i [102] . The itr-1 gene encodes an inositol 1 , 4 , 5-triphosphate receptor ( IP3R ) homolog that resides on the endoplasmic reticulum ( ER ) and releases Ca2+ into the cytoplasm in response to IP3 signalling [103]; the itr-1 ( sy290gf ) allele is a gain-of-function mutation that increases [Ca2+]i . Because the itr-1 ( sy290gf ) mutant carries a point mutation and does not display an obvious phenotype , the dpy-20 ( e1282 ) mutation , which is closely linked to the itr-1 gene , was included to facilitate the identification of itr-1 homozygotes . To account for potential marker effects , paralysis was also scored in a dpy-20 ( e1282 ) ; crIs4 genetic background . slo-1 ( js379 ) is a loss-of-function allele of a gene that encodes a Ca2+ activated potassium BK channel [104] . The SLO-1 channel is normally activated by a rise in [Ca2+]i , which leads to plasma membrane hyperpolarization . In turn , [Ca2+]i is reduced by the inactivation of L-type Ca2+ channels , such as EGL-19 [65] , [104] , [105] . unc-24 ( e138 ) is a loss-of-function allele of a gene encoding a protein containing stomatin-like and lipid transfer domains that indirectly regulates Na+ channels , and hence , similar to slo-1 , unc-24 affects [Ca2+]i through plasma membrane hyperpolarisation [106]–[109] . Primer sequences are available in Protocol S2 . Protocol S3 provides details about gene cloning and reporter construction . RNAi was performed as described by cloning PCR amplified products into the L4440 RNAi feeding vector [110] . RNAi constructs for asp-1 to asp-6 were obtained from a library [111] . Animals were harvested and RNA was extracted as described by Hope ( 1999 ) . cDNA was amplified from 1 . 5 µg of DNAse treated mRNA using Taqman Reverse Transcription Reagents ( Applied Biosystems ) , as directed by the manufacturer . qPCR was performed in triplicate in 20 µl reactions using Fast SYBR Green Master Mix ( Applied Biosystems ) and analysed using Fast System SDS software ( Applied Biosystems ) ; PCR products were verified by agarose gel electrophoresis . Gene expression data were analysed using the ΔΔCT method and normalized using ama-1 as an endogenous reference gene relative to N2 wildtype animals [112] . Primers were designed to span exon-exon boundaries and when applicable , mRNA was amplified upstream of mutant deletion/insertion sites . The primers used are listed as follows: ama-1 ( PK1084/PK1085 ) ; clp-1 ( PK1062/PK1063 ) ; clp-2 ( PK1113/PK1114 ) ; clp-3 ( PK1066/PK1067 ) ; clp-4 ( clp-4_f2/clp-4_r2 ) ; clp-6 ( clp-6_f2/clp-6_r2 ) ; clp-7 ( PK1074/PK1087 ) ; clp-8 ( clp-8_f2/clp-8_r2 ) ; clp-9 ( clp-9_f2/clp-9_r2 ) ; clp-10 ( clp-10_f2/clp-10_r2 ) ; clpr-1 ( clpr1_f2/clpr1_r2 ) ; asp-1 ( PK1095/PK1096 ) ; asp-2 ( PK1097/PK1098 ) ; asp-3 ( PK1099/PK1100 ) ; asp-4 ( PK1101/PK1102 ) ; asp-5 ( PK1103/PK1104 ) ; asp-6 ( PK1105/PK1106 ) . Worms were transformed by germ line microinjection [113] . Microinjection solutions were composed of the plasmid of interest at 10 µg/ml and a co-transformation marker , either 80 µg/ml of pRF4 rol-6 ( su1006 ) or 50 µg/ml of TG96 sur-5p::gfp . At least three independent transgenic strains were generated and examined for each construct , but only those displayed in this manuscript are listed , as they are representative of the patterns observed for a given construct . The following extrachromosomal arrays were generated with the pRF4 co-transformation marker: crEx65 ( clp-1p::nls::mrfp ) , crEx70 ( clp-2p::nls::mrfp ) , crEx72 ( clp-3p::nls::mrfp ) , crEx74 ( clp-4::nls::mrfp ) , crEx78 ( tra-3p::nls::mrfp ) , crEx83 ( clp-6p::nls::mrfp ) , crEx79 ( clp-7::nls::mrfp ) , crEx202 ( clp-1p::gfp ) , crEx110 ( hsp-16 . 64p::clp-1 ) , crEx86 ( hsp-16 . 64p::clp-2 ) , crEx88 ( hsp-16 . 64p::clp-4 ) , crEx8 ( hsp-16 . 64p::tra-3 ) , crEx96 ( hsp-16 . 64p::clp-7 ) and crEx333 ( unc-54p::clp-1::mrfp ) . The following extrachromosomal arrays were generated with the TG96 co-transformation marker: crEx325 ( unc-54p::clp-1 ) , crEx141 ( unc-54p::clp-2 ) , crEx147 ( unc-54p::clp-4 ) , crEx263 ( unc-54p::tra-3 ) , crEx258 ( unc-54p::clp-7 ) , crEx241 ( unc-47p::clp-1 ) , crEx190 ( unc-119p::clp-1 ) , crEx319 ( unc-54p::clp-1 ( C371A ) ) , crEx335 ( unc-54p::clp-1::mrfp ) and crEx336 ( unc-54p::clp-1 ( C371A ) ::mrfp ) . The unc-54p::clp-1 ( crEx325 ) extrachromosomal array , crEx325 was chromosomally integrated by gamma irradiation using a 137Cs source ( RX30/50 , Gravatom Industries ) . The integrated strain , crIs4 ( unc-54p::clp-1 ) was outcrossed five times with N2 prior to study . Tissue-specific GFP reporters used to identify tissues expressing calpain reporters include: unc-119::gfp , a pan-neuronal marker [114]; unc-47::gfp , which is specifically expressed in GABAergic neurons of the ventral nerve cord [115]; myo-3::gfp::nls , which is expressed in all muscle cells except those of the pharynx [116]; scm::gfp , a seam cell marker [117]; and pes-6::gfp , a excretory cell GFP reporter . Brood size and embryonic lethality was scored by placing individual L4 staged hermaphrodites on NGM plates and transferring them daily to a fresh plate until egg laying had ceased . The number of dead eggs ( embryonic lethality ) and live animals ( brood size ) were scored two days after transfer of the mother . The percentage of animals displaying paralysis was scored on day 2 of adulthood after first obtaining synchronized populations of animals of the specified genotype by bleaching gravid adults with alkaline hypochlorite [118] . Phenotypic scoring was performed by gently prodding animals with a platinum pick and registering their response: Unc animals retained the ability to move , but with impaired mobility; paralyzed animals failed to migrate . Animals were stained with Alexa Fluor 594 phalloidin ( Invitrogen ) . Briefly , day 2 adults displaying wildtype , Unc or paralyzed phenotypes were lyophilized in an Automatic Environmental Speedvac ( Savant ) prior to fixation in ice-cold acetone . Animals were resuspended in 20 µl S-Mix ( 0 . 2 M Na phosphate ( pH 7 . 5 ) , 1 mM MgCl2 , 0 . 004% ( w/v ) SDS ) containing 2 U Alexa Fluor 594 phalloidin , incubated for 1 hour in the dark and washed twice with PBS-Tween 20 ( 0 . 5% ) ( Sigma ) before viewing . Eighty day 2 adult animals were placed in 20 µl of SDS protein sample buffer , electrophoresed on 10% or 4–12% gradient SDS-polyacrylamide gels and transferred to nitrocellulose membranes . Blots were incubated with primary antibodies at the following concentrations: anti-mRFP/GFP primary antibody ( Invitrogen ) , 1∶1000; anti-myc 9E10 , 1∶1000; anti-α tubulin ( Abcam ) , 1∶4000; anti-actin ( Sigma ) , 1∶1000 . Protein was visualized using anti-mouse or anti-rabbit horseradish peroxidase ( HRP ) linked antibodies ( Amersham ) at 1∶5000 dilution and Western Lightning™ chemiluminescent substrate ( Perkin Elmer ) . Nitrocellulose membranes were stripped using Restore Plus stripping buffer ( Thermo Scientific ) . Protein levels were quantified using ChemiDoc-It imaging system ( UVP ) . Worms were grown in wells of 24-well plates containing 1 ml of MYOB agar [119] , including 5 µM nemadipine-A ( kindly provided by Peter Roy ) or 0 . 01% DMSO ( control ) , as described [72] . Plates were incubated at 20°C , except those carrying hlh-1 ( cc561ts ) mutants , which were raised at 15°C . Phenotypic scoring is described above . Animals were immobilized using 10 mM sodium azide . Differential interference contrast ( DIC ) and fluorescent images were captured with a Zeiss Axioskop 2 fitted with an ORCA-ER ( Hamamatsu ) digital camera driven by Openlab 4 software ( Improvision ) , or a Zeiss LSM 710 confocal driven by Zeiss Zen software . Statistical analysis was performed using Student's T-test . | Calpains are calcium activated non-lysosomal proteases that cleave proteins with exquisite selectivity . Proteins can be activated by calpain cleavage , because they are released from inhibitory constraints , or they can be targeted for further degradation to facilitate their normal physiological turnover or to promote cellular remodelling . Inappropriate calpain activity can lead to degenerative pathologies and cancers . Our understanding of calpain function is based primarily on typical calpains , which carry EF hand motifs that bind Ca2+ or mediate dimerization; however , typical and atypical calpains , which lack EF hand motifs , are both present in mammals . Hence , any therapeutic intervention designed to suppress degenerative conditions , particularly those caused by elevated Ca2+ levels , should also consider the potential involvement of atypical calpains . We have taken advantage of the model organism C . elegans , which only encodes atypical calpain proteins , to gain an understanding of the evolution and activities of these proteins . We show that the CLP-1 atypical calpain is normally expressed in muscle and localizes to sarcomeric sub-structures . We find that CLP-1 contributes to the muscle degeneration observed in a model of Duchenne muscular dystrophy . Our studies also highlight the importance of calcium dysregulation in promoting CLP-1 activity and muscle degeneration . | [
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] | 2012 | The Atypical Calpains: Evolutionary Analyses and Roles in Caenorhabditis elegans Cellular Degeneration |
Within the first three weeks of human immunodeficiency virus ( HIV ) infection , virus replication peaks in peripheral blood . Despite the critical , causal role of virus replication in determining transmissibility and kinetics of progression to acquired immune deficiency syndrome ( AIDS ) , there is limited understanding of the conditions required to transform the small localized transmitted founder virus population into a large and heterogeneous systemic infection . Here we show that during the hyperacute “pre-peak” phase of simian immunodeficiency virus ( SIV ) infection in macaques , high levels of microbial DNA transiently translocate into peripheral blood . This , heretofore unappreciated , hyperacute-phase microbial translocation was accompanied by sustained reduction of lipopolysaccharide ( LPS ) -specific antibody titer , intestinal permeability , increased abundance of CD4+CCR5+ T cell targets of virus replication , and T cell activation . To test whether increasing gastrointestinal permeability to cause microbial translocation would amplify viremia , we treated two SIV-infected macaque ‘elite controllers’ with a short-course of dextran sulfate sodium ( DSS ) –stimulating a transient increase in microbial translocation and a prolonged recrudescent viremia . Altogether , our data implicates translocating microbes as amplifiers of immunodeficiency virus replication that effectively undermine the host’s capacity to contain infection .
In HIV infection , there is mounting evidence that host-virus interactions occurring prior to peak viremia serve as key determinants of durable host containment of virus replication[1] . Despite the importance of these hyperacute phase phenomena in dictating the pace of virus dissemination and disease progression , there is limited understanding of actionable targets for manipulating the host environment in which these early interactions take place . One of the hallmark features of chronic HIV and SIV infection is a persistent and pathogenic translocation of gastrointestinal microbial products into peripheral blood[2 , 3] . Although the consequences of microbial translocation are complex and multifaceted , when inoculated into healthy individuals , microbial products stimulate a marked expansion of the CD4+CCR5+ T cell compartment[4] , which is the primary cell type in which HIV/SIV replicates and serves as a key determinant of early HIV viremia[5] . In cultured lamina propria mononuclear cells infected with HIV , exposure to microbial products stimulates virus replication[6] . Inoculation of bacterial LPS into non-human primates chronically infected with SIV provokes a striking logarithmic increase in plasma viremia[7 , 8] . Critically , microbial translocation does not appear to occur in ‘natural host’ species , such as Sooty Mangabeys and African green monkeys , in which SIV infection is non-pathogenic[9 , 10] . Given the demonstrated capacity of microbial products to promote immunodeficiency virus replication in vitro and in vivo , we hypothesized that translocation of microbial products into blood occurs early during pathogenic immunodeficiency infection and likely amplifies viremia .
We infected eight major histocompatibility complex ( MHC ) -identical cynomolgus macaques with molecularly cloned SIVmac239 . Virus replication peaked at 14 ( n = 3 ) or 18 ( n = 5 ) days post-infection ( DPI ) , reaching titers ranging from 6 . 4Log10 to 7 . 2Log10 viral RNA ( vRNA ) copies per milliliter ( copies/ml ) of plasma ( Fig 1A ) . Acute-phase virus replication decreased to establish chronic-phase set-point viral loads , which were calculated as the geometric mean of 70–140 DPI viral loads , ranging from 3 . 2Log10 to 5 . 2Log10 vRNA copies/ml of plasma . To monitor the quantity of microbial genomic DNA circulating in peripheral blood , we combined universal 16S ribosomal DNA ( rDNA ) sequencing with 16S rDNA qPCR[11] . To remove potentially confounding 16S rDNA contamination , which is commonly introduced by molecular biology reagents[12] , we passed genera-level taxonomies through a correction workflow ( S1 Fig ) to bioinformatically remove genera from experimental samples if they were detected in contemporaneously prepared water controls . Forty-two days prior to SIV infection , 16S rDNA loads averaged 2 . 9±0 . 5Log10 copies/ml of plasma ( n = 7 ) ( Fig 1B ) . On the day of SIV challenge ( 0 DPI ) , 16S rDNA loads were not significantly different , averaging 2 . 8±0 . 8Log10 copies/ml ( n = 8 ) . At 8 DPI , in all but 1 animal , 16S rDNA loads increased between 4 and 1390 fold ( 5 . 0±0 . 4Log10 copies/ml; n = 7 ) above baseline . On average , 16S rDNA loads at 8 DPI were 421-fold higher than pre-infection levels . By 14 DPI , 16S rDNA loads decreased to near or below baseline levels , averaging 1 . 0±0 . 6Log10 copies/ml ( n = 8 ) . 16S rDNA loads remained stable between 21–168 DPI ( 2 . 0±1 . 4Log10 to 2 . 1±0 . 5Log10 copies/ml; n = 8 ) . From 168–224 DPI , average plasma 16S rDNA loads increased ( P = 0 . 0078; n = 8 ) to 2 . 9±0 . 9Log10 copies/ml , which are similar to levels observed during untreated HIV infection[13] . To determine whether this hyperacute-phase increase in microbial DNA was an artifact of our cynomolgus macaque model , we obtained 0 and 7 DPI plasma samples corresponding to an independent cohort ( n = 11 ) of Indian rhesus macaques . These animals were infected following a single intravenous inoculation of swarm SIVmac251 inoculum . As in the cynomolgus macaque cohort , we found that 16S rDNA loads increased ( P = 0 . 002; n = 11 ) from 0–7 DPI from 1 . 9±1 . 5Log10 to 3 . 4±1 . 1Log10 16S rDNA copies/ml of plasma ( S2A and S2B Fig ) . In a cohort of mock-challenged macaques , we did not observe a significant ( P = 1 . 000; n = 5 ) change in 16S rDNA loads between 0 and 7 days post-inoculation of phosphate-buffered saline ( S2C Fig ) . Taken together , these data identify a transient high magnitude increase in circulating levels of bacterial rDNA prior to peak SIV viremia , which is ( a ) not specific to a single species of macaque , ( b ) not unique to a single SIV challenge virus , and ( c ) not dependent on the route of transmission . Following quantification of 16S rDNA in plasma , we sought to identify the taxa detected in plasma . At the phylum-level , peripheral blood was predominated by Proteobacteria ( 39 . 1%±21 . 3% ) , Firmicutes ( 18 . 7%±16 . 7% ) , Tenericutes ( 16 . 5%±28 . 5% ) , and Bacteroidetes ( 11 . 5%±12 . 3% ) ( Fig 2A ) . Prior to SIV challenge , Proteobacteria accounted for 34 . 7%±20 . 9% and 42 . 5%±22 . 8% of plasma microbial products in cynomolgus macaques and rhesus macaques , respectively . At 8 DPI , Proteobacteria continued to predominate ( 46 . 7%±34 . 5% ) the plasma microbial composition of rhesus macaques , whereas Proteobacteria were displaced by Bacteroidetes ( 39 . 6±3 . 1% vs . 35 . 6±3 . 3% ) in the cynomolgus macaque cohort . At the genera level , an average of 32 . 9±7 . 6 unique taxa were detected in cynomolgus macaque plasma prior to SIV infection ( Fig 2B ) . By 8 DPI , when 16S rDNA in plasma was highest ( per our sampling resolution ) , the number of genera in plasma increased to an average of 71 . 7±14 . 8 . By 14 DPI , diversity dropped precipitously to a cohort average of 10 . 9±3 . 6 genera . By 21 DPI , genera count increased to near pre-infection levels ( cohort average = 30 . 5±19 . 3 genera ) and demonstrated relative stability at 35 DPI ( cohort average = 27 . 3±6 . 9 genera ) and 140 DPI ( cohort average = 22 . 8±2 . 9 genera ) . Among the most abundant genera observed at 8 DPI were Limnohabitans , Cloacibacterium , Ruminococcus , Oscillospira , and Lactobacillus ( Fig 2C ) . Although Ruminococcus , Oscillospira , and Lactobacillus are known constituents of cynomolgus macaque flora[14] , Cloacibacterium and Limnohabitans are not often observed in vertebrates . However , Cloacibacterium has been observed in the gastrointestinal tract of patients with adenomas[15] , and Limnohabitans has been observed in patients with cellulitis[16] . Both genera were detected at multiple timepoints , in multiple , but not all , animals . In rhesus macaques , the most abundant genera at 7 DPI were Acinetobacter , Micrococcus , Phyllobacterium , Pseudonocardia , and Stenotrophomonas . Future studies will be needed to better understand differences between cynomolgus macaques and rhesus macaques , specifically in regard to the composition of gastrointestinal microbial community and identity of translocating taxa . Following SIV challenge , we did not detect overt microbial dysbiosis within the stool of cynomolgus macaques . Across all timepoints , four phyla were predominant in stool–Proteobacteria , Firmicutes , Bacteroidetes , and Spirochaetes ( S3A Fig ) . At the genera-level , Prevotella and Treponema were maintained at high abundance throughout the period of observation ( S3B Fig ) . Although this study was not designed to identify the origin of translocating microbial products , the gastrointestinal tract has been reported as a major source of translocation in HIV/SIV infection[2] . To explore the relationship between microbial translocation and the gastrointestinal microbial community , we calculated the proportion of circulating bacterial rDNA corresponding to genera detected in contemporaneous stool ( Fig 3A ) . We found that following SIV challenge , the overlap between microbial DNA sequences in plasma and stool increased significantly ( P<0 . 0005; n = 7 ) , which suggests that at least some of the microbial products translocating into peripheral blood originated from the gastrointestinal tract . However , following SIV challenge , we observed a decrease in plasma levels of intestinal fatty acid binding protein ( IFABP ) , which is released into systemic circulation[17] by enterocytes when intestinal epithelium is compromised . From 0–8 DPI , plasma IFABP decreased ( P = 0 . 0234; n = 8 ) from 3 . 2±1 . 0 ng/ml to 2 . 7±1 . 2 ng/ml ( Fig 3B ) . By 21 DPI , IFABP levels ( 3 . 4±0 . 8 ng/ml; n = 8 ) were not significantly different from baseline . These data suggest that some proportion of hyperacute microbial translocation may originate from the gastrointestinal tract , but that there is not significant loss of intestinal epithelium integrity . We next quantified plasma levels of bacteria-specific host factors within the cohort of 8 MHC-identical cynomolgus macaques infected with SIVmac239 . In the context of microbial translocation , host antibodies specific for bacterial endocore ( EndoCAb ) bind and clear LPS from circulation , which consequently reduces their titer[18] . From 0–8 DPI , cohort average EndoCAb levels decreased significantly ( P = 0 . 0078; n = 8 ) from 13 . 1±6 . 7 Milli Merck Units ( MMU ) /ml to 9 . 5±4 . 0 MMU/ml ( Fig 3C ) . This reduction is consistent with saturation of circulating EndoCAb by translocating LPS . Although not strictly a measure of the bacteria-specific host response[19] , sCD14 circulates at high levels in the plasma of healthy individuals[20] and interacts with translocating LPS to stimulate antigen-presenting cells via toll-like receptor signaling[21] . We did not observe a statistically significant change ( P = 0 . 0547; n = 8 ) in circulating levels of sCD14 from 0 DPI ( 480 . 9±91 . 7 ng/ml ) to 8 DPI ( 428 . 6±100 . 9 ng/ml ) ( Fig 3D ) , which is consistent with previous observations[22] . During chronic HIV infection , plasma levels of sCD14 are known to predict the relative rate of progression to AIDS[23] . Consistent with this prognostic association , we found that plasma levels of sCD14 at both 8 DPI ( r2 = 0 . 6497 , P = 0 . 0157; n = 8 ) and at 21 DPI ( r2 = 0 . 6045 , P = 0 . 0231; n = 8 ) correlated with set-point viral load during chronic infection ( Fig 3E & 3F ) . We found no relationship between plasma levels of sCD14 and EndoCAb ( S4 Fig ) . We next assessed the relationship between hyperacute microbial translocation and peripheral inflammation . One of the earliest host responses to HIV infection is an increase in plasma levels of monocyte chemotactic protein 1 ( MCP-1 ) [24] , which is secreted by various cell types to recruit lymphocytes and phagocytes to sites of inflammation[25] . From 0–8 DPI , we observed a significant increase ( P = 0 . 0078; n = 8 ) in MCP-1 levels from 147 . 1±34 . 4 pg/ml to 297 . 7±141 . 4 pg/ml ( Fig 3G ) . By 21 DPI , MCP-1 levels had decreased to below baseline ( 126 . 2±22 . 6 pg/ml; n = 8 ) , and continued to decrease at 56 DPI ( 85 . 5±9 . 9 pg/ml; n = 8 ) . Serum amyloid A ( SAA1 ) , which is produced in the liver , is commonly used to measure relative levels of inflammation[26] and promotes chemotaxis of both lymphocytes and phagocytes[27] . From 0–8 DPI , plasma levels of SAA1 increased ( P = 0 . 0156; n = 8 ) from 3 . 4±2 . 7 μg/ml to 8 . 2±4 . 2 μg/ml ( Fig 3H ) . The abundance of CD4+CCR5+ T cells , which are the primary targets of HIV/SIV replication[28] , increases during acute SIV infection in Asian-origin macaques[29] as well as following exposure to bacterial LPS[4] . We used flow cytometry ( S5A Fig ) and complete blood counts ( CBCs ) to monitor CD4+CCR5+ target cell abundance in peripheral blood . From 0–8 DPI , we found that the frequency of CD4+ cells expressing CCR5 increased ( P = 0 . 0078; n = 8 ) from 1 . 5±0 . 3% to 3 . 6±1 . 0% ( S5B Fig ) . By 21 DPI , the average peripheral frequency of CD4+ cells expressing CCR5+ increased to 4 . 8±2 . 4% . From 0–8 DPI , the absolute number of CD4+CCR5+ cells increased ( P = 0 . 0313; n = 8 ) transiently from 10 . 1±3 . 2 to 26 . 0±11 . 7 cells per μl blood ( S5C Fig ) . By 21 DPI , the number of CD4+CCR5+ cells decreased to 18 . 6±7 . 0 cells . We interpret these dynamics as hyperacute expansion of the CD4+CCR5+ target cell compartment with subsequent contraction into the chronic phase of infection due to a combination of virus replication and withdrawal of microbial products . Longitudinal CD4 counts and peripheral levels of Ki67+ , CD38+ , and HLA-DR+ subsets of CD4+ and CD8+ T cells are shown in S5D–S5J Fig . We selected two “elite controller” cynomolgus macaques that had maintained average plasma viral loads of 2 . 2±1 . 9Log10 ( for cy0165 ) and 2 . 1±1 . 7Log10 ( for cy0646 ) vRNA copies/ml of plasma across a 5-week period of baseline observation ( Fig 4A ) . To increase gastrointestinal permeability and facilitate microbial translocation , we treated both macaques with dextran sulfate sodium ( DSS ) as described previously[30] . Briefly , both macaques were gavaged with 200 ml of sterile drinking water containing 0 . 5% DSS once per day for 5 consecutive days ( on days 0 , 1 , 2 , 3 , and 4 of observation ) . During treatment , both animals maintained plasma viremia to levels below the limit of detection . By day 7 of observation ( day 3 post-DSS treatment ) , plasma viremia increased in both animals to 2 . 4 Log10 and 2 . 9Log10 vRNA copies/ml plasma . In one of the animals , cy0165 , plasma viremia decreased gradually until day 28 of observation . The effect of treatment was more significant in the second animal , cy0646 . From day 7 to 11 of observation , plasma viremia increased in titer to 3 . 2Log10 vRNA copies/ml . Due to blood draw volume limits , combined with sudden weight loss ( S6A Fig ) , we were unable to monitor plasma viremia in cy0646 from day 12 to 22 of observation . By day 23 of observation , plasma viremia for cy0646 had increased to 4 . 2Log10 vRNA copies/ml plasma– corresponding to more than a 120-fold increase over pre-treatment SIV titer . By day 28 , plasma viremia had decreased to 3 . 5Log10 vRNA copies/ml , and continued to decrease until day 34 of observation . Despite variation in the dynamics by which virus recrudesced , both animals experienced a marked prolonged increase in viremia following one short-course DSS treatment cycle . Prior to starting DSS treatment ( day -7 of observation ) , the macaques had plasma 16S rDNA loads of 4 , 380 and 420 copies/ml of plasma for cy0165 and cy0646 , respectively ( Fig 4B ) . During DSS treatment ( day 2 and 4 of observation ) , 16S rDNA loads averaged 740±198 copies/ml and 1130±141 copies/ml for cy0165 and cy0646 , respectively . By day 7 of observation ( day 3 following cessation of DSS treatment ) , 16S rDNA loads had increased to 15 , 220 copies/ml and 5 , 690 copies/ml for cy0165 and cy0646 , respectively . Consistent with our hyperacute-phase observations , the observed increase in plasma 16S rDNA load was transient , having decreased in both animals to near pre-treatment titers by day 11 of observation . One of the animals , cy0646 , experienced significant weight loss following DSS treatment and could not be sampled between day 12 and 22 of observation . Despite this undesirable clinical effect , the increased plasma 16S rDNA titer following DSS treatment was markedly lower than that observed in hyperacute SIV infection . We first examined plasma levels of bacteria-reactive host factors– EndoCAb and sCD14 . Prior to DSS treatment , plasma EndoCAb levels ( Fig 4C ) were 5 . 7±0 . 43 MMU/ml and 12 . 1±0 . 7 MMU/ml for cy0165 and cy0646 , respectively . Following treatment , cy0165’s EndoCAb levels did not change markedly , though decreased precipitously in cy0646 to 6 . 8 MMU/ml by day 19 of observation . The marked decrease in circulating EndoCAb for cy0646 is consistent with it being saturated by translocating LPS . Prior to treatment , plasma sCD14 levels ( Fig 4D ) were 661±77 ng/ml and 539±15 ng/ml for cy0165 and cy0646 , respectively . Following treatment , sCD14 levels increased in cy0165 to 772 ng/ml ( 1 . 7-fold increase ) by day 7 of observation . More delayed kinetics were observed for cy0646 , though sCD14 levels increased to 857 ng/ml ( 1 . 6-fold over baseline ) by day 19 post-treatment . Again , we used plasma levels of IFABP to mark increased gastrointestinal permeability ( Fig 4E ) . Relative to baseline , post-treatment IFABP levels increased as much as 1 . 16-fold ( to 1 . 1 ng/ml ) and 2 . 22-fold ( to 1 . 7 ng/ml ) for cy0165 and cy0646 , respectively . These results implicate DSS-mediated dysfunction of gut barrier integrity in our observed increase in plasma viremia and circulating 16S rDNA . We next measured plasma levels of inflammatory factor , MCP-1 ( Fig 4F ) . Prior to treatment , cy0165 and cy0646 maintained plasma MCP-1 levels of 131 . 2±18 . 3 pg/ml and 49 . 8±58 . 5 pg/ml , respectively . MCP-1 levels did not change markedly for cy0165 at any timepoint , but cy0646 showed fluctuating levels until day 52 , at which time MCP-1 levels had increased to 317 . 8 pg/ml ( 6 . 4-fold above baseline ) . Altogether , DSS treatment stimulated increased inflammation , increased intestinal permeability , and perturbed translocation-reactive host factors .
The systemic dissemination and replication of certain enteric viruses has been tied to their interaction with gastrointestinal bacteria[31] . In the gastrointestinal tract , mouse mammary tumor virus , an orally transmitted retrovirus , becomes coated in bacterial LPS , which causes circulating virions to provoke interleukin-10 production , which aids in virus subversion of cellular immunity[32] . Intriguingly , elicitation of anti-gp41 antibodies in a recent HIV vaccination study was purportedly compromised by epitope sharing between commensally encoded antigen and the viral gp41 glycoprotein[33] . These data , when considered alongside evidence that gastrointestinal barrier integrity is compromised early during HIV infection[34] , point toward microbial products being beneficial to incipient HIV infection . When bacterial LPS is inoculated into healthy human volunteers , it stimulates a marked increase in the availability of CD4+CCR5+ T cells[4] . Given that CCR5-tropic variants of HIV are responsible for newly transmitted HIV infections[35 , 36] , it is tempting to speculate that bacterial products in circulation during early HIV infection promote virus replication , both directly by stimulating CD4+CCR5+ target cell compartment expansion , and indirectly by generally increasing immune activation . This hypothesis is supported by the observation that disease severity is attenuated in SIV-infected macaques that are treated with the LPS-binding drug , sevelamer , during acute infection[37] , as well as the data presented in this manuscript showing that inducing microbial translocation stimulates increased viremia in SIV-infected macaques . In our study , prior to the acute-phase peak of viremia , we observed a high-magnitude increase in circulating levels of 16S rDNA . We can only speculate that the observed increase in 16S rDNA titer corresponds to increased entry of either intact bacteria or dissociated bacterial products into circulation . Alternatively , the increase in 16S rDNA titer may correspond to impaired host clearance of pre-existing bacteria ( or dissociated products ) in blood or tissues . However , the rapid reduction of 16S rDNA titer by 14 DPI suggests that host clearance of bacterial products was not particularly compromised during hyperacute SIV infection . Since the observed increase in 16S rDNA titer appears transient , if it corresponds to intact “live” bacteria , they may have been incapable of persisting within blood or tissues . Additionally , with regard to the transience of the observed hyperacute microbial translocation , enteropathy is known to occur during acute immunodeficiency virus infection . We suspect that as peak viremia declines to establish the chronic-phase set-point , with partial control of virus replication comes partial healing of the enteric lesions that facilitated translocation of microbial products from the gastrointestinal lumen . This would explain why the influx of 16S rDNA into blood and corresponding perturbation of inflammatory markers are transient . Although future studies incorporating longitudinal examination of the integrity of the gastrointestinal mucosa will be needed to confirm this speculation , this explanation is consistent with the transience of microbial translocation observed following short-course DSS treatment . It is also worth noting that plasma 16S rDNA loads likely underestimate the presence of 16S rDNA within whole blood[38] . Regardless , we found that increased 16S rDNA loads were accompanied by ( a ) prolonged saturation of LPS-specific antibodies , ( b ) transient perturbation of sCD14 , increased levels of inflammation as measured by MCP-1 and SAA1 , and ( c ) expansion of the peripheral CD4+CCR5+ T cell compartment . We also identified a correlation between acute-phase levels of sCD14 in plasma and chronic-phase plasma viral loads , which links chronic-phase viremia to hyperacute-phase phenomena . Future study is required to determine whether gut barrier dysfunction occurs as a direct consequence of viral particles interacting with gastrointestinal epithelia[39] or an indirect consequence of the inflammatory response to incipient infection . Though mechanistic details have not yet been elucidated , our data suggest that incipient SIV infection compromises the integrity of the gastrointestinal epithelium , which led to our hypothesis that microbial translocation amplifies early immunodeficiency virus replication . Our hypothesis is supported by previous work[7 , 8 , 30 , 37 , 40] linking microbial exposure to increased plasma viremia and by our observation that a prolonged recrudescent viremia is provoked by short-course treatment of SIV-infected macaques with the gastrointestinal permeabilizing compound , DSS . Our use of DSS has limitations that merit discussion . Although short-course DSS treatment did well to recapitulate the transience of microbial translocation observed in hyperacute SIV infection , the magnitude by which microbial products translocated was considerably higher during hyperacute SIV infection . Administering DSS at a higher dose might have more closely modeled this magnitude , but doing so may have jeopardized the health of our animals . Although exposure to microbial products[7 , 8 , 30 , 37 , 40] and oral administration of DSS[30] is known to increase immunodeficiency virus replication , our mechanistic understanding of how either insult amplifies viremia is incomplete . It is also important to note that characterizing the effect of specific translocating taxa will be imperative to better understanding both the fundamental and clinical implications of microbial translocation during immunodeficiency virus infection– this understanding will very likely require inoculating animals with well-defined assemblages of microbes . Since there is variation in plasma 16S rDNA titer at baseline , better understanding how an animal’s typical microbial “state” influences immunodeficiency virus infection may prove useful . Despite the benefits of using SIV-infected macaques to model HIV infection , differences exist between macaques and humans that may be relevant to the hyperacute-phase of infection[41] . Although evidence exists[42] , it is not currently clear whether microbial translocation occurs in hyperacute HIV infection , but studying early HIV infection requires longitudinally sampling people likely to become infected , which is ethically complicated given the demonstrated efficacy of pre-exposure prophylaxis . In summary , we report hyperacute-phase microbial translocation as one of the earliest pathological events to occur during immunodeficiency virus infection , and that it may , in fact , precede detectable lesions along the gastrointestinal epithelium . Although future study , incorporating longitudinal examination of the gastrointestinal epithelium during hyperacute SIV infection , is needed to better resolve the precise kinetics of mucosal damage , we tested the hypothesis that experimentally compromising the barrier integrity of the gastrointestinal epithelium and inducing microbial translocation in SIV-infected macaques would compromise host control of virus replication . This finding has important implications for understanding interactions between immunodeficiency virus replication and the remarkable host responses capable of durably constraining progressive infection . HIV prophylaxis may benefit from incorporating strategies to mitigate the capacity for microbial products to amplify virus replication . Lastly , a number of strategies are being investigated to provoke production of viral antigen from latently infected cells . Our DSS treatment data demonstrates that increasing gastrointestinal permeability and inducing microbial translocation results in a period of prolonged viremia , which suggests that inducing controlled rounds of microbial translocation ( or mimicking microbial antigenemia ) may be an effective strategy to reactivate the latent reservoir .
Nine cynomolgus macaques ( 8 females and 1 male ) were infected with SIV following a single atraumatic intrarectal inoculation with 7 , 000 TCID50 of SIVmac239 virus ( Genbank: M33262 ) . Five SIV-negative female cynomolgus macaques were mock-challenged by a single atraumatic intrarectal inoculation with phosphate-buffered saline . Eleven Indian-origin rhesus macaques were infected with SIV following intravenous-administration of SIVmac251 ( 500 TCID50 ) swarm inoculum . A 0 . 5% solution of dextran sulfate sodium ( DSS ) was prepared by resuspending colitis-grade DSS ( MPBio , Santa Ana , CA ) in sterile drinking water and stored at 4°C . To increase permeability of the gastrointestinal epithelium , animals were treated once per day for 5-consecutive days with 200ml of the DSS-containing drinking water– administered by gavage . Animals were monitored for clinical signs of colitis and gastrointestinal distress and received palliative and clinical care at the full discretion of WNPRC veterinarians . Plasma was isolated from whole blood by ficoll-based density centrifugation , and cryopreserved at -80°C . Nucleic acid for each animal at each time-point was isolated from 300 μl of thawed cryopreserved plasma using the Maxwell 16 Viral Total Nucleic Acid Purification Kit ( Promega , Madison WI ) , per the manufacturer’s specifications . This extraction method indiscriminately isolates both DNA and RNA , which we eluted into 50 μl of nuclease-free water . Nucleic acid was isolated from all eight ( for the hyperacute study ) or two ( for the DSS treatment study ) cynomolgus macaques within the same instrument run and using the same batch of reagents . At each time-point , water controls were processed alongside experimental samples . Following isolation , plasma nucleic acid was aliquoted into the SIV viral load assay , and the remainder was stored at -80°C until 16S-based sequencing and qPCR . Plasma SIV viral loads were determined as published previously[43] . 16S ribosomal PCR and sequencing were performed as previously described[44 , 45] . Briefly , the V4 region of the 16S ribosome gene was amplified from plasma DNA using barcoded Illumina-specific primers . Each sample was amplified in triplicate , pooled , and separated by 1% gel electrophoresis . After gel purification , samples were quantified with a Qubit high-sensitivity DNA kit ( Invitrogen ) and equimolar amounts of each sample were combined into a final pool . To exclude short fragments , the combined final pool was cleaned using the Ampure XP kit ( Agilent , Santa Clara , CA ) . The final pool was then quantified and sequenced on the Illumina MiSeq to a depth sufficient to capture the total taxonomic composition of our samples , as determined by rarefaction . We used MacQIIME 1 . 9 . 1[46] to process the raw sequencing data to genera-level resolution using the default 97% identity similarity threshold for assembling operational taxonomic units ( OTUs ) . For each experimental and control sample , genera-level relative abundance OTU tables were generated and exported . Contaminant correction was performed by comparing genera-level OTUs present in experimental samples to those present in their corresponding water control . All genera detected ( >0% ) within sample-matched water controls were removed ( frequency set to 0% ) from the sample’s OTU table . The frequencies of remaining genera were then normalized to account for the proportion of genera removed from the sample’s microbial community . To quantify the number of 16S ribosome DNA copies per milliliter of plasma , the absolute copy number was determined using a previously published universal 16S rDNA qPCR assay[11] , and then corrected by subtracting the proportion of 16S rDNA copies that corresponded to contaminants . Differences between pre-infection and 8 DPI levels of bacterial 16S rDNA were evaluated for statistical significance using two-tailed Wilcoxon signed rank testing . Plasma SAA1 and IFABP were quantified using a commercially available Monkey SAA1 ELISA kit ( MyBioSource , San Diego , CA ) and Monkey IFABP/FABP2 ELISA kit ( MyBioSource , San Diego , CA ) with 1:1000 and 1:2 sample dilution , respectively . Commercially available ELISA kits for Human CCL2/MCP-1 ( R&D Systems , Minneapolis , MN ) , Human EndoCab IgM ( Hycult Biotech , Plymouth Meeting , PA ) , and Human sCD14 ( R&D Systems , Minneapolis , MN ) were used according to the manufacturer’s protocols on plasma diluted 1:3 , 1:100 and 1:300 , respectively . Each sample was quantified in duplicate and analyzed with a 4-Parameter Logistic fit using the SoftMax Pro 6 . 4 program ( Molecular Devices , Sunnyvale , CA ) . Differences between 0 and 8 DPI levels were evaluated for statistical significance using two-tailed Wilcoxon signed rank testing . Cryopreserved PBMCs were thawed at 37°C and washed in R10 media ( RPMI containing 10% FBS ) . Between 3–5 million washed cells were transferred to 1 . 2 ml cluster tubes , and resuspended in 200 μl of R10 media . Cells were stained with 5 μl anti-CCR5-BV421 ( clone 2D7/CCR5; BD Biosciences , San Jose , CA ) at 37°C for 15 min . Cells were then surface-stained with 5 μl anti-CD4-PE/Cy7 ( clone L200; Fisher Scientific ) for 30 min at room temperature . Cells were washed twice with FACS buffer ( PBS containing 10% FBS ) , fixed with 2% paraformaldehyde ( PFA ) , incubated at 4°C , and run on a SORP BD-LSRII ( BD Biosciences , San Jose , CA ) . FlowJo ( version 9 . 8 . 3 , Tree Star ) was used to perform sample compensation and gating . For each sample , the frequency of peripheral CD4+CCR5+ cells was determined by first gating on singlet events , followed by gating on lymphocyte-sized cells , gating on CD4+ cells , and finally by gating on CCR5+ cells within the CD4+ gate . Differences between 0 and 8 DPI levels were evaluated for statistical significance using two-tailed Wilcoxon signed rank testing . The cynomolgus macaques used in this study were cared for by the staff at the Wisconsin National Primate Research Center according to regulations and guidelines of the University of Wisconsin Institutional Animal Care and Use Committee , which approved this study ( protocol g00517 ) in accordance with recommendations of the Weatherall report and according to the principles described in the National Research Council’s Guide for the Care and Use of Laboratory Animals . The Indian-origin rhesus macaques used in this study were cared for by the staff at the Oregon National Primate Research Center according to regulations and guidelines of the Oregon Health Sciences University Institutional Animal Care and Use Committee ( protocol #0989 ) in accordance with recommendations of the Weatherall report and according to the principles described in the National Research Council’s Guide for the Care and Use of Laboratory Animals . Per Animal Wellfare Approved regulations , all animals were housed in enclosures with at least 4 . 3 , 6 . 0 , or 8 . 0 sq . ft . of floor space , measuring 30 , 32 , or 36 inches high , and containing a tubular PVC or stainless steel perch . Each individual enclosure was equipped with a horizontal or vertical sliding door , an automatic water lixit , and a stainless steel feed hopper . All animals were fed using a nutritional plan based on recommendations published by the National Research Council . Twice daily the macaques on the described studies were fed a fixed formula , extruded dry diet ( 2050 Teklad Global 20% Protein Primate Diet ) with adequate carbohydrate , energy , fat , fiber ( 10% ) , mineral , protein , and vitamin content . Feeding strategies were individually tailored to the age and physical condition of the experimental subjects . Dry diets were supplemented with fruits , vegetables , and other edible objects ( e . g . , nuts , cereals , seed mixtures , yogurt , peanut butter , popcorn , marshmallows , etc . ) to provide variety to the diet and to inspire species-specific behaviors such as foraging . To further promote psychological well-being , animals were provided with food enrichment , human-to-monkey interaction , structural enrichment , and manipulanda . Environmental enrichment objects were selected to minimize chances of pathogen transmission from one animal to another and from animals to care staff . While on study , all animals were evaluated by trained animal care staff at least twice each day for signs of pain , distress , and illness by observing appetite , stool quality , activity level , physical condition . Animals exhibiting abnormal presentation for any of these clinical parameters were provided appropriate care by attending veterinarians . Prior to all experimental procedures , animals were sedated using ketamine anesthesia , which was reversed at the conclusion of a procedure using atipamizole . Animals were monitored regularly until fully recovered from anesthesia . Animals were not euthanized as part of these studies . | We infected macaques with simian immunodeficiency virus ( SIV ) to identify phenomena that may compromise immunological containment of HIV replication following transmission . Within the first days of infection , we detected a significant increase in plasma levels of microbial products . This ‘microbial translocation’ was accompanied by inflammation and expansion of the immune cells that the virus exploits to establish infection . We postulate that this hyperacute microbial event serves to promote virus replication during a period in which it would be vulnerable to the host immune response . | [
"Abstract",
"Introduction",
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"viruse... | 2016 | Microbial Translocation and Inflammation Occur in Hyperacute Immunodeficiency Virus Infection and Compromise Host Control of Virus Replication |
In computational biology , modeling is a fundamental tool for formulating , analyzing and predicting complex phenomena . Most neuron models , however , are designed to reproduce certain small sets of empirical data . Hence their outcome is usually not compatible or comparable with other models or datasets , making it unclear how widely applicable such models are . In this study , we investigate these aspects of modeling , namely credibility and generalizability , with a specific focus on auditory neurons involved in the localization of sound sources . The primary cues for binaural sound localization are comprised of interaural time and level differences ( ITD/ILD ) , which are the timing and intensity differences of the sound waves arriving at the two ears . The lateral superior olive ( LSO ) in the auditory brainstem is one of the locations where such acoustic information is first computed . An LSO neuron receives temporally structured excitatory and inhibitory synaptic inputs that are driven by ipsi- and contralateral sound stimuli , respectively , and changes its spike rate according to binaural acoustic differences . Here we examine seven contemporary models of LSO neurons with different levels of biophysical complexity , from predominantly functional ones ( ‘shot-noise’ models ) to those with more detailed physiological components ( variations of integrate-and-fire and Hodgkin-Huxley-type ) . These models , calibrated to reproduce known monaural and binaural characteristics of LSO , generate largely similar results to each other in simulating ITD and ILD coding . Our comparisons of physiological detail , computational efficiency , predictive performances , and further expandability of the models demonstrate ( 1 ) that the simplistic , functional LSO models are suitable for applications where low computational costs and mathematical transparency are needed , ( 2 ) that more complex models with detailed membrane potential dynamics are necessary for simulation studies where sub-neuronal nonlinear processes play important roles , and ( 3 ) that , for general purposes , intermediate models might be a reasonable compromise between simplicity and biological plausibility .
"All models are wrong but some are useful" [1] . A scientific model can never be ‘true’ or ‘perfect’ , since it is , at best , a close approximation of reality . Hence the outcome of a model should not be judged with a simple dichotomy of correct or incorrect , but instead with a graded scale of credibility [2] . Although any scientific theory must be falsifiable [3] , it is not falsification itself but the careful scrutiny of the difference between theoretical predictions and empirical data that actually advances our understanding of the modeled system [4 , 5] . Conventionally , scientific modeling of a complex system is characterized by several guiding principles [4 , 6 , 7]: First , a model provides a comprehensive description of the system; second , a model helps in identifying key factors of the system and improves our understanding of its operational rules; third , a model simulates and predicts the outcome of the system; fourth , the outcome of a model simulation confirms or disproves the current hypothesis about the system . In addition to these characteristics , simulations with a well-established model can complement empirical studies . Namely , a theoretical model can guide future experimental research by producing testable predictions [8 , 9] . Furthermore , components of a model can be easily manipulated in a way that may not be possible with a real system for technical , ethical , or cost-related reasons [4 , 10]; such efforts include exploratory studies of medical interventions [5] . In order for a model to be credible , it needs to be validated with empirical observations . Validations that are done both with the fundamental ( low-level ) structure of a model and with its emerging ( high-level ) outcome ensure our confidence in the predictions of the model [2] . Here we explore these aspects of computational models with a specific focus on a neuronal circuit that is known to be crucial for sound localization , the auditory function to determine the location of the source of acoustic signals . The two primary cues for sound localization are the timing and intensity differences of the sound arriving at the two ears [11] , called interaural time and level differences ( ITD/ILD ) , respectively . Many vertebrates have specialized neuronal circuits in the brainstem for detecting ITDs and ILDs . In mammals , including humans , the lateral superior olive ( LSO ) is one of such locations where binaural neurons receive inputs originating from the two ears and encode relevant information for sound localization [12] . The principal neuron of the LSO receives excitatory inputs from the spherical bushy cells of the anteroventral cochlear nucleus ( AVCN: Fig 1A ) [13–21] , whose spiking patterns encode timing and intensity information of sounds arriving at the ipsilateral ear . The LSO neuron also receives inhibitory synaptic inputs from neurons in the ipsilateral medial nucleus of the trapezoid body ( MNTB: Fig 1A ) [17 , 22–26] , which are excited by globular bushy cells in the contralateral AVCN [19 , 22 , 24 , 27–29] . Because of this excitatory-inhibitory interaction , LSO neurons typically show sensitivity to ILDs [30 , 31] . Namely , the spike rate of an LSO neuron becomes high when the sound source is located in space towards the ipsilateral ear and low when the source is located towards the contralateral ear [32 , 33] . In addition to this intensity coding , LSO neurons are also sensitive to the timing of sound stimuli; the output spike rate of LSO varies according to the modulation frequency of monaural AM sounds [34] as well as to the ITD of the fine structure [35 , 36] and envelope of binaural amplitude-modulated ( AM ) sounds [37–39] . Examples of such timing and intensity coding in the LSO are shown in Fig 1 . Although responses of LSO neurons to monaural AM sounds show relatively large variabilities , a majority of neurons presented low- or band-pass characteristics frequently with a mild peak at 100–400 Hz ( Fig 1B ) . This response property can be explained by monaural coincidence detection of excitatory inputs and its variability originates from unit-to-unit differences of biophysical parameters of coincidence detection [40] . The spike rate of the LSO neuron varies periodically with the envelope ITD of AM sounds , with its period being the reciprocal to the modulation frequency ( Fig 1C ) . ITD-tuning curves of LSO neurons to binaural AM sounds at different modulation frequencies are usually aligned at or near their troughs , which is a signature of ‘anti-coincidence detection’ of excitatory and inhibitory synaptic inputs [36 , 38 , 40] . The classical ILD-tuning curve of the LSO neuron shows a monotonic sigmoidal decrease of spike rates according to ILDs ( Fig 1D ) . The peak and trough rates as well as the location of the mid-point of the ITD-tuning curve generally depend on the overall input level [31] . A large variety of models have been used to study the functions of LSO . Previous neuronal models of LSO ranged from abstract ones that dealt with the input-output statistics of LSO using point processes [41–44] , to a detailed multi-compartment model that incorporated neuronal morphology and spatial distribution of ion channels [45] . Between these two ends of the spectrum , single-compartment ( point neuron ) models with various internal dynamics have been created , such as simple comparison [46] or temporal summation [47] of excitatory and inhibitory inputs , an electric circuit model of resonating membrane potentials [48] , a leaky integrate-and-fire model with standard configurations [49] , with synaptic plasticity [50] or with afterhyperpolarization [51] , and a Hodgkin-Huxley ( HH ) -type conductance-based model with several types of ion channels [52 , 53]; see [54] for a more detailed review of earlier modeling approaches . Recently , LSO models ( either abstract or physiology-based ) have been incorporated with larger scale simulations , for example , to study psychophysical outcomes [55 , 56] , to develop bio-inspired neural networks of sound localization [57] , and to evaluate binaural hearing of cochlear implant users [58 , 59] . In light of such applications , ‘reproducibility’ , ‘credibility’ , and ‘generalizability’ comprise fundamental principles of computational models . Reproducibility refers to the ability of the model to ( re- ) generate sufficiently similar ( if not identical ) results to the original implementation . Since the importance of reproducible models has been extensively discussed recently ( see , e . g . , [60 , 61] and references therein ) , we do not investigate this issue in the present paper . Credibility refers to the ability of the model to reliably simulate empirical observations . Only with sufficient credibility can a model be reliably used as a building block to construct higher level models [4] . To ensure the credibility of a model , simulated outcome of the model must be validated against corresponding experimental data ( see , e . g . , [62–64] for the concept of model validation in various scientific fields ) . Generalizability refers to the applicability of the model to a wide range of contexts including ones that the model was not primarily designed for . In principle , simplistic models with a small number of components are less flexible and less expandable than complex models , resulting in lower generalizability . In the field of neuro- and sensory science , however , most models are tuned to simulate specific sets of experiments . It is therefore normally unclear how a model may or may not reproduce empirical results that are beyond the initial scope of the modeling approach [5 , 65–67] . The problem of credibility and generalizability also applies to modern LSO modeling; different LSO models are rarely compared with each other , and thus potential users who want to incorporate an LSO model into their simulation framework usually have little or no clue which model to use . In this study , we introduce , ( re- ) examine and compare several types of single-compartment LSO neuron models . The complexities of the models , spanning from functional ones that simply compare their excitatory and inhibitory inputs to biophysically detailed conductance-based models whose membrane potential dynamics are determined by nonlinear kinetics of ion channels . Our selection largely covers the spectrum of physiological point neuron models . More specifically , we examine seven LSO models of different levels of complexity: three shot-noise models ( coincidence counting model , exponential and alpha Stein models ) that simply simulate the excitatory-inhibitory interaction in LSO , and four conductance-based models that describe the membrane potential dynamics of LSO neurons . The conductance-based models can be further subdivided into two classes: integrate-and-fire ( IF ) -type ( passive and active IF models ) with explicit threshold parameters , and HH-type ( original and adjusted Wang-Colburn models ) whose thresholds are determined by the interaction of voltage-dependent conductances . Among these seven models , the coincidence counting model [40] , exponential Stein model [47 , 54] , passive IF model [49–51] , and the original Wang-Colburn model [53] were already used in previous modeling studies to simulate monaural or binaural computation of LSO . The alpha Stein and adjusted Wang-Colburn models are modifications of their original counterparts with better biological plausibility or predictive credibility . The active IF model , which is an enhanced version of the standard ( passive ) IF model with an additional nonlinear conductance , had first been introduced to replicate the activity of auditory coincidence detectors [68] , and has been revised here to fit the response properties of LSO . The parameters of the selected models were tuned to reproduce known in vivo and in vitro recording results including the monaural and binaural tunings of LSO neurons ( as shown in Fig 1 ) , which we considered to be the most representative response properties of LSO ( see Discussion ) . For conductance-based models , sub- and suprathreshold responses of their membrane potentials are also tuned with available physiological data . Since not all models were constructed to replicate all these response properties , calibrating the models at this stage is already part of the generalization process of modeling . After fitting the models , additional model responses and computational performances are compared to further characterize the models . Construction , parameter selection and justification of each model , as well as the response characteristics we examined , are fully described in Materials and Methods with corresponding references . The main goals of this study are to provide several different types of LSO models that are confirmed to be capable of reproducing a pre-defined set of empirical data , and to reveal the fundamental characteristics of these models , so that they can readily be used for future applications . Envisioned usage scenarios range from fundamental biophysical studies to biomedical and engineering applications . Example of fundamental studies include investigating the roles of various ion channels and nonlinear dynamics that determine the excitatory-inhibitory interaction of LSO ( e . g . , [45 , 51 , 53] ) and mathematically formulating the input-output relationship of auditory coincidence detectors ( e . g . , [69 , 70] ) . Simulating the binaural hearing of normal listeners [55] and cochlear implant users [58 , 59 , 71] is part of possible biomedical applications , while constructions of bio-inspired binaural neuronal networks for mobile robots [72] and cell phone noise reduction [73] are engineering applications that require real-time computation . Since there is a general trade-off between simplicity and biological plausibility [9] , selecting the most suitable model should depend on the purposes of a specific application . The present study thus aims to reveal the advantages and disadvantages of each LSO model to help future users select an appropriate model for their envisaged use . In the following sections , we examine each LSO model in detail . Based on our comparison results of the seven LSO models that are tuned ( generalized ) with the common criteria , we conclude that the simplistic shot-noise models are suitable for applications where computational efficiency or theoretical transparency is desired , while the more complex conductance-based models are generally required for investigating the underlying sub-neuronal mechanisms of binaural computation . Within the conductance-based models , either HH-type models or IF-type models should be selected by the user , depending on the required details of discharging mechanisms and morphological expandability [74] .
In this study , we compare the seven physiological models of LSO that were briefly introduced in the last section . The full definitions of the models and the detailed criteria for selecting their parameters are provided in Materials and Methods . In this section , we briefly describe the underlying ideas of calibration and evaluation of the models . In next sections , systematic examinations of each model will follow . Following our previous work [40] , the input stage of the modeling framework consists of 20 excitatory fibers and 8 inhibitory fibers , which correspond to bushy cells in the AVCN and principal neurons in the MNTB , respectively ( Fig 1A ) . The spiking patterns of these input fibers are modeled with an inhomogeneous Poisson process to simulate empirical spike rates and degree of phase-locking , both of which varied with the modulation frequency and the sound level ( Fig 2; see Materials and methods for relevant references ) . To enable direct comparisons across LSO models , the same set of simulated inputs is fed to all the model neurons; namely , excitatory and inhibitory presynaptic spikes generated by Poisson processes with the identical random seed are commonly given to each model as its input . Each LSO neuron model has its own set of parameters , some of which are experimentally constrained ( i . e . , corresponding empirical data exist ) and others are not . We used data from cats , gerbils , guinea pigs , rats , and mice to calibrate and justify the parameters of each LSO model ( see specific section for each model in Materials and methods ) . Experimental measurements , however , are generally subject to random noise , trial-to-trial variability , and unit-to-unit variability . Therefore , empirical data for any specific parameter are usually reported as a range , but not as a single value . Wherever possible , we tried selecting parameters from empirically measured ranges . In conductance-based models , we first fit the parameters for subthreshold responses , and next tuned the remaining parameters for spiking output . To facilitate comparisons across models , we selected the parameters so that the model output resembled empirical results . The output measures we used are fully described in Materials and Methods with relevant references . In brief , we calibrated the model with the monaural AM frequency tuning curve for the modulation frequency between 50 and 1200 Hz ( Fig 3A ) , the binaural AM phase tuning curve at the modulation frequency of 300 Hz ( Fig 3B ) , and the binaural level tuning curve at the ipsilateral level of +35 dB ( Fig 3C ) . For each of these tuning curves , the peak rate , trough rate , and the modulation depth ( defined as peak rate minus trough rate ) were determined . For each of these three rates in each of the three tuning curves , we defined a ‘target’ range ( bold numbers in Fig 3 ) . Model parameters were selected such that the resulting spike rates fell within these target ranges . If we did not find a combination of parameters that satisfies all the target ranges , we loosened the criteria by adopting the ‘accepted’ ranges ( non-bold numbers in Fig 3 ) , and re-selected the model parameters . In searching parameters , we did not use fully automated methods such as genetic algorithms , but chose one of the proper parameter sets in a semi-manual way by dividing the parameter space into grids ( see Discussion ) . After generalizing the models with the common tuning criteria , we also calculated the binaural tuning curves at different modulation frequencies ( 150 and 450 Hz ) or at different ipsilateral sound levels ( +25 to +45 dB ) to further characterize the model . The coincidence counting model was introduced to explain how ‘anti-coincidence’ of excitatory and inhibitory synaptic inputs affects the monaural and binaural coding in the LSO [40] . The model simply counts the number of synchronized excitatory inputs arriving within the coincidence window and generates a spike if this number reaches or exceeds the threshold ( Fig 4A ) . Effects of inhibition are modeled as a subtraction of excitatory inputs ( or equivalently as an increase of the threshold ) within the inhibition time window . Excitation and inhibition have different amplitudes and time scales ( Fig 4B ) , which were selected to fit the target output rates . Sample traces of the model are shown in Fig 4C and 4D; since each synaptic input was modeled as a rectangle with an abrupt onset and offset ( Fig 4B ) , simulated subthreshold model traces were generally jaggy ( Fig 4C , left; Fig 4D , left ) . For phase-locked inputs driven by AM sounds , the intensity of summed synaptic inputs changed periodically at the modulation frequency of the sound . When the excitatory and inhibitory inputs were out of phase , the subthreshold response ( virtual membrane potential ) of the model showed large oscillations and the resulting output rate becomes high ( Fig 4C , top left ) . When the excitation and inhibition arrived in phase , they canceled each other , resulting in a low output rate ( Fig 4C , bottom left ) . Because of this excitation-inhibition interaction , the output rate of the model neuron changed according to the phase difference of simulated excitatory and inhibitory synaptic inputs ( Fig 4C , right ) . For non-phase-locked inputs ( corresponding to non-modulating sound stimuli as in Fig 1D ) , the output rate of the model neuron depended on the relative intensities of excitatory and inhibitory inputs ( Fig 4D , right ) . When the sound level was higher at the ipsilateral ear than at the contralateral ear , excitatory inputs strongly drove the model neuron , leading to a high output spike rate ( Fig 4D , top left ) . As the sound level at the contralateral ear increased , intensity of inhibitory inputs became stronger , making the virtual membrane potential stay away from the spike threshold ( Fig 4D , bottom left ) . The simulated monaural tuning curve ( rate modulate transfer function: rate-MTF ) for the coincidence counting model showed a mild peak at 200–300 Hz ( Fig 4E ) , corresponding to the ‘peak & decrease’ type of empirical tuning curves ( Fig 1B ) . This peak was explained by monaural coincidence detection of excitatory inputs [40] . The degree of phase-locking , measured by modulation gain ( synchrony modulation transfer function: synch-MTF ) , showed similar patterns to experimental data [34] , with a mild peak found at 200–500 Hz ( Fig 4E , inset ) . However , the synch-MTF showed a rebound at above 1 kHz ( Fig 4E , inset , arrow ) , which was not seen in previous recordings ( e . g . , [34] ) . Simulated binaural phase-tuning curves at 150 , 300 and 450 Hz ( Fig 4F ) all resembled empirical data ( Fig 1C ) , with troughs aligned at a positive time difference . Simulated binaural level-tuning curves at different ipsilateral levels ( Fig 4G ) were also similar to empirical data ( Fig 1D ) , with level-dependent peak rates and midpoint positions . In the following sections , we compare these monaural and binaural tuning properties between LSO models . The Stein model is named after Richard B . Stein [75 , 76] , who introduced the model to theoretically investigate the variability of neuronal spiking activity . This model was later adopted for the study of the binaural function of LSO [47] . The type of model is also called the ‘shot-noise’ model [54] , but we use this term for a wider category that includes both the coincidence counting model and the Stein models . In this paper , we compare two types of Stein models that are distinguished by the function used for synaptic inputs; namely , the conventional version named the ‘exponential Stein model’ with exponentially decaying functions , and the revised version , the ‘alpha Stein model’ , using alpha functions . These Stein models have more flexibility than the coincidence counting model , since they have decaying synaptic inputs and thus allow for non-integer thresholds . The internal state of the model reflecting the decaying synaptic inputs is here called the ‘virtual membrane potential’ . The term ‘virtual’ is used to indicate the fact that a shot-noise model does not have an explicit membrane potential ( in mV ) but instead counts the number of input spikes as an analog of the membrane response . The integrate-and-fire ( IF ) model and its variations have been widely used in theoretical and computational neuroscience [78–80] , including modeling studies of LSO [49–51] . Compared to the shot-noise models examined above , IF-type models can be more directly related to biological membranes by having variables and parameters with clear biophysical meanings , such as the membrane potential , input resistance , and the membrane time constant . Here we examine two IF-type models: the standard leaky ( linear ) IF model , which we call the ‘passive IF model’ , and an enhanced version with a nonlinear subthreshold current , which we call the ‘active IF model‘ . The general idea of the active IF model was previously presented in [68] , but its membrane properties and spike-associated current were revised to fit known physiological characteristics of LSO . In both models , we first tuned the subthreshold membrane parameters using empirical data , and then selected the threshold to reproduce monaural and binaural responses ( see Materials and methods for details ) . The Wang-Colburn model [53] is a conductance-based , HH-type model with several nonlinear conductances ( Fig 9A ) . The leak and KLVA currents characterize the subthreshold responses , whereas the high-voltage activated potassium ( KHVA ) and sodium ( Na ) currents are responsible for spike initiation and after-spike repolarization . The kinetic equations for the Wang-Colburn model was taken from the Rothman-Manis model [87] , which was based on physiological recordings of guinea pigs and has been widely used in computational studies of auditory neuroscience . The model does not have an explicit spike threshold as a parameter , since the spike threshold of a HH-type model is determined by nonlinear interactions of the ionic conductances . Because of these characteristics , the Wang-Colburn model has more plausible biological grounds than other simpler models that we have examined so far . It nevertheless has a number of parameters that are not physiologically well-constrained ( i . e . , not all parameters were empirically measured , or even measurable ) . In the present study , we examine two types of the Wang-Colburn models: the original version [53] and an adjusted version to better fit empirical data ( see Materials and methods for detailed definitions ) . In previous sections , we have characterized the response properties of seven LSO neuron models . Mutual relations of the models are shown in Fig 11A . Monaural and binaural tuning properties of the models are summarized in Table 1 . We used monaural AM tuning , binaural phase tuning , and binaural intensity tuning curves for selecting the parameters . In order to quantify model performance for comparison , we set a criterion for the peak , trough , and depth of these three curves , yielding nine targeted ranges in total ( see Materials and methods for their definitions ) . All models satisfied the target ranges for monaural AM tuning showing similar band-pass curve shapes with a peak at around 200–300 Hz ( Fig 11B ) . Binaural phase tuning curves of the models were also similar to each other with some variations in modulation depths ( Fig 11C ) , while their binaural intensity tuning curves considerably differed particularly in peak amplitudes ( Fig 11D; see also Discussion ) . The coincidence counting model , the active IF model and the adjusted Wang-Colburn model satisfied all the targeted ranges ( as shown by the bold numbers in Table 1 ) . For the other models , we weakened the criteria by introducing wider accepted ranges . All models except the original Wang-Colburn model achieved the accepted ranges for the nine output rates ( non-bold numbers in Table 1 ) . In addition to the credibility of the model , which is characterized by how well the model reproduces corresponding empirical data , computational performance is another important factor to evaluate a model . Relative computational time increased with the complexity of the model ( Table 1 , rightmost column , from top to bottom ) . The coincidence counting model was more than 100 times faster than the Wang-Colburn model , followed by the Stein models ( 40–70 times faster ) and the passive IF model ( about 20 times faster ) . Calculation of the nonlinear dynamics of KLVA conductance considerably reduced the speed for the active IF model , because the model became a system of two differential equations , making it more than 4 times slower than the passive model . Additional nonlinear conductances made the computation even slower by another factor of over 4 in the Wang-Colburn models . It should be noted that we used the simple forward Euler method with a fixed time step to measure the relative computational time . Selection of a proper integration method in combination with an adaptive time step may reduce the computational time without losing the computational accuracy [89] . Therefore the relative computational time shown here should be regarded as one possible measure for evaluating the computational performances of the models . In summary , the coincidence counting model yielded better fit to the targeted ranges with less computational costs than the Stein models and the passive IF model , whereas the active IF and adjusted HH model required more computational time to make similarly good physiological predictions . In Discussion , we re-examine the physiological and computational performances of each model , and provide our suggestions on possible applications of these models .
Having multiple working hypotheses has long been suggested to be advantageous in scientific studies over sticking to only one ( possibly flawed ) ‘ruling’ hypothesis , because comparison of hypotheses is more likely to reveal various causes of a complex phenomenon by encompassing it from several sides [90–92] . Comparative research across animal species , for example , allows us to identify general functions of the subjects under study [93 , 94] . Similarly , comparison of different models helps us reveal which specific assumption results in what outcome , providing insights about the common operating principles of the system . In this study , we examined seven physiological models of LSO , which simulated neuronal processing of monaural and binaural acoustic information relevant to sound localization . The outcome of each model was compared with available empirical data as well as with simulation results of other models . In most earlier modeling studies , the function of an LSO neuron was abstracted as an interaction of excitatory and inhibitory inputs that determines the output spike rate [41–44 , 46] . In contrast , as empirical data accumulated , more physiological modeling approaches became prevalent [45 , 47–53] . These models were constructed on available in vivo and in vitro recording data: e . g . , the ionic conductances and time constant of the membrane , synaptic time scales , spiking responses to monaural and binaural sound stimuli , and refractoriness . Such a physiological model is sometimes called a ‘pinkbox’ model [54] , making a contrast to functional , ‘blackbox’ models that solely focus on the input-output statistics of a neuron [9] . The credibility of a physiological model is warranted by the solid connection between its underlying biophysical processes and resulting spiking behavior . Within the category of physiological models , several levels of description may exist , resulting in a number of different models explaining the same phenomena . In the present study , we presented the models in order of ascending complexity , from the most simplistic coincidence counting model ( Fig 4 ) to the elaborate HH-type models ( Figs 9 and 10 ) , having Stein and IF models ( Figs 5–8 ) in between . Previous theoretical studies connected several levels of description by using various techniques in reducing models: e g . , the reduction of a HH model into a threshold model [100] , into a two-variable model [101] or into a variation of active IF models [102–104] , and the reduction of a spike neuron model into a rate model [105] . Our active IF model of LSO is closely related to an MSO model with KLVA conductance and thresholding [106] , which was introduced as a reduced description of more-detailed conductance-based model . In this study , we examined and compared the biological grounds , simulated responses , computational costs , and further expandability of seven neuronal models of the lateral superior olive ( LSO ) . Envisioned applications of these models may range from basic research for investigating biophysical mechanisms of binaural information processing , to biomedical and engineering applications for better human and machine hearing . Based on our comparison results , we obtained the following conclusions:
Our LSO modeling framework consists of two stages , input and output . In the input stage , sound stimuli are converted into simulated spike sequences of excitatory and inhibitory inputs to the model LSO neuron . In the output stage , the model neuron ‘processes’ these inputs according to its specific rules and produces output spikes . Since the main aim of this study was to compare various types of LSO neuron models , we fixed the input stage and evaluated the output of each model neuron . In this section , we first define the common input stage to be used with all LSO models . In next sections , evaluation criteria and detailed model descriptions are provided . The major input sources to the LSO neuron are spherical bushy cells in the AVCN and principal neurons in the MNTB , which provide excitatory and inhibitory synaptic inputs , respectively ( Fig 1A ) . In our modeling framework , simulated excitatory and inhibitory spike trains of these neurons were used as the common input to drive all LSO models . Other input sources are not considered , although they might modify the neuronal activity of LSO neurons [126] . We assumed that the activity of these AVCN and MNTB fibers ( driven by modulated or unmodulated tones ) can be described as an inhomogeneous Poisson process [78 , 79] with a time-varying intensity function λ ( t ) . As in our previous study , we assumed that an LSO neuron receives 20 excitatory input and 8 inhibitory inputs ( see [40] for the justification of these numbers ) . The activities of these input fibers were assumed to be statistically independent from each other . Table 3 summarizes the equations we used for generating inputs to LSO models . Since the spiking activity of bushy cells and MNTB neurons are generally similar to each other ( e . g . , [34] ) , we assumed the same frequency and level dependence for both types of neurons . For AM tones , we considered the situation where the sound level was fixed and the modulation frequency and relative phase of the envelope between the two ears were varied . The intensity function λ ( t ) of input fibers are locked to the modulation frequency fm , with λ1 ( fm ) being the frequency-dependent average intensity and pk ( x ) being a 2π-periodic function . We used a von-Mises distribution function [159] for pk ( x ) , where the degree of phase-locking measured as vector strength ( VS ) [160] was parameterized by the concentration factor k ( Table 3 ) . For more detail about theoretical formulations , see [84] . As in our previous study [40] , we adopted monotonically decreasing functions ( Fig 2A and 2B ) to roughly mimic empirical frequency dependence of intensity ( λ1 ) and phase-locking ( VS ) of input fibers in gerbils [161] and cats [34 , 162] . In our simulations , we first fixed the modulation frequency fm and calculated the corresponding VS ( fm ) ; we then back-calculate the concentration factor k from the equation for VS ( k ) ; and we finally obtained Poissonian spike trains with this time-varying intensity function pk ( x ) . We assumed that all input fibers ( either excitatory or inhibitory ) are locked to the same phase of the envelope when driven by AM tones . For ipsilateral monaural stimulation , the spontaneous activity of MNTB neurons was simply modeled as homogeneous ( time-independent ) Poisson trains without phase-locking . To simulate ILD coding in the LSO ( in response to non-modulated tones ) , we adopted a sigmoidal level-dependent intensity function λ ( SPL ) of input fibers ( Table 3; Fig 2C ) . This equation roughly approximates known physiological recording results , which showed relatively large variations across species ( cat VCN: [34 , 163]; monkey VCN: [164]; gerbil VCN: [165]; cat MNTB: [166 , 167]; rodent MNTB: [168] ) . We assumed that spiking activities of input fibers were not phase-locked for non-modulating tones , and hence the intensity function λ of the Poisson process used for ILD coding was intensity-dependent but time-independent . The main goal of this study was to compare different types of LSO models with similar monaural and binaural tuning properties . Based on previous recording results ( e . g . , Fig 1 ) , we determined a set of reference output , to which the parameters of our LSO models were tuned . For each model , tuning curves for monaural AM frequency coding ( Fig 3A ) , binaural AM phase coding ( Fig 3B ) , and binaural intensity coding ( Fig 3C ) were examined ( see following subsections for their detailed descriptions ) . For each of these tuning curves , we set a ‘targeted range’ ( bold numbers in Fig 3 ) and an ‘accepted range’ ( non-bold numbers in Fig 3 ) of spike rates . This means that we have nine criteria in total for each optimized model to satisfy ( Table 1 ) . The performance of a model was measured by the number of targeted/accepted values achieved . We did not use the detailed shapes of simulated tuning curves as a primary measure of the performance , since no systematic data on corresponding curve shapes were available from previous experimental studies . For each parameter set , we calculated the average spike rates of the model neuron over 40 seconds . It should be noted that multiple parameter combinations sometimes yielded virtually identical results , with the same number of targeted values attained ( see Discussion ) . In such cases , we selected parameters that were closer to the corresponding empirical mean or median ( if available ) , and had shorter digits ( e . g . , 1 . 2 rather than 1 . 2345 ) . Care was also taken to ensure that a small variation ( typically a few percent ) in a single parameter value did not lead to a change in the hit/miss ratio of the target ranges , by avoiding parameter values with which simulated spike rates fell onto the ‘borderline’ of the targeted ( or accepted ) ranges . This means that the set of model parameters we used may not be the ‘only best’ , but should rather be regarded as one of ‘reasonably good’ combinations of parameters that satisfy our target criteria ( see also Discussion ) . For conductance-based models ( integrate-and-fire models and Wang-Colburn models: see subsequent sections for detailed descriptions ) , we used the following measures to tune the model parameters and evaluate their resulting membrane properties . In the following subsections , we provide the detailed descriptions of LSO models used in this study . All of them are single compartment models , which lack morphological structures ( such as axons or dendrites ) and thus receive synaptic inputs directly at the cell body ( soma ) . Since the models have no internal noise sources , the model responses are deterministic; i . e . , the model produces identical output for the fixed input . Trial-to-trial variability of the model output is solely due to the stochastic nature of simulated inputs . The models can be categorized into either ‘shot-noise models’ , in which synaptic inputs are directly reflected to the abstracted response of the model ( virtual membrane potential ) , or ‘conductance-based models’ , in which synaptic inputs and other ionic currents are described as temporally-varying conductances that eventually lead to the change in the modeled membrane potential ( Fig 11A ) . The conductance-based models are further subdivided into two: ‘integrate-and-fire ( IF ) models’ , in which spike generation process is abstracted as the detection of threshold crossing and succeeding reset of the membrane potential , and ‘Hodgkin-Huxley ( HH ) -type models’ , in which sub- and suprathreshold responses of the membrane are fully described as the combined nonlinear dynamics of ionic conductances ( Na+ , K+ , etc . ) . Of the seven models described below , the coincidence counting model , exponential Stein model , and alpha Stein model are shot-noise models; the passive and active IF models are ( conductance-based ) IF models; the original and adjusted Wang-Colburn models are HH-type models . We used the explicit ( forward ) Euler method for the numerical integration of the model equations . All simulations were performed with a time step 2 μs , although IF and shot-noise models generally allowed for longer time steps for stable and accurate calculations ( see , e . g . , [68] ) . In order to evaluate the computational cost of each model , we calculated the average integration time of twenty-five traces , each of which was 40-second long ( i . e . , 1000 seconds in total ) . To yield relative computational costs , we normalized the average integration time of each model by that of the coincidence counting model which required the shortest computation time . Numerical algorithms were implemented in D [185] and simulations were carried out on a desktop computer ( Dell Precision T1700 ) with 64 bit Windows 7 Professional Operating System , Intel Xeon CPU E3-1270 v3 ( 4 core , 3 . 5 GHz ) and a 16 GB memory . For readers’ convenience , Matlab implementation of the models is publicly available online at https://github . com/pinkbox-models . | Computational models help our understanding of complex biological systems , by identifying their key elements and revealing their operational principles . Close comparisons between model predictions and empirical observations ensure our confidence in a model as a building block for further applications . Most current neuronal models , however , are constructed to replicate only a small specific set of experimental data . Thus , it is usually unclear how these models can be generalized to different datasets and how they compare with each other . In this paper , seven neuronal models are examined that are designed to reproduce known physiological characteristics of auditory neurons involved in the detection of sound source location . Despite their different levels of complexity , the models generate largely similar results when their parameters are tuned with common criteria . Comparisons show that simple models are computationally more efficient and theoretically transparent , and therefore suitable for rigorous mathematical analyses and engineering applications including real-time simulations . In contrast , complex models are necessary for investigating the relationship between underlying biophysical processes and sub- and suprathreshold spiking properties , although they have a large number of unconstrained , unverified parameters . Having identified their advantages and drawbacks , these auditory neuron models may readily be used for future studies and applications . | [
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"a... | 2017 | Physiological models of the lateral superior olive |
Measles virus ( MV ) is a highly contagious member of the Morbillivirus genus that remains a major cause of childhood mortality worldwide . Although infection induces a strong MV-specific immune response that clears viral load and confers lifelong immunity , transient immunosuppression can also occur , leaving the host vulnerable to colonization from secondary pathogens . This apparent contradiction of viral clearance in the face of immunosuppression underlies what is often referred to as the ‘measles paradox’ , and remains poorly understood . To explore the mechanistic basis underlying the measles paradox , and identify key factors driving viral clearance , we return to a previously published dataset of MV infection in rhesus macaques . These data include virological and immunological information that enable us to fit a mathematical model describing how the virus interacts with the host immune system . In particular , our model incorporates target cell depletion through infection of host immune cells—a hallmark of MV pathology that has been neglected from previous models . We find the model captures the data well , and that both target cell depletion and immune activation are required to explain the overall dynamics . Furthermore , by simulating conditions of increased target cell availability and suppressed cellular immunity , we show that the latter causes greater increases in viral load and delays to MV clearance . Overall , this signals a more dominant role for cellular immunity in resolving acute MV infection . Interestingly , we find contrasting dynamics dominated by target cell depletion when viral fitness is increased . This may have wider implications for animal morbilliviruses , such as canine distemper virus ( CDV ) , that cause fatal target cell depletion in their natural hosts . To our knowledge this work represents the first fully calibrated within-host model of MV dynamics and , more broadly , provides a new platform from which to explore the complex mechanisms underlying Morbillivirus infection .
Measles virus ( MV ) is highly contagious and remains one of the leading causes of child mortality worldwide , despite the existence of a safe and effective vaccine [1] . In addition to its public health importance , MV is also a paradigm for understanding the dynamics of acute respiratory infections at broad epidemiological scales [2 , 3] . However , at finer within-host scales , the kinetics of MV are less well understood . Unlike other common respiratory viruses such as influenza , MV does not initially infect epithelial cells of the respiratory tract [4] . Instead , MV is unusual in that it preferentially targets host immune cells , in particular those expressing the CD150 ( SLAMF1 ) receptor [5 , 6] . This atypical target population includes dendritic cells of the innate immune system , and B and T lymphocytes of the adaptive system [6 , 7] , and thus creates a dynamical feedback whereby the cells responsible for mounting an effective immune response are also under viral predation . Clinical progression of disease initiates in the respiratory tract with infection of dendritic cells and alveolar macrophages [4] . These cells then migrate to lymph nodes and lymphoid tissues , where the virus replicates extensively in resident B and T lymphocytes , and eventually spreads systemically [8] . Therefore , although MV enters ( and exits ) the host via the respiratory tract , its subsequent pathogenesis is markedly different from that of classical respiratory viruses . During viral replication and spread , immune suppression coincides with the substantial depletion of host lymphocytes ( typically from 7 days post infection ( dpi ) and recovering over a timescale of weeks ) [8] . Most MV fatalities result from complications mediated by secondary infection during this period of immune vulnerability [9 , 10] . However , in tandem there is also a rapid expansion of the adaptive immune system around 10–14 dpi , with activation of MV-specific effector T cells and increased antibody production [6 , 11 , 12] . These seemingly contrasting dynamics are often referred to as the ‘measles paradox’ . Although the strong immune response ultimately leads to lifelong protection against reinfection , the interplay between immune cell depletion and activation , and its impact on viral clearance , are not well understood . Conventional wisdom suggests that viral clearance is dominated by the adaptive immune response , in particular by MV-specific CD8+ cytotoxic T cells . Large expansions of these cells have been observed in humans during acute infection , and they have been shown to efficiently clear MV-infected cells in vitro [13–16] . In addition , patients with impaired cellular immunity are less able to control infection , experiencing delays to viral clearance and prolonged viral shedding [17] . These dynamics have also been tested in vivo through experimental infection of macaques , a model system for human infection [18–20] . For example , in Permar et al . ( 2003 ) macaques were depleted of CD8+ T cells prior to , and in the first four days following , MV infection . Compared to control individuals , macaques with depleted T cells experienced higher viral loads and delays to viral clearance from peripheral blood [21] . In contrast , macaques with impaired humoral immunity exhibit similar clinical pathology to control individuals [22] . Together , these findings suggest the CD8+ T cell response is an important factor limiting viral growth during peak viremia . Despite this experimental work , however , questions still remain regarding the impact of target cell depletion on viral dynamics , particularly during early replication in lymphoid tissues . For example , MV is cytopathic and germinal centers of human patients show severe lymphoid exhaustion during the prodromal stages of infection; similar dynamics have also been observed in lymphoid tissues of macaques [8 , 23–26] . Moreover , direct comparisons of viral kinetics in blood and lymphoid tissues have shown that target cells are generally infected at higher rates in the latter; for example up to 10% of peripheral B cells are infected during peak viremia , compared to over 30% in lymphoid tissues [6 , 8] . The true extent of target cell depletion may therefore be underestimated in experiments analyzing only blood measurements . More generally , extreme target cell depletion has been observed during infections of closely related viruses within the same Morbillivirus genus . Ferrets infected with canine distemper virus ( CDV ) can experience infection of up to 80% of peripheral blood mononuclear cells ( PBMC ) during peak viremia and often succumb to disease before any immune response is detected [27] . Target cell limitation is therefore a major component of viral dynamics in similar eco-immunological systems . Although both cellular immunity and target cell depletion may contribute to MV clearance , their relative importance has not been assessed systematically . Due to the complex dynamics of infection , with feedbacks between virus , target cells , and the host immune response , mathematical modeling provides a tractable approach to tease these drivers apart . Most notably , pioneering work modeling the dynamics of HIV ( one of the best-studied lymphotropic viruses ) has demonstrated the importance of both target cell depletion and host immunity in driving viral dynamics . For instance , simple ‘target cell limited’ models ( i . e . models considering only the predation of virus on host target cells ) can capture the early dynamics of infection [28–31] , whereas incorporating CD8+ T cells can improve model predictions following the initial peak in viral load [30 , 32–34] . However , key differences between HIV and MV are that the former infects CD4+ T cells and CD4-expressing macrophages and establishes lifelong infection , whereas the latter targets all CD150+ lymphocytes ( including CD4+ and CD8+ T cells ) and causes acute , self-limiting disease . In general , fewer modeling studies have focused on acute infections due to the difficulty in generating high resolution data over the short timescales relevant for infection . Nonetheless , insights have been gained from particularly well-developed systems such as lymphocytic choriomeningitis virus ( LCMV ) and influenza . For example , models describing influenza A dynamics have revealed the importance of both the innate and adaptive immune responses in driving rapid viral turnover and eventual decline [35–37] . In comparison to LCMV and influenza , modeling acute MV infection poses the additional challenge that activated immune cells are also targets of viral predation . In particular , since MV-specific CD4+ and CD8+ effector T cells are susceptible to infection ( although the latter at a potentially lower rate ) , their proliferation may counterintuitively facilitate viral growth by increasing the target cell population [13] . These potential activation costs have yet to be incorporated within a dynamic framework of MV infection [12 , 38] . To our knowledge , two within-host models have previously been developed for MV infection , but neither accounted for the susceptibility of activated host immune cells . The first model , presented in Heffernan and Keeling ( 2008 ) , assumed that target cells and MV-specific T cells were independent populations , and so the strength of the cellular immune response was unaffected by viral predation . Furthermore , although this framework captured some qualitative dynamics of infection , it was not calibrated against virological or immunological data and so could not be quantitatively verified . In contrast , the subsequent model of Lin et al . ( 2012 ) was calibrated against high resolution data and incorporated a wider array of adaptive immune mechanisms , including viral suppression by MV-specific antibodies and immune suppression by regulatory T cells . However , this model also ignored feedbacks between the virus and host immune cells , and thus did not fully capture the costs of immune activation during MV infection . In addition , the primary focus of the study was the long-term dynamics of MV within the host , rather than the clearance of acute viremia . In this paper , we build on previous work by developing a new mathematical model of MV dynamics that incorporates predatory feedbacks between the virus and host immune cells . We calibrate the model using the virological and immunological data presented in Lin et al . ( 2012 ) and consequently find that a framework including both target cell depletion and cellular immune activation best captures the overall dynamics . By using the calibrated model to simulate the effects of increased target cell availability and suppressed cellular immunity , we find that the latter has a greater detrimental impact on the host’s ability to clear acute infection . This comparison provides quantitative evidence that cellular immunity is the more dominant factor driving viral clearance . Finally , by extending our analysis to incorporate higher viral growth rates , we demonstrate that the model can also reproduce extreme target cell depletion dynamics , such as those more in line with CDV infection in ferrets . In summary , this work presents the first calibrated model of MV dynamics to tease apart key drivers of viral clearance and , more generally , outlines a new quantitative framework to explore the complex dynamics underlying Morbillivirus infection .
The data depict key virological and immunological components of MV infection ( Fig 1 ) and have been described previously [12] . Briefly , seven juvenile rhesus macaques were infected with wild-type MV ( Bilthoven strain ) and monitored for up to 71 dpi . During the course of acute infection , regular blood samples were analyzed for infectious viral load , the number of circulating lymphocytes , and the activity of MV-specific T cells and antibodies . Despite substantial individual variation across the data , there are consistent and striking patterns . First , infectious viral load peaks between 7–10 dpi and coincides with a sharp decline in lymphocyte numbers , characteristic of MV-induced lymphophenia ( Fig 1A and 1B ) . Following peak viral load , there is also a rapid increase in cellular and humoral immune activity , signifying the induction of a strong MV-specific adaptive immune response ( Fig 1C and 1D ) . In particular , the T cell response peaks between 14–18 dpi and coincides with the recovery of the lymphocyte population , whereas the antibody response continues to increase past day 35 . It is also important to note that since T cell activity was measured by ex vivo restimulation of PBMC , the data may underestimate the full potency of the original effector response [39] . Collectively , the distinct balance between viral growth , lymphocyte depletion , and subsequent immune cell stimulation typifies the measles paradox and demonstrates the utility of these data in exploring the role of target cell depletion and immune activation in driving viral clearance . In addition to the classic characteristics of MV infection described above , we also note intriguing kinetics that are not well-described in previous literature . Firstly , at 3 dpi there is a striking and consistent increase in lymphocyte numbers across all individuals ( Fig 1B ) . The biological mechanisms underlying this increase are not fully understood , but it may represent incoming lymphocytes from lymphoid tissues or early activation of peripheral lymphocytes . Secondly , after 35 dpi and the clearance of acute viremia , some individuals also experience a resurgence in lymphocyte and T cell numbers ( e . g . 40V and 67U; Fig 1B and 1C ) . Again , the mechanisms for this recovery are not well-characterized , but it may signal the reactivation of MV-specific immune cells due to long-term persistence of infection [42–44] . We return to these points in the Discussion . To examine the roles of target cells , T cells , and antibodies in driving within-host MV dynamics , three different models were fit to the immunological and virological data: ( i ) a target cell limited model , ( ii ) a model with target cells and activated T cells , and ( iii ) a model with target cells , activated T cells , and antibodies ( see Materials and methods ) . Briefly , we assume host lymphocytes are the principal virus target cells , and divide these into subpopulations of ‘general’ susceptible cells ( such as naive and memory B and T cells ) and MV-specific T cells that become activated in response to viral load . Although epithelial cells in the respiratory tract and skin are infected following viremia , these are not considered primary drivers of virus dynamics and are thus excluded from the current model [6] . Initially , susceptible cells proliferate or become infected by virus . Infected cells then produce replicated virus which can infect other susceptible cells or is removed from the system through natural decay and non-specific immune processes . In model ( iii ) , infectious virus is also cleared by antibodies . Infected cells are removed through natural decay and infection-induced apoptosis , and in models ( ii ) and ( iii ) are also killed by activated MV-specific T cells through direct lysis [11 , 14 , 15 , 45 , 46] . One crucial aspect of MV dynamics is that activated T cells may also be susceptible to viral predation . We therefore allow these cells to become infected , at which point they are assumed to lose all cytotoxic capabilities . For parsimony , we assume activated T cells are equally susceptible to infection as the general population , although in reality there may be nuances of heterogeneity across different lymphocyte subsets [8 , 13] . Finally , activated T cells that escape infection eventually either die , or become memory T cells and join the general susceptible population . The model schematics are depicted in Fig 2 and the corresponding equations are presented in the Materials and methods . Each model was calibrated using the viral load , lymphocyte , and MV-specific T cell data ( Fig 1A–1C ) , and statistical fits were compared using Akaike Information Criterion corrected for small sample sizes ( AICc; see Materials and methods ) . For the majority of individuals , AICc values indicated best support for model ( ii ) ( Table 1 ) , and the corresponding model predictions demonstrated close agreement with the data ( Fig 3 ) . Interestingly , this model also captured the late increases in lymphocyte and T cell numbers by predicting a corresponding resurgence in viral load ( e . g . individual 67U; Fig 3 ) . In contrast , the higher AICc values of model ( iii ) suggest the inclusion of antibodies did not provide a sufficient improvement in fit to warrant the increase in model complexity . Furthermore , although this model produced visually similar fits to the T cell model , it could not capture the late increase in cell numbers ( S2 Fig ) . We therefore conclude that cellular immunity plays an important role in the acute dynamics of infection , whereas antibodies may be less relevant at these relatively short timescales . All subsequent analyses were thus conducted using the target cell and T cell model . To test model parsimony with respect to target cell dynamics , we compared alternative functions governing the activity of MV-specific T cells and proliferation of the general susceptible population ( see Materials and methods ) . For T cell activation , we chose a saturating function of viral load ( Eq 5 ) that allows the activation and proliferation of T cells to dominate the immune response when viral load is high , and contraction and memory conversion to dominate once viral load has declined [47 , 48] . Eq 5 has been previously employed for LCMV and has the advantage that T cell activation and contraction is described as a continuous dynamical process [47] . This function had greater statistical support than alternative expressions describing constant activation within a discrete time window , or activation that was proportional to viral load ( S1 Table and S13 Fig ) . All subsequent analyses were therefore conducted using Eq 5 . To capture the early activation of general lymphocytes , we explored a model in which proliferation was of temporary duration , td ( where td was estimated during the fitting process ) ( Eq 6 ) . Although simpler functions omitting proliferation , or allowing constant proliferation , had better statistical support ( S2 Table and S14 Fig ) , the corresponding visual fits were relatively poor ( see for example S3 Fig ) . In particular , the simpler models could not capture the initial increase in lymphocyte numbers observed at three dpi ( S4 Fig ) . For the remainder of the paper we therefore use Eq 6 , but note that this choice does not impact our general conclusions . For instance , when proliferation is omitted , we still find that the model with target cells and T cells outperforms the corresponding target cell only and target cell , T cell , and antibody models ( S3 Table ) . Parameter estimates for the best-fitting target cell and T cell model ( Eqs 1–6 ) are presented in Table 2 . As a measure of within-host viral fitness , the basic reproduction number ( the average number of cells infected by one virus-infected cell in a fully susceptible population ) was calculated from these estimates [49] . Note that here we distinguish between R0 , the reproduction number in the absence of an initial immune response , and R 0 * , the corresponding expression when activated T cells are present . Despite variation amongst individuals , the parameters governing competition between viral growth and cellular immunity ( e . g . R0 , R 0 * , and the T cell proliferation and killing rates , q and k ) were relatively well-conserved . To account for underestimation of the effector T cell response , a scaling factor , ψ , was included when fitting the model to the MV-specific T cell data ( see Materials and methods ) . The resulting estimates were large and likely driven by the rapid recovery of total lymphocytes following lymphopenia . More specifically , since the predominant mechanism for lymphocyte expansion is through proliferation of activated T cells ( A ) , capturing lymphocyte recovery required a greater expansion of these cells than was measured in the data . Finally , using fitted parameters from Table 2 we also estimated the half-life of an infected cell due to T cell killing , which is ln ( 2 ) /kA and changes over the course of infection . Across individuals , these half-lives ranged from 1 . 9–8 . 4 hours at peak viral load to 6 . 2–28 minutes at the height of the T cell response . To investigate the impact of parameter imprecision on model outcome , additional uncertainty and sensitivity analyses were conducted ( see Materials and methods ) . Although the uncertainty analysis indicated substantial variation in model output for different parameter values ( S7 Fig ) , subsequent sensitivity estimates suggested this was largely driven by a subset of key parameters , namely: the viral decay rate ( c ) , the death rate of infected cells ( δ ) , the viral replication rate ( p ) , and , to a lesser extent , the T cell killing rate ( k ) ( S8 Fig ) . By bootstrapping model residuals we also found parameters that were more conserved across individuals ( e . g . R0 , R 0 * , q ) tended to have tighter confidence intervals , but a greater degree of correlation with other parameters ( see for example S5 and S6 Figs ) . Together these results suggest that although many parameter combinations may provide similar qualitative fits to the data , the underlying balance between virus and immune cell competition is generally conserved . To explore drivers of viral clearance , we first qualitatively assessed the immunological and virological dynamics predicted by the best-fitting model . In all individuals , the sharp decline in susceptible target cells ( up to 85% ) coincided with the timing of peak viral load ( shaded red regions , Fig 4A and 4D ) , and preceded the dramatic expansion of activated T cells ( Fig 4C ) . One may therefore hypothesize that target cell depletion mediates viral dynamics during the initial turnover phase , before the cellular immune response is fully activated . Together with the previous AICc comparisons , these qualitative assessments suggest respective roles for both cellular immunity and target cell depletion in driving viral decline . To extend our qualitative assessments , we conducted two additional simulation experiments to quantitatively compare the impact of cellular immunity and target cell depletion on viral dynamics ( see Materials and methods ) . First , to test the importance of cellular immunity , we recreated the T cell depletion experiment conducted by Permar et al . ( 2003 ) . This involved reducing the initial population of activated T cells to low levels and then suppressing subsequent activation and proliferation until day four . Secondly , to test the importance of target cell depletion we simulated the addition of new susceptible cells to the lymphocyte pool one day after the occurrence of peak viral load . Results from both experiments were then compared to control simulations from the original model ( i . e . Fig 4 ) . In all cases we found that the depletion of activated T cells caused greater increases in peak viral load , and delays to viral clearance , than the addition of new target cells ( Fig 5 ) . To systematically compare the impact of each simulation experiment across individuals , we measured the relative change in predicted viral load between each control and experimental simulation using Eq 7 ( see Materials and methods and S1 Fig ) . For all macaques , T cell depletion resulted in a greater change in viral load than target cell addition , despite some individual variation in the magnitude of these changes ( Fig 6A ) . These general findings were robust to changes in experimental conditions , including the initial number of activated T cells and duration of T cell depletion , and the magnitude and timing of target cell addition ( S9 and S10 Figs ) . Our findings were also preserved when using the alternative lymphocyte proliferation functions that gained better statistical support ( i . e . when we omitted proliferation , or allowed constant proliferation; see S15 Fig ) . Overall , these results suggest that cellular immunity is the more dominant factor driving clearance of acute MV infection . Finally , we tested the versatility of the model by exploring its capacity to capture the extreme target cell depletion dynamics observed during other Morbillivirus infections , such as CDV . To do this , the above experiments were first repeated with an increased viral replication rate , p ( see Materials and methods ) . This choice was guided by observations that viral load peaks earlier in macaques infected with CDV than MV , indicative of a higher viral growth rate [50] . Increasing the replication rate resulted in all individuals experiencing a greater impact of target cell addition than T cell depletion ( Fig 6B ) , signaling a qualitative switch to dynamics driven by target cell limitation . Extending this analysis to other model parameters suggested that those relating to within-host viral fitness ( i . e . the transmission rate , β , and the decay rate , c ) and direct immune cell predation ( the T cell killing rate , k ) were also capable of producing this stark effect ( S11 Fig ) . Although preliminary , these results suggest an important role for virus fitness phenotypes in determining ultimate drivers of Morbillivirus clearance .
In conclusion , this paper presents the first within-host model of MV dynamics to incorporate predatory feedbacks between the virus and host immune system . By calibrating this model against virological and immunological data , we have provided insight into the roles of adaptive immunity and target cell depletion in mediating viral clearance . Although we note caveats associated with model parameterization and parsimony , these could be alleviated by future experimental advances and targeted data collection . In particular , efforts should focus on independent estimation of key parameters , and characterization of dynamics within different lymphocyte subsets . More broadly , comparing MV dynamics with CDV may provide further insights into mechanisms of viral clearance across the Morbillivirus genus .
The within-host target cell and T cell model is a system of ordinary differential equations given by d S d t= - β S V + q s δ ^ ( t ) S + r ( 1 - f ( V ) ) A ( 1 ) d I d t= β ( S + A ) V - δ I - k I A ( 2 ) d A d t= q f ( V ) A - β A V - ( 1 - f ( V ) ) ( d + r ) A ( 3 ) d V d t= p I - c V , ( 4 ) where f ( V ) = V s + V ( 5 ) for some saturation constant , s , and δ ^ ( t ) = { 1 if t < t d , 0 if t ≥ t d . ( 6 ) As described above , host lymphocytes are initially divided into subpopulations of ‘general’ susceptible cells ( S ) and MV-specific activated T cells ( A ) . The former includes naive and memory B and T cells , whereas the latter represents T cells that will become activated in response to viral infection . As the virus ( V ) enters the host , susceptible cells become infected ( I ) and produce replicated virus at per-capita rate p . Virus produced by these cells then infects more cells ( at rate βV ) or is removed from the system through natural decay and non-specific immune processes ( c ) . During the first td days of infection , susceptible cells proliferate ( qs ) and , in response to viral load , MV-specific activated T cells proliferate ( q ) and kill infected cells through direct lysis ( k ) . Infected cells are also removed from the system through natural and infection-induced decay ( δ ) . Since activated T cells may also be susceptible to viral predation , we allow these cells to become infected , at which point they lose all cytotoxic capabilities . Finally , activated T cells that aren’t infected either die ( d ) , or become memory T cells ( r ) and join the general susceptible population . The accumulation of these MV-specific memory T cells ( M ) can be predicted using the expression dM/dt = r ( 1 − f ( V ) ) A . Note that we assume the birth and death of susceptible cells over the short time span of acute viremia is small relative to infection-induced cell proliferation and death . We therefore omit these dynamics to preserve model parsimony . The saturation function , f ( V ) , was chosen to allow activation and proliferation of T cells to dominate the immunological response when viral load is high , and for contraction and memory conversion to dominate once viral load has declined [47 , 48] . We also investigated alternative functions , including activation within a discrete timeframe and proliferation that was proportional to viral load ( S1 Appendix and S13 Fig ) , but both resulted in poorer statistical fits to the data . We note that while infected cells produce both infectious and non-infectious virus particles , their relative roles in immune activation have yet to be determined . However , our general conclusions remain unchanged even if an ( unknown ) proportion of virus is non-infectious but still contributes to T cell activation . In this case , the saturation function is f ( αV ) , where α is the ( assumed constant ) proportion of total virus that is immunostimulatory , and can be absorbed into the saturation parameter , s . Similarly , the proportion of virus that is infectious is contained within the transmission parameter , β . The function governing lymphocyte proliferation , δ ^ ( t ) , was chosen to capture the temporary increase in lymphocyte numbers arising from early activation . As the mechanisms underlying this dynamic are poorly understood , we also explored two simpler scenarios , one with constant proliferation ( i . e . δ ^ ( t ) = 1 , ∀ t ) , and another with no proliferation ( i . e . δ ^ ( t ) = 0 , ∀ t ) . Further details of these modifications can be found in the S1 Appendix and S14 Fig . The model described by Eqs 1–6 incorporates the dynamics of target cell depletion and immune activity through the action of MV-specific killer T cells . However , we also investigated two alternative immunological scenarios . In the more complex scenario , we incorporated the humoral immune response by allowing MV-specific antibodies to provide additional viral clearance . This was achieved by modifying the viral load equation to d V d t = p I - c V - θ Y V , where Y ( t ) represents the MV-specific IgG response ( titer × avidity ) at time t , and is input directly from the antibody data ( Fig 1D ) . In the simpler scenario , we created a target cell limited model by removing all terms relating to direct immune-mediated viral clearance ( i . e . the antibody clearance term , θYV , and the cytotoxic T cell term , kIA ) . In the absence of an adaptive immune response , the basic reproduction number for this model ( the average number of cells infected by one virus-infected cell in a fully susceptible population ) is given by R0 = pβS0/cδ , where S0 is the initial number of susceptible cells . However , when activated T cells are present at initial infection ( i . e . A0 ≠ 0 ) , R 0 * = p β ( S 0 + A 0 ) / c ( δ + k A 0 ) [49] . Note that innate immunity was omitted from all models . In addition to preserving model parsimony , this choice was motivated by experimental evidence that MV inhibits innate immune functioning , in particular the production of IFN-α and IFN-β [61] . A list of all model parameters is given in Table 3 and the model schematics are illustrated in Fig 2 . The model was calibrated using the viral load , lymphocyte , and MV-specific T cell data ( Fig 1A–1C ) . Specifically , the viral load data were used to fit the virus compartment ( V ) , the total lymphocyte data to fit the sum of the model lymphocyte compartments ( S + I + A ) , and the MV-specific T cell data to fit the activated T cell compartment ( A ) . Since Lin et al . ( 2012 ) expressed the original T cell data in cells per million PBMC , these were first transformed to the scale of the lymphocyte data ( cells per microliter ) by using PBMC per microliter measurements from the original study . During the fitting process , the T cell data were also scaled by a constant , ψ ≥ 1 , to account for underestimation of T cell activity during the ex vivo restimulation assay . For the model incorporating antibody-mediated viral clearance , we interpolated the MV-specific antibody data ( Fig 1D ) to approximate Y ( t ) at every time , t . Initial conditions for the total number of lymphocytes were obtained directly from the lymphocyte data , of which we assumed there were no infected cells ( i . e . I ( 0 ) = I0 = 0 ) . The initial viral load and number of activated T cells ( V0 and A0 , respectively ) were estimated during the fitting process , and the initial number of susceptible cells ( S0 ) was defined as the difference between the total number of lymphocytes and activated T cells . The differential equations of each model ( ( i ) target cells only , ( ii ) target cells and T cells , and ( iii ) target cells , T cells , and antibodies ) , were solved numerically in R using the deSolve package [62] . Following previous studies , the subsequent viral load , activated T cell , and total lymphocyte predictions were fit to the corresponding data by maximum likelihood optimization in R , assuming normally-distributed residuals between the log-transformed data and model predictions [36 , 37 , 63] . Results in the main text are reported from models fit independently to each individual . However , fitting the models to pooled data across all individuals gave qualitatively similar results ( e . g . S16 Fig ) . Further details of the fitting procedure can be found in the S1 Appendix . The statistical support for each model was compared using Akaike Information Criterion corrected for small sample sizes ( AICc ) [64 , 65] . This metric assesses goodness of fit whilst also penalizing models with more parameters . The AICc value for each model is given by AIC c = 2 k ^ - 2 ln L ^ + 2 k ^ ( k ^ + 1 ) n - k ^ - 1 , where L ^ is the maximum likelihood estimate , k ^ is the number of estimated parameters , and n is the number of data points available for fitting . Lower AICc values indicate a more parsimonious fit and were thus used to identify the best-fitting model . Approximate credible intervals on parameter estimates were obtained by bootstrapping model residuals with 500 replicates . These replicates were also used to estimate pairwise parameter correlations . Additional uncertainty and sensitivity analyses were undertaken using Latin Hypercube sampling ( LHS ) and partial rank correlation ( PRC ) methods [66] . In combination , these techniques assess how imprecision in input parameters impacts model predictions , and can identify parameters with the greatest influence on model outcome . Such methods have been described previously with respect to HIV transmission models [66] , and further details of the implementation in this study are given in the S1 Appendix . To compare the impact of target cell depletion and cellular immune activity on viral clearance we conducted two additional simulation experiments with the best-fitting model . First , to test the importance of cellular immunity we recreated the T cell depletion experiment conducted by Permar et al . ( 2003 ) . Specifically , we reduced the initial population of activated T cells to low levels ( A0 = 1×10-1 cells/μl ) and then suppressed subsequent activation and proliferation until day four . Results were then compared to control simulations with no restrictions on T cell activity . We hypothesized that a delay to viral clearance , as observed in the original experiments [21] , would signal an important role for cellular immunity in mediating viral decline . Secondly , to test the importance of target cell depletion we performed additional simulations in which new susceptible cells ( Snew = 2000 ) were added to the lymphocyte pool one day after the occurrence of peak viral load . We hypothesized that if target cell availability limits viral growth and contributes to eventual decline , adding new cells should release this restriction and allow viral load to increase again . To compare the impact of each simulation experiment across individuals , we measured the relative change in predicted viral load , ΔV . This was defined as the difference in the area under the curve ( AUC ) between the experimental ( VE ) and control ( VC ) simulations , normalized by the AUC of the control simulation i . e . Δ V = ∫ 0 t T V E d t - ∫ 0 t T V C d t ∫ 0 t T V C d t , ( 7 ) where tT is the total simulation time ( for an illustration see S1 Fig ) . To test the sensitivity of our results to experimental conditions , we repeated the above analysis whilst varying key parameters in each simulation . For the T cell depletion experiment we changed the initial number of activated T cells and the duration of T cell suppression , and for the target cell addition experiment we varied the number of new target cells added and the timing of this addition . Finally , we tested the versatility of the model by exploring its capacity to capture the extreme cell depletion dynamics observed in other Morbillivirus systems , such as CDV . More specifically , we repeated the above analysis whilst changing certain parameter conditions to favor viral fitness over cellular immunity . This choice was guided by observations that viral load peaks earlier in macaques infected with CDV than MV , indicative of a higher viral growth rate [50] . For each new condition , one parameter from the original best-fitting model was modified , and the control and experimental simulations were performed as described above . Recalculating ΔV then provided a new comparison of the impact of target cell depletion and cellular immunity on viral clearance . To simplify the analysis we first chose parameter modifications that would double R0 , and therefore increase the within-host viral fitness by a biologically substantive degree . This included scenarios which doubled the replication rate , p , doubled the transmission rate , β , or halved the viral decay rate , c . For comparative purposes , the investigation was then extended to other parameters mediating the strength of cellular immunity . This included halving the T cell killing rate , k , and proliferation rate , q . Parameters that led to the greatest influence of target cell depletion on viral dynamics were identified from the final set of comparisons . | Measles is a highly contagious virus from the Morbillivirus genus that induces a strong adaptive immune response , despite also causing transient immunosuppression . How viral clearance is achieved amidst these seemingly contradictory dynamics ( the so-called ‘measles paradox’ ) is poorly understood . To identify key factors driving viral clearance , we fit a mathematical model to data describing measles virus infection in rhesus macaques . In particular , our model incorporates target cell depletion through infection of immune cells—a hallmark of measles pathology neglected from previous models . The model accurately captures the data , and , by simulating effects of increased immune cell depletion and recovery , we find that cellular immunity is a stronger mediator of viral clearance than target cell depletion . Interestingly , we find contrasting dynamics dominated by target cell depletion when viral fitness is increased . This may have wider implications for closely related animal morbilliviruses that cause fatal target cell depletion in their natural hosts . | [
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"vert... | 2018 | Modeling the measles paradox reveals the importance of cellular immunity in regulating viral clearance |
The apicomplexan , Cryptosporidium parvum , possesses a bacterial-type lactate dehydrogenase ( CpLDH ) . This is considered to be an essential enzyme , as this parasite lacks the Krebs cycle and cytochrome-based respiration , and mainly–if not solely , relies on glycolysis to produce ATP . Here , we provide evidence that in extracellular parasites ( e . g . , sporozoites and merozoites ) , CpLDH is localized in the cytosol . However , it becomes associated with the parasitophorous vacuole membrane ( PVM ) during the intracellular developmental stages , suggesting involvement of the PVM in parasite energy metabolism . We characterized the biochemical features of CpLDH and observed that , at lower micromolar levels , the LDH inhibitors gossypol and FX11 could inhibit both CpLDH activity ( Ki = 14 . 8 μM and 55 . 6 μM , respectively ) , as well as parasite growth in vitro ( IC50 = 11 . 8 μM and 39 . 5 μM , respectively ) . These observations not only reveal a new function for the poorly understood PVM structure in hosting the intracellular development of C . parvum , but also suggest LDH as a potential target for developing therapeutics against this opportunistic pathogen , for which fully effective treatments are not yet available .
Cryptosporidium parvum is a gastrointestinal parasite that can cause moderate to severe diarrhea in children and adults , and deadly opportunistic infection in AIDS patients [1 , 2] . In addition , because Cryptosporidium oocysts are resistant to chemical stresses , such as chlorine treatment , it also frequently causes water-borne outbreaks around the world [3 , 4] . Current treatment options for cryptosporidiosis are limited [1 , 5] . In the United States , only nitazoxanide is approved by the Federal Drug Administration ( FDA ) to treat cryptosporidial infections in immunocompetent individuals , but this is not approved for immunocompromised patients [6–8] . Cryptosporidium shares many biological features with other apicomplexans . They all undergo similar stages of life cycle development , including the invasion of sporozoites into host cells after excystation from oocysts , followed by varied cycles of merogony to form merozoites , gametogenesis to form micro- and macro-gametes , fertilization , and oocyst formation . The sporozoites and meorzoites also contain an apical complex consisting of a number of unique cytoskeletal structures and secretory organelles , such as rhoptries and micronemes . During the intracellular development , Cryptosporidium and most other apicomplexans reside within a vacuole termed parasitophorous vacuole , although some escape from the vacuole shortly after invasion ( e . g . , Theileria and Babesia ) . The vacuole is formed during the parasite invasion and defined by a host cell-derived membrane structure termed parasitophorous vacuole membrane ( PVM ) . However , Cryptosporidium also differs from other apicomplexans in that these parasites lack both an apicoplast and a typical mitochondrion , and are incapable of the de novo synthesis of amino acids , fatty acids , and nucleosides . Additionally , they undergo a unique intracellular , but extracytoplasmic development , in which the PVM faces the extracellular environment , rather than the host cell cytosol [9–11] . Energy metabolism in some members of the cryptosporidia lacks both the Krebs cycle and the cytochrome-based respiration chain ( e . g . , C . parvum and C . hominis ) ; whereas , others possess only the former but lack the latter ( e . g . , C . muris ) . Therefore , this genus of parasites relies mainly , if not solely , on glycolysis to produce ATP . To facilitate this “anaerobic metabolism” , Cryptosporidium possesses an L-lactate dehydrogenase ( LDH ) [EC 1 . 1 . 1 . 27] , two alcohol dehydrogenases ( ADHs ) , and an acetyl-CoA synthetase , which potentially produce lactic acid , alcohol , or acetic acid as organic end products [9] . Among these enzymes , LDH is known to be of the bacterial-type , likely derived from malate dehydrogenase ( MDH ) by a very recent gene duplication event [12] . LDH is considered to be a drug target in some parasites , including the apicomplexans Plasmodium and Toxoplasma , and in the anaerobic protozoa , Giardia lamblia , Trypanosoma cruzi , and Entamoeba histolytica [13 , 14] . In the present study , we show that the C . parvum LDH ( CpLDH ) protein is distributed in the cytosol of free sporozoites and merozoites , but is then transferred to the PVM during intracellular development , indicating that in this parasite , the PVM is involved in lactate-fermentation . We also characterized the enzyme kinetic features of CpLDH and demonstrate that two known LDH inhibitors , gossypol and FX11 , can inhibit both enzymatic activity and parasite growth in vitro .
We have previously used C . parvum microarray and qRT-PCR to show that the CpLDH gene is highly expressed in oocysts and free sporozoites , suggesting that pyruvate fermentation might be critical to these extracellular parasite stages [15] . To determine whether CpLDH is a metabolically active enzyme in the parasite , we measured the levels of lactate released by C . parvum oocysts and free sporozoites . We detected levels ranging from 3 . 1–14 . 4 nmol per 107 oocysts or per 4×107 sporozoites when these are incubated at 37°C for 1 to 4 h ( Fig 1 ) , confirming that lactate is released by C . parvum oocysts and sporozoites . A longer 4 h incubation increased the amount of lactate released by free sporozoites by 2 . 5-fold ( i . e . , from 5 . 8–14 . 4 nmol ) , but not by oocysts ( i . e . , from 3 . 15–3 . 29 nmol ) , suggesting that free sporozoites , after being excystated from oocysts , are more metabolically active than oocysts . Based on the size of sporozoites ( ~1×5 μm ) , we estimated that intracellular lactate concentrations in sporozoites could range from 19–91 mM if this metabolite is not released from , but rather , accumulates in the parasite ( vs . ~1 . 3 mM in human normal bloods [16] ) . To investigate the distribution of the CpLDH protein in the parasite , we produced a rabbit polyclonal antibody against a CpLDH-specific peptide and a rat polyclonal antibody against the recombinant CpLDH protein . Antibodies were affinity-purified using the corresponding antigens ( i . e . , peptide and recombinant maltose-binding protein ( MBP ) -CpLDH fusion protein ) . Western blot analysis showed that neither antibody was cross-reactive with any host cell proteins , and both were able to recognize recombinant CpLDH protein . These antibodies detect bands at ~37 kDa and at ~34 kDa from whole proteins extracted from free sporozoites ( Fig 2 ) . These protein sizes agree with the theoretical masses of native CpLDH ( 33 . 9 kDa ) and the recombinant CpLDH containing a few extra linker amino acids ( 37 kDa ) . MBP protein was labeled by the rat antibody , but not by the rabbit antibody , as the former was produced by immunizing rabbits with uncleaved MBP-CpLDH in first immunization , and subsequently with cleaved CpLDH in booster shots , while the later was raised against a peptide antigen . The presence of antibodies recognizing the bacterial MBP in the rat serum would not , however , complicate the subsequent immunofluorescence staining , due to the lack of MBP protein in parasite and human cells . Affinity-purified pre-immune sera produced no signals in all samples ( Fig 2 ) . These observations confirm the antibody specificity and the presence of the CpLDH protein in the parasite . Immunofluorescence microscopy using the rabbit antibody detected CpLDH in the cytosol of sporozoites within oocysts , in free sporozoites , and in merozoites ( Fig 3A ) . However , during parasite intracellular development , CpLDH was found to be associated with the PVM ( Fig 3B ) . While weak fluorescence signals were sometimes observed in intracellular parasites , these signals were insignificant in comparison with those from the PVM . No signals were observed in parasites or in host cells when using pre-immune sera ( S1 Fig ) . We also performed immunofluorescence labeling using primary rabbit antibody that was presoaked with either synthetic peptide antigen , recombinant CpLDH , or MBP . We found that pretreatment with recombinant CpLDH protein or synthetic peptide antigen completely eliminated labeling of the PVM; whereas , presoaking with MBP had no effect on PVM labeling ( Fig 3C ) . Similarly , the rat antibody also labeled proteins on PVM , and these signals could be eliminated by presoaking the antibodies with the CpLDH protein , but not with MBP ( Fig 3D ) . The distribution of CpLDH in the PVM was further confirmed by immunoelectron microscopy , in which the majority of the colloidal-gold particles were observed along the inner side of the PVM ( Fig 4A ) . In agreement with the immunofluorescence microscopy data , some gold particles were also present in intracellular parasites , but these were much fewer than those on , or near , the PVM . When gold particles were manually counted and expressed as number of particles/μm2 , 71 . 2% ( ± 9 . 0% ) and 70 . 8% ( ± 6 . 0% ) of the total particles were present on or along the inner side of PVM when stained with rabbit and rat antibodies ( vs . 21 . 7%–19 . 3% in the parasite merozoites , 5 . 3%–4 . 5% in the parasitophorous vacuolar space and 1 . 8%–5 . 4% in the host cells ) ( Fig 4B ) . To determine the biochemical properties of CpLDH , this enzyme was expressed as an MBP-fusion protein and purified to homogeneity ( Fig 5B , inset ) . We observed that recombinant CpLDH was capable of catalyzing reactions in both directions ( i . e . , from pyruvate to lactate and vice versa ) . However , it favors the conversion of pyruvate to lactate , showing a >25-fold smaller Km and a >24-fold larger Vmax on pyruvate than on lactate ( Table 1; Fig 5A–5F ) . This protein also displayed the highest activity near neutral pH values and preferred using NADH/NAD+ to NADPH/NADP+ ( Fig 5A and 5B ) . CpLDH was found to act as an allosteric enzyme , with positive cooperativity on NADH ( Hill coefficient = 2 . 4 ) ( Fig 5D ) , but displayed typical Michaelis-Menten kinetics on pyruvate , lactate , and NAD+ ( Fig 5C , 5E and 5F ) . The kinetic parameters we observed for this enzyme were comparable to those reported for the LDHs from Plasmodium falciparum and Toxoplasma gondii [17–19] . We next evaluated the effects of two known LDH inhibitors , gossypol and FX11 , on the enzymatic activity of CpLDH . Gossypol is a polyphenolic compound derived from cotton plants that acts as an inhibitor of LDHs , including those from humans , Plasmodium , and Toxoplasma [20 , 21] , while FX11 was recently discovered to be a selective inhibitor of human LDH-A , displaying Kis on HsLHD-A and HsLDH-B of ~8 μM and >90 μM , respectively [22] . We found that both compounds acted as noncompetitive inhibitors on CpLDH , with respect to NADH ( Fig 6 ) . For gossypol , the Ki and IC50 values were 14 . 8 μM and 21 . 0 μM , respectively ( Table 2 ) . This Ki value was slightly larger than , but comparable to , those reported for PfLDH ( 0 . 7 μM ) , TgLDHs ( 1 . 1–6 . 1 μM ) , and human LDHs ( 1 . 4–4 . 2 μM ) [17 , 18 , 20] , suggesting that , similar to PfLDH and TgLDHs , CpLDH is sensitive to inhibition by gossypol , and possibly to derivatives of this compound as well . FX11 was also able to inhibit CpLDH enzymatic activity at low micromolar levels , but it was less effective on CpLDH than on HsLHD-A ( i . e . , the Ki and IC50 values on CpLDH were 55 . 6 μM and 65 . 0 μM , respectively ) ( Table 2 ) . We further tested the effect of gossypol and FX11 on the in vitro growth of C . parvum in HCT-8 cells using a qRT-PCR assay that we previously developed [15 , 23] . We performed 44 h infection assays , in which oocysts were incubated for 3 h in a drug-free medium to allow excystation and invasion before inhibitors were added into the culture via a medium exchange . Subsequently , we found that both inhibitors exhibit anti-cryptosporidial activity at low micromolar concentrations ( i . e . , IC50 for gossypol and FX11 were 11 . 8 μM and 39 . 6 μM , respectively ) ( Fig 7A and 7B ) . The positive control , paromomycin at 140 μM , inhibited parasite growth by 80 . 3% as expected ( Fig 7B , inset ) . Gossypol and FX11 both displayed low to moderate cytotoxicity on cancerous HCT-8 host cells at all tested concentrations; their IC50 values on HCT-8 cells , as determined by MTT assay , were 51 μM and 87 μM , respectively ( Table 2 ) . The anti-cryptosporidial IC50 value for gossypol was comparable to those reported for the same compound against P . falciparum ( 7–13 μM ) and T . gondii ( between 5–10 μM ) [18 , 24] . Conversely , to the best of our knowledge , this is the first report of anti-parasitic activity for FX11 . The anti-cryptosporidial efficacy of FX11 was lower than that of gossypol , which was well-correlated with their Ki values on CpLDH ( Table 2 ) . To evaluate the effects of inhibitors on individual asexual developmental stages , gossypol ( 16 μM ) and FX11 ( 60 μM ) were applied to intracellular parasites between 3 to 20 h post-infection ( hpi ) ( corresponding to the first generation of merogony ) , and between 20 to 44 hpi ( corresponding to the second generation of merogony and some gametogenesis ) . In this assay , both asexual developmental stages could be inhibited , but the efficacies were higher against the second generation of merogony development ( Fig 7C ) . We observed that pretreatment of excystated sporozoites and type I merozoites with 16 μM gossypol for 40 min at room temperature reduced parasite invasion ( 3-hpi assay ) by 17% and 25% , respectively ( Fig 7D ) . The reductions were statistically significant ( p<0 . 05 by Student’s t-test ) , but less dramatic than expected considering that CpLDH is expressed at the highest levels in these motile stages . This might be explained by the fact that: 1 ) gravity could help sporozoites and merozoites settle down to the bottom of the culture wells to reach the host cells , so parasites might only need minimal energy to reach to the host cells , 2 ) the invasion process was rapid , and/or 3 ) the CpLDH levels were too high to be significantly inhibited by gossypol . It is possible that gossypol and FX11 could also inhibit host cell LDH and other metabolic pathways , which might in turn affect parasite infection and growth . To address this possibility , HCT-8 cells were pretreated with gossypol ( 16 μM ) for 24 h and then infected with C . parvum sporozoites in the absence of inhibitor . We observed that pretreatment of host cells with 16 μM gossypol had no inhibitory effect on parasite infection ( Fig 7E ) . Rather , a slight , albeit non-statistically significant , increase in parasite infection was observed . Because the glycolytic pathway of cancer cells might be more susceptible to modulation by LDH inhibitors , we performed a 44-h infection assay using FHs 74 Int primary fetal enterocytes ( ATCC #CCL-241 ) . Here , we observed that gossypol ( 10 μM ) and FX11 ( 60 μM ) inhibited the growth of C . parvum by 67 . 7% and 45 . 3% , respectively ( Fig 8 ) . This primary cell line is a highly valuable alternative to the cancer cells for studying Cryptosporidium infection in vitro . However , the conditions for growing C . parvum in FHs 74 Int cells require further optimization , as parasite growth was much less efficient in these cells than in the commonly used HCT-8 and Caco-2 cells .
In the present study , we found that the CpLDH protein is cytosolic during the motile , extracellular , stages of parasite growth , but associates with the PVM during the intracellular development . The PVM is a unique membrane structure , which hosts the intracellular development of apicomplexan parasites and facilitates interactions between these parasites and host cells . The Cryptosporidium PVM is unique from those of other apicomplexans , such as Plasmodium , Toxoplasma , and Eimeria , in that it localizes on the top of host cells , rather than in the cytosol . Therefore , Cryptosporidium is an intracellular , but extracytoplasmic , parasite . The protein composition of the cryptosporidial PVM is poorly understood . Currently , only a few proteins involved in fatty acid metabolism have been localized to the PVM , such as the long-type fatty acyl-CoA binding protein ( CpACBP ) [25] , an oxysterol-binding protein-related proteins ( CpORP1 ) [25] , and the long chain fatty acid elongase ( CpLCE ) [26] . The discovery that CpLDH is a PVM-association protein suggests that this unique structure participates not only in fatty acid metabolism , but also in lactate fermentation . Since lactate cannot be reutilized by Cryptosporidium , we speculate that the PVM localization of CpLDH facilitates the quick release of lactate into host cells and/or the environment to eliminate the potential detrimental effect caused by the accumulation of this compound in the parasite . We also characterized the biochemical activity of CpLDH and observed that the LDH inhibitors gossypol and FX11 also inhibit both CpLDH enzymatic activity and the growth of C . parvum in vitro at low micromolar concentrations . Gossypol and FX11 are mammalian LDH inhibitors and have been explored as potential therapeutics against cancer cells , which rely more heavily on aerobic glycolysis to survive [27] . Of the two , gossypol may act on other targets in host cells , including steroid dehydrogenase , telomerase , calcineurin phosphatase , aromatase , ribonucleotide reductase arachidonate lipoxygenases , adenylate cyclase , catechnol-O-methyltransferase , phospholipase A2 , and apurinic/apyrimidinic endonuclease 1/redox enhancing factor-1 ( APE1 ) [28–35] . FX11 was recently discovered as a selective LDH-A inhibitor [22 , 36] . Because many cancer cells mainly rely on LDH-A , inhibition by FX11 is considered to be an achievable , and tolerable , treatment for LDH-A-dependent tumors . In the case of HCT-8 cells , which are commonly used for studying Cryptosporidium infection in vitro , the mRNA level of LDH-A is >5-fold higher than that of LDH-B ( Fig 9 ) . Therefore , it is possible that gossypol and FX11 might inhibit the growth of C . parvum in vitro not only via direct action on the parasite LDH , but also by altering the host cell metabolism . However , this seems unlikely as pretreatment of host cells with gossypol at 16 μM for 24 h had no effect on the growth of C . parvum ( Fig 7E ) , suggesting that the physiological changes induced by gossypol only minimally contribute to its anti-Cryptosporidium activity . Additionally , we also observed anti-Cryptosporidium activity for both gossypol and FX11 when using FHs 74 Int primary cells , which are less sensitive than cancer cells to the inhibition of LDH activity ( Fig 8 ) . Collectively , these observations indicate that the anti-Cryptosporidium activity of gossypol and FX11 is largely due to their inhibitory activity against CpLDH . It is understood that the ultimate validation of CpLDH as a drug target can only be achieved using genetic tools for knockout or knockdown of genes of interest in Cryptosporidium . More recently , the first successful genetic modification of C . parvum was reported using CRISPR/Cas9 [37] . Although the current transfection system requires to propagate parasites in mice and remains to be further developed into a routine technique , it raises a hope that CpLDH and other potential drug targets in C . parvum may be genetically validated in the very near future . It is also noticeable that the observed anti-Cryptosporidium activities of gossypol and FX11 in vitro are not strong enough for drug development of these specific compounds . Nonetheless , the present data , together with the fact that C . parvum relies on glycolysis for producing ATP due to its lack of the Krebs cycle and cytochrome-based respiratory chain , support the notion that the bacterial-type CpLDH is worth exploring as a potential target for the development of anti-cryptosporidial therapeutics .
The IOWA-1 strain of C . parvum was used in all experiments . Parasite oocysts were purchased from Bunch Grass Farm ( Deary , Idaho , USA ) and stored in phosphate-buffered saline ( PBS ) at 4°C until use . Oocysts were treated with 10% Clorox in ice for 8 min , washed 5–8 times with water by centrifugation , and purified by a Percoll gradient centrifugation protocol [38] . For all parasite experiments , we used oocysts that were less than 3 months old ( since harvest ) . Free sporozoites were prepared by an excystation procedure , in which oocysts were incubated in PBS ( pH 7 . 5 ) containing 0 . 25% trypsin and 0 . 5% taurodeoxycholic acid at 37°C for 1 h , followed by three or more washes with PBS . Free merozoites ( type I ) were collected from the culture medium after in vitro culture of parasites for ~20 h in HCT-8 cells ( also see more detailed in vitro cultivation procedures below ) . Rabbit anti-CpLDH antibody was prepared commercially in two specific pathogen-free rabbits , using a CpLDH-specific synthetic peptide , 297KLLGESINEVNTIS310 , conjugated to keyhole limpet hemocyanin at the N-terminus as antigen , by a standard immunization protocol ( GenScript [Piscataway , NJ] ) . A second anti-CpLDH antibody was produced in five specific pathogen-free rats using recombinant CpLDH protein as antigen by Alpha Diagnostic International ( San Antonio , TX ) , in which the first immunization were performed with the MBP-CpLDH fusion protein , and booster shots were administered with cleaved and purified CpLDH . Polyclonal antibodies were affinity purified using synthetic peptide conjugated to the agarose resin ( rabbit antibody ) or the recombinant CpLDH protein conjugated onto nitrocellulose membrane ( rat antibody ) as described [39] . Pre-immune sera were similarly affinity-purified in parallel using corresponding antigens ( i . e . , peptide and recombinant protein for rabbit and rat antibodies , respectively ) from the same volumes of sera , and eluted into the same amount of elution buffer as used for antisera . For western blot analysis , C . parvum oocysts and sporozoites were prepared in RIPA buffer containing protease inhibitor cocktail ( Sigma-Aldrich , St . Louis , MO ) . Oocysts were disrupted using five freeze-thaw cycles , while free sporozoites prepared by the excystation procedure were directly lysed in RIPA buffer . Parasite lysates ( 2 . 0×107 oocysts or 7 . 0×107 sporozoites per lane ) were fractionated by 10% SDS-PAGE and transferred onto nitrocellulose membranes . The blots were treated in 5% BSA in TBST buffer ( containing 50 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl and 0 . 05% Tween 20 ) , incubated with the purified anti-CpLDH polyclonal antibody ( 1:500 dilution in 5% BSA-TBST ) , and then treated with horseradish peroxidase-conjugated goat-anti-rabbit or anti-rat IgG antibody ( 1:5000 dilution ) . The blots were then visualized using an enhanced chemiluminescence reagent ( Sigma-Aldrich ) . C . parvum oocysts , sporozoites , and type I merozoites were fixed in 4% formaldehyde for 20 min , washed with PBS , and then applied onto poly-L-lysine coated coverslips . After these were air-dried at ambient temperature , cells were permeabilized with cold methanol:acetone ( V:V = 1:1 ) for 5 min at -20°C . For the intracellular parasite stages , host cells infected with C . parvum were fixed in 4% formaldehyde for 30 min . After three washes in PBS , excess formaldehyde was quenched with 50 mM NH4Cl for 15 min . Fixed cells on coverslips were washed and permeabilized with 0 . 1% Triton X-100 and 0 . 05% SDS in PBS for 5 min . Samples were then incubated with affinity-purified anti-CpLDH antibody ( 1:50 dilution in 1% FBS-PBS ) for 1 h , followed by 3 washes with 1% FBS-PBS ( 5 min each ) and incubation with TRITC-conjugated goat anti-rabbit IgG antibody ( 1:400 dilution ) in PBS for 1 h . After three more washes with PBS , samples were mounted on glass slides in a SlowFade mounting medium , containing 4' , 6-diamidino-2-phenylindole ( DAPI ) for counter-staining of nuclei ( Molecular Probes/Invitrogen ) . All incubations and washes were performed at room temperature or as specified . Cells labeled with fluorescent molecules were examined with an Olympus BX51 research microscope with appropriate filter sets . Images were captured with a Retiga SRV CCD Digital Camera ( QImaging ) and uniformly manipulated with Adobe Photoshop CS6 for signal contrast and intensity . For immunoelectron microscopy , intracellular parasites were similarly prepared , but grown in LabTek Permanox chamber slides for 18 hpi . Infected monolayers were washed in PBS and fixed in 4% paraformaldehyde , mixed with 0 . 1% glutaraldehyde in PBS for 15 h at 4°C . Fixed samples were washed with 0 . 05 M maleate buffer , containing 0 . 5 mM CaCl2 and 2% sucrose , incubated for 30 min at 20°C in 0 . 5% uranyl acetate in maleate buffer , and then washed again in maleate buffer . Samples were dehydrated in a gradient ethanol series , then infiltrated with LR White acrylic resin and cured at 50°C for 13 h . Selected segments of embedded samples were transferred to LR White in gelatin capsules and cured at 50°C for an additional 14 h . Thin sections were produced using a diamond knife on a Leica EM UC6 ultramicrotome and mounted on film-coated grids . Sections on grids were blocked sequentially in blocking buffer I ( 100 mM glycine in TBS ) and II ( 2 . 5% BSA/0 . 2% cold water fish gelatin ) , and then incubated with primary antibody at 4°C overnight . After washes , samples were labeled with goat anti-rabbit IgG secondary antibodies conjugated with 10 nm gold beads for 2 h at room temperature , washed again , and then post-stained briefly with 2% uranyl acetate and Reynold’s lead citrate . Colloidal gold-labeled thin sections were examined with a Philips Morgagni 268 transmission electron microscope ( FEI Company , Hillsboro , OR ) at an accelerating voltage of 80 kV . Digital images were recorded with a MegaViewIII digital camera operated with iTEM software ( Olympus Soft Imaging Systems ) . The distribution of gold particles was manually counted , in which IEM images containing full-sized PVM structures were cropped into separate images representing four types of cellular structures ( i . e . , PVM , parasitophorous vacuolar space , parasite and host cell ) using Photoshop CS6 Extended ( Adobe Systems Inc . , San Jose , CA ) . The area of each structure was measured for calculating the numbers of gold particles per square micrometer . The relative density of gold particles were expressed as the percentage of the total in each dataset . Three IEM images were measured for each type of antibody . Statistical significance was evaluated by two-tailed Student t-test for all individual pairs of structures . The lactate released by parasite oocysts and sporozoites was detected using a lactate assay kit ( Eton Bioscience Inc . , San Diego , CA ) . In this assay , LDH converts lactate and NAD+ into pyruvate and NADH , and then a NADH-coupled enzyme reaction reduces a tetrazolium salt INT ( 2- ( 4-iodophenyl ) -3- ( 4-nitrophenyl ) -5-phenyl-2H-tetrazolium chloride ) into formazan that exhibits an absorbance maximum at 490 nm . Oocysts ( 3×107 ) removed from refrigeration ( 4°C ) were washed twice with PBS and resuspended in 200 μL PBS . Free sporozoites were prepared from 3×107 oocysts by an excystation procedure , as described above , and resuspended in 200 μL PBS . Both oocyst and sporozoite samples were subjected to centrifugation after incubation at 37°C for 1 and 4 h , respectively , from which 20 μL of supernatants were used for detecting lactate contents . The assay was carried out in 70 μL of reaction buffer in 384-well microplates . The reactions were initialized by adding 50 μL of lactate reaction solution into 20 μL sample solutions or lactate standards ( 20 to 640 μM ) . After incubation at 37°C for 1 h , the reactions were stopped by adding 50 μL of 0 . 5 M acetic acid . OD490 values were measured using a Multiskan spectrum spectrophotometer ( Thermo Scientific , West Palm Beach , FL ) . The pMALc2x-CpLDH vector , described in a previous study , was used to express recombinant CpLDH protein in a Rosetta 2 strain of Escherichia coli [12] . Briefly , a single colony of transformed bacteria was inoculated into 50 mL LB media , containing ampicillin ( 50 μg/ml ) , chloramphenicol ( 25 μg/ml ) , and glucose ( 2 mg/ml ) , and this was allowed to grow overnight at 37°C . The overnight cultures were diluted with fresh medium ( 1:10 ratio ) and incubated for ~2 h at 37°C until their OD600 reached ~0 . 5 . Isopropyl-1-thio-β-D galactopyranoside ( IPTG ) was then added to the cultures at a final concentration of 0 . 3 mM to induce protein expression and bacteria were incubated for ~16 h at 16°C for protein overexpression . Bacterial pellets were collected by centrifugation , and MBP-fusion protein was purified by amylose resin-based affinity chromatography , according to manufacturer’s protocols ( New England Biolabs , Ipswich , MA ) . The MBP-tag alone was similarly expressed , purified under the same conditions , and used as negative control . All chemicals used in biochemical assays were purchased from Sigma-Aldrich or as specified . The activity of CpLDH against pyruvate and lactate was determined by monitoring the reduction of NADH at OD340 in a Multiskan Spectrum spectrophotometer ( Thermo Scientific ) . Forward direction assays were performed in 200 μL reaction buffer , containing 50 mM Tris-HCl buffer ( pH 8 . 0 ) , 100 ng MBP-CpLDH1 , 0 . 25 mM NADH , and 1 . 2 mM pyruvate . Reverse direction assays were similarly performed in 50 mM Tris-HCl buffer ( pH 9 . 2 ) , containing 500 ng MBP-CpLDH1 , 1 mM NAD+ , and 100 mM L-lactate . For testing the effect of pH on CpLDH activity , in the forward and reverse reactions , three buffer systems , Citric Acid–Na2HPO4 buffer ( pH 5 . 0–6 . 5 ) , Tris-HCl buffer ( pH 7 . 0–9 . 0 ) , and Sodium Carbonate–Sodium Bicarbonate buffer ( pH 9 . 0–10 . 0 ) , were used . Varied substrate and cofactor concentrations were used for determining the kinetic parameters ( i . e . , pyruvate at 20–3 , 600 μM , NADH at 25–800 μM , lactate at 12 . 5–400 mM , and NAD+ at 25–800 μM ) . The effect of gossypol or FX11 on the ability of CpLDH to catalyze the conversion of pyruvate to lactate was examined in reactions containing 50 mM Tris-HCl buffer ( pH 7 . 0 ) , 2 . 4 mM pyruvate , varied concentrations of NADH , and 10 μM of either gossypol or 50 μM of FX11 . In all experiments , MBP-tag only was used as a control for background subtraction . All assays were carried out in triplicate , and at least two independent assays were performed . GraphPad Prism ( v5 . 0f ) ( http://www . graphpad . com ) was used to calculate kinetic parameters with appropriate nonlinear regression models , including Michaelis-Mention and allosteric sigmoidal models for substrates/cofactors . Only reaction data in the linear range were used in computations , which typically occurred within a reaction time of 5 min . The Ki values for gossypol and FX11 were calculated by Lineweaver-Burke plot and by the Cheng-Prusoff equation for noncompetitive inhibitors [40] . HCT-8 cells ( ATCC# CCL-244 ) were maintained as previously described [23] . Prior to each infection experiment , HCT-8 cells were seeded into 24-well flat bottom plates and allowed to grow overnight until they reached ~80% confluence . In a typical 44-h infection assay , C . parvum oocysts were added to wells at a parasite:host cell ratio of 1:2 ( 105 oocysts/well ) . Cells receiving no infection , or sham infection with oocysts heat killed by pretreatment at 65°C for 30 min , were included as negative controls . After incubation for 3 h at 37°C to allow parasite excystation and invasion , uninfecting parasites were removed , and fresh medium containing 3 . 125 to 34 μM gossypol , or 10 to 80 μM FX11 was added . Medium containing 140 μM paromomycin was used as a positive control . Intracellular parasites were allowed to grow for additional 41 h ( total 44 h growth time ) before total RNA was isolated . The effect of inhibitors on different asexual developmental stages was similarly assayed with one dose of gossypol ( 16 μM ) or FX11 ( 60 μM ) , in which infected cells were treated with inhibitors between 3–20 h and 20–44 h hpi , respectively . Total RNA was isolated at the end of each treatment . The cytotoxicity of both gossypol and FX11 against HCT-8 cells was evaluated by an MTT-based in vitro Toxicology Assay Kit ( Sigma-Aldrich ) , as described elsewhere [41] . The effect of inhibitors on the attachment/invasion of free sporozoites and type I merozoites ( C . parvum motile stages ) was also evaluated . Sporozoites were prepared by an excystation procedure , while merozoites were collected from the culture supernatants as described above . Free sporozoites and merozoites were pretreated with 16 μM gossypol in culture medium at room temperature for 40 min , and then added to plates containing HCT-8 cells ( 104 sporozoites and 103 merozoites per well , respectively ) . After 3 hpi , uninfected parasites were removed by three washes with PBS , and the infected host cell monolayers were lysed for RNA isolation . We also evaluated the effects of gossypol and FX11 on the growth of C . parvum in vitro in FHs 74 Int primary enterocytes ( ATCC# CCL241 ) . In this assay , host cells were similarly cultured as described above and inoculated with C . parvum oocysts at a parasite:host cell ratio of 1:2 . After 3 h of invasion , free parasites in the medium were removed , and gossypol ( 10 μM ) and FX11 ( 60 μM ) were separately added into the culture with a medium exchange . Infected cells were allowed to grow for additional 41 h , followed by the isolation of total RNA . In all experiments , samples receiving no gossypol ( untreated groups ) were used as negative control . At least three biological replicates , plus two technical replicates were performed for each experiment . Parasite loads were evaluated by qRT-PCR-based detection of the relative levels of parasite 18S rRNA as previously described [15 , 23] . Briefly , total RNA was isolated from HCT-8 cells that were either uninfected or infected with parasites for various times , using the RNeasy Mini Kit ( QIAGEN Inc . , Valencia , CA ) . RT-PCR reactions were performed using a QIAGEN one-step RT-PCR QuantiTect SYBR green RT-PCR kit , using the following pairs of primers: Cp18S-1011F ( 5’ TTG TTC CTT ACT CCT TCA GCA C 3’ ) and Cp18S-1185R ( 5’ TCC TTC CTA TGT CTG GAC CTG 3’ ) for C . parvum 18S rRNA ( Cp18S ) , and Hs18S-1F ( 5’ GGC GCC CCC TCG ATG CTC TTA 3’ ) and Hs18S-1R ( 5’ CCC CCG GCC GTC CCT CTT A 3’ ) for host cell 18S rRNA ( Hs18S ) . Each reaction mixture ( 25 μl ) contained 2 ng total RNA , 500 nM each primer , 10 nM fluorescein isothiocyanate ( FITC ) , 0 . 25 μL RT master mix , and 1x QuantiTect SYBR green . Mixtures were incubated at 50°C for 30 min for synthesizing cDNA , heated at 95°C for 15 min to inactivate the reverse transcriptase , and then subjected to 40 thermal cycles of PCR amplification ( 95°C for 20 s , 58°C for 30 s , and 72°C for 30 s ) in a CFX Connect real-time PCR detection system ( Bio-Rad Laboratories , Hercules , CA ) . At least two replicate qRT-PCRs were performed for each sample , and all qRT-PCR reagents were loaded manually . Relative levels of parasite 18S rRNA were calculated by a ΔΔCT method with a general formula of 2-ΔΔCT , in which changes in threshold cycle ( ΔCT ) values between Cp18S and Hs18S were first determined by the equation CT[Cp18S] − CT[hs18S] , followed by the calculation of ΔΔCT between treated and untreated samples . Statistical significance on the relative levels of parasite 18S rRNA was evaluated by Student’s t-test . HCT-8 cells were seeded and cultured for overnight as described above for the isolation of total RNA as described above . The relative levels of the HsLDH-A and Hs-LDH-B genes were determined by qRT-PCR using the following primers: hLDHA-F376 ( 5’ GGG GCA CGT CAG CAA GAG GG 3’ ) and hLDHA-R488 ( 5’ AGC AAC TTG CAG TTC GGG CTG T 3’ ) ( LDH-A ) and hLDHB-F981 ( 5’ TGC CCG GGG ATT AAC CAG CGT 3’ ) and hLDHB-R990 ( 5’ GTC CTT CTG GAT GTC CCA CAG GGT 3’ ) ( LDH-B ) . Levels of human 18S rRNA were used for normalization . | Cryptosporidians are unique among the apicomplexans in regards to their parasitic life style ( e . g . , they are intracellular , but undergo extracytoplasmic development within a host membrane-derived structure termed parasitophorous vacuole membrane , PVM ) and their metabolism ( e . g . , they are incapable of de novo nutrient synthesis and rely on glycolysis for the synthesis of ATP ) . We discovered that the Cryptosporidium parvum bacterial-type L-lactate dehydrogenase ( CpLDH ) enzyme is cytosolic during the parasite’s motile , extracellular , stages ( sporozoites and merozoites ) , but becomes associated with the PVM during intracellular development , indicating the involvement of the PVM in lactate fermentation . We also observed that micromolar concentrations of the LDH inhibitors gossypol and FX11 inhibit both CpLDH activity and the growth of C . parvum in vitro , suggesting that CpLDH is a potential target for the development of anti-cryptosporidial therapeutics . | [
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] | [] | 2015 | Cryptosporidium Lactate Dehydrogenase Is Associated with the Parasitophorous Vacuole Membrane and Is a Potential Target for Developing Therapeutics |
Genetic and developmental architecture may bias the mutationally available phenotypic spectrum . Although such asymmetries in the introduction of variation may influence possible evolutionary trajectories , we lack quantitative characterization of biases in mutationally inducible phenotypic variation , their genotype-dependence , and their underlying molecular and developmental causes . Here we quantify the mutationally accessible phenotypic spectrum of the vulval developmental system using mutation accumulation ( MA ) lines derived from four wild isolates of the nematodes Caenorhabditis elegans and C . briggsae . The results confirm that on average , spontaneous mutations degrade developmental precision , with MA lines showing a low , yet consistently increased , proportion of developmental defects and variants . This result indicates strong purifying selection acting to maintain an invariant vulval phenotype . Both developmental system and genotype significantly bias the spectrum of mutationally inducible phenotypic variants . First , irrespective of genotype , there is a developmental bias , such that certain phenotypic variants are commonly induced by MA , while others are very rarely or never induced . Second , we found that both the degree and spectrum of mutationally accessible phenotypic variation are genotype-dependent . Overall , C . briggsae MA lines exhibited a two-fold higher decline in precision than the C . elegans MA lines . Moreover , the propensity to generate specific developmental variants depended on the genetic background . We show that such genotype-specific developmental biases are likely due to cryptic quantitative variation in activities of underlying molecular cascades . This analysis allowed us to identify the mutationally most sensitive elements of the vulval developmental system , which may indicate axes of potential evolutionary variation . Consistent with this scenario , we found that evolutionary trends in the vulval system concern the phenotypic characters that are most easily affected by mutation . This study provides an empirical assessment of developmental bias and the evolution of mutationally accessible phenotypes and supports the notion that such bias may influence the directions of evolutionary change .
It is evident that the genotype-phenotype map , encompassing organismal development , is highly non-linear , so that random mutation does not result in random phenotypic variation . For example , mutation may induce plentiful phenotypic variation for one trait but none for another . In the extreme case there is an absolute bias , so that certain phenotypes are impossible to generate though mutationally induced developmental changes , i . e . there is a developmental constraint [11] , [12] . The phenomenon of developmental bias can be thought of as milder , relative constraint , where random mutational ( or environmental ) effects translate preferably into certain phenotypes [13]–[15] . Differences in such bias may be primarily quantitative and can be expressed as different probabilities of generating a given phenotypic spectrum upon random perturbation . There is circumstantial evidence that developmental bias is common [13] , [15]–[19] . In addition , experimental evidence suggests that genetic and developmental architecture bias the production of phenotypic variation . For example , repeated instances of parallel evolution indicate that evolution may follow a limited range of pathways [e . g . 20 , 21] . However , identifying the relative contribution of mutational versus selective forces in these comparative analyses remains challenging . Recent tests using experimental evolution approaches provide direct evidence on how genetic architecture may bias molecular variation made available to selection [22] . Overall , very few studies [e . g . 23] have quantified the inducible spectrum of phenotypic variation to evaluate whether “intrinsic” tendencies may influence the direction of phenotypic evolution . In general , as pointed out by Yampolsky & Stoltzfus ( 2001 ) there is little research focusing on experimental characterization of the spectrum of spontaneous variation and the underlying causes of molecular and developmental causes of any observed biases , which would allow testing the hypothesis that biases in the introduction of variation have influenced evolutionary patterns of the examined traits . The mutational architecture may itself evolve , i . e . the regions of phenotypic space reached by mutation differ among genotypes . In other words , developmental bias is genotype-dependent . The inducible phenotypic spectrum for a given genotype has been referred to as “phenotypic neighbourhood” [24] or “local bias” [17] . Such evolutionary variation in mutational properties may be characterized by comparative quantitative analyses of mutation accumulation ( MA ) lines started from multiple distinct genotypes . Such studies show that mutational parameters may vary substantially between taxa and/or between genotypes of a single species [25]–[27] . We previously showed that mutational damage accumulates about twice as fast in C . briggsae as in C . elegans for lifetime reproductive output ( ≈“fitness” ) [25] , [28] , body size [29] , and at dinucleotide microsatellites [30] . These results reveal evolution of quantitative biases in the production of phenotypic variation ( which could be due to evolution of mutation rates ) , but the underlying developmental and molecular causes of biases in the examined traits are so far unknown . To quantify and evaluate the significance of developmental bias and its genotype-dependence , analogous studies need to be carried out in simple , tractable developmental systems . C . elegans vulval cell fate patterning is a model system for the study of intercellular signalling events [31] and has also served to study developmental robustness , cryptic variation and evolution [32]–[35] . The C . elegans hermaphrodite vulva develops from a subset of ventral epidermal blast cells , the Pn . p cells . In wild-type animals , three neighbouring cells , P5 . p , P6 . p and P7 . p adopt vulval cell fates in the sequence 2°−1°−2° . Furthermore , three additional Pn . p cells , P3 . p , P4 . p and P8 . p , have the capacity to adopt a vulval cell fate , when one or more cells of P5 . p to P7 . p are missing [36] . The six cells , P3 . p to P8 . p , therefore constitute the vulval competence group . During the second and third larval stages , the vulval precursor cells adopt alternative cell fates governed by an intercellular signalling network of Ras , Notch and Wnt pathways ( Figure 1 ) . A correct fate pattern of three vulval precursor cells ( 2°−1°−2° ) is required to form a functional vulva . Deviation from this pattern can cause a reduction in offspring number due to impaired egg laying capacity and may further prevent male mating [34] . Vulval cell fate patterning is conserved among Caenorhabditis species [37]–[39]: P5 . p to P7 . p adopt vulval fates with the pattern 2°−1°−2° while all other vulval precursor cells adopt non-vulval fates , either a 3° fate ( the Pn . p cell divides once ) or a 4° fate ( the Pn . p cell fuses early to the epidermal syncytium hyp7 without division ) . Species , however , may differ in the frequency of 3° versus 4° fate adopted by P3 . p , P4 . p and P8 . p [37] and in the replacement competence of these cells upon laser ablation [38] . We previously quantified the precision of vulval development of ( isogenic ) C . elegans and C . briggsae isolates in multiple experimental environments [34] , [37] . The results suggest that vulval development is robust to environmental and stochastic perturbations: apparent vulval defects occur in approximately 1 out of 1000 animals [34] . In contrast , developmental defects and variants increased significantly in mutation accumulation lines derived from a single C . elegans isolate , N2 [40] , thus degrading the precision of vulval cell fate patterning [34] . This result indicates that mutation accumulation represents a feasible approach to quantify largely unbiased , mutationally induced phenotypic variation of this developmental system . In this study , we examined the variation in mutational responses of the vulval developmental system within and between related species . We used mutation accumulation ( MA ) lines derived from two C . briggsae ( HK104 and PB800 ) and two C . elegans ( N2 and PB306 ) wild isolates that had accumulated mutations over approximately 250 generations [25] . We focused on quantifying and characterizing the spectrum of vulval developmental variants induced by spontaneous random mutation to address the following questions: 1 ) Does developmental precision decay upon mutation for all four isolates , and if so , can the action of natural selection be inferred by comparison of the degree of precision among wild isolates ? 2 ) Does the vulval developmental system show a bias in its mutational response , i . e . are certain developmental variants more likely to occur than others ? Which phenotypic characters of the developmental systems show maximal mutability ? 3 ) Do the degree and spectrum of mutationally induced developmental variation vary between genotypes , i . e . to what extent is developmental bias genotype-dependent ? How does the degree of mutability of a given developmental phenotype relate to its actual evolutionary variation within and between species ?
Correlations of line means between two categories of non-canonical variant patterns ( Class A and B ) and two categories of fitness-related traits ( W , CVE , W ) are reported in Table S4 . Given the number of variant categories and examined isolates , these tests are not powerful , but several trends emerged from the pattern of correlations . First , the correlation between class A variants ( disrupted 2°−1°−2° pattern , likely resulting in defects ) and other variants with complete 2°−1°−2° ( class B+C ) was positive in all isolates . The strength of the correlation between defects and variants was dependent on the starting genotype but was not species-specific: the correlation was strong and significant in C . briggsae PB800 and C . elegans PB306 , but much weaker in the other isolates of each species . Second , the correlation between fitness traits and variants with complete 2°−1°−2° pattern ( class B+C ) , but not variants with disrupted 2°−1°−2° pattern ( class A ) , was stronger in C . elegans than in C . briggsae . In particular , the correlation between variant classes B+C and the within-line variance in fitness was uniformly strong and positive in C . elegans ( ∼0 . 5 ) and much weaker in C . briggsae ( not significantly different from zero ) . The correlation in the VEL N2 lines was less than in the CFB N2 lines ( ∼0 . 2; not significantly different from zero ) . Third , all correlations were uniformly weak in the HK104 isolate of C . briggsae , a result we have consistently observed in this isolate [29] . To compare the mutational variance ( VM ) for variant vulval phenotypes with the standing genetic variance ( VG ) , we analyzed data on developmental precision obtained from 10 C . briggsae and 25 C . elegans isogenic wild isolates ( Nindividuals = 8′460 ) . Wild isolate data are presented in Table S6 , showing the proportion of variants for classes A , B , and C . Point estimates of the variance in line means ( V L̂ ) were very low ( ∼10−5 ) for class A variants ( strongly disrupted vulval patterns , defects ) and for the pool of class B + C variant categories , and jackknife 95% confidence limits included zero in both categories in all isolates . Further , when isolates for which multiple estimates of p were available were considered , the maximum likelihood estimates for the among-isolate ( genetic ) component of variance were zero for both categories in both species . Thus , vulval development was highly invariant in both C . elegans and C . briggsae wild isolates , and most of the variant patterns observed were limited to variants of class C ( 3° to 4° transformation of P4 . p/P8 . p ) , in C . briggsae . Across all four sets of MA lines , the different vulval variant patterns were observed at unequal frequencies ( Table 1 ) . Vulval precursor cells adopting a non-vulval 3° fate ( P3 . p , P4 . p and P8 . p ) showed overall more variability than the cells adopting a vulval cell fate ( P5 . p to P7 . p ) . Specifically , we found that the developmental phenotype with the highest mutational variance is that already showing high variability in the ancestral controls , i . e . P3 . p division frequency ( 3° versus 4° fate; variant #14; class D ) ( Table 1 and Figure 3; note change of scale for this variant ) . The second most common variants concern P4 . p and P8 . p division frequency ( variant #12 and 13; class C ) . Behind comes a subset of the variant patterns that affect the vulval fates such as centering shifts ( class B ) , hyperinduction ( class A or B ) or missing precursor cells ( class B ) . Therefore , variants causing likely defects in vulval function ( class A ) were overall less frequent than variants in classes B or C . That different sub-traits of the vulval developmental system degrade at different rates is further confirmed by the mixed-model analysis of the rate of change in the trait mean frequency Rm ( see below ) . To detect evolutionary variation in the mutability of the vulval developmental system , we tested for an overall interaction between variant vulval phenotype and ancestral genotype in an analysis of variance framework . The mixed-model analysis of the rate of change in the trait mean frequency Rm confirmed a substantial main effect of trait ( nominal P<0 . 0001 ) and the expected large main effect of species ( nominal P<0 . 002 ) ( Table S5 ) . Thus , the rate of change in mean frequency during mutation accumulation depended on the variant trait and the species . The main effects of isolate ( nominal P>0 . 8 ) and trait x isolate ( nominal P>0 . 10 ) were not significant . However , note that several of the most extreme differences in mutational induction of specific vulval variants occurred between the isolates of the same species rather than between species ( see below ) . Below we report specific examples of genotypic biases in mutationally induced phenotypic variants . Note that because of low frequency of developmental variants and multiple comparisons , the significance level of given comparisons may be poor ( the critical experiment-wide 5% significance level for thirteen individual comparisons is P<0 . 0038 ) . The clearest examples of intraspecific variation in the mutational pattern are the hyper- and hypo-induction variants in C . elegans: MA lines displayed more hyperinduction variants and less hypoinduction variants in the PB306 isolate compared to the reference isolate N2 . One hypothetical scenario to explain the elevated propensity to generate hyperinduced variants upon mutation accumulation in PB306 might be an increased activity of inductive vulval signalling , already present in the ancestral ( wild type ) genotype . In this scenario , such a difference would rarely be phenotypically expressed in the ancestral genetic background , but become more prevalent in MA lines due to mutational perturbations . To test this hypothesis , we asked whether the activity of the main signalling cascade inducing vulval cell fates , the EGF/RAS/MAPK cascade , was higher in PB306 than in N2 . We introgressed an integrated construct containing a transcriptional Ras reporter , egl-17::cfp [41] , into the two isolates to examine Ras activity levels during the vulval patterning process from mid-L2 to early-L3 stage ( see Materials and Methods ) . Consistent with the hypothesis , PB306 showed a significantly higher Ras pathway activity in the relevant vulval precursor cell , P6 . p , during mid-L2 and early L3 stages compared to N2 ( Figure 4 ) . Thus , the difference in the mutational accessibility of hyperinduced variants between PB306 and N2 may result through variation in the activity of the Ras pathway , which is phenotypically silent ( cryptic ) under normal conditions .
The developmental system underlying Caenorhabditis vulva precursor cell fate patterning was consistently degraded in mutation accumulation ( MA ) lines derived from all four isolates . In contrast to previously examined traits , such as body size , a quantitative trait varying along a single axis [29] , [42] , the variation is here practically absent among and within ancestral controls and mutational challenges induce novel variants . Vulval patterning variants almost always had a very low penetrance in a given mutation accumulation line . Many MA lines showed multiple , distinct variants and we never found a line in which a specific variant pattern was fixed . The observed mutational pattern of small-effect variants may either be explained by non-null mutations in structural genes or mutations in regulatory regions with effects too small to be retained in conventional genetic screens . The core genetic elements of the vulval signalling network amount to approximately 30 genes [31] , covering an estimated 150 kb . A conservative estimate of the mutation rate is one mutation per genome per generation in C . elegans [43] , so that tested MA lines exhibit an average of 250 mutations per genome ( 100 Mb ) . Assuming that about a third of the nucleotide sites are susceptible to mutations having some phenotypic effect , the probability of mutating such a site in this category of “identified vulva genes” is 0 . 125 for a given MA line . This is consistent with the frequency of defects that we observe; however , this estimate is highly speculative , in particular , because we have no information on the distribution of mutational effects at a given locus . Moreover , it appears likely that several of the mutationally induced vulval variants may have been triggered by mutations of genes not directly involved in the vulval signalling network . Diverse developmental mutations primarily affecting body size and shape have the potential to disrupt the spatial and temporal integrity of the vulval induction process [44] , and we have observed that many of these mutations ( e . g . , dpy , lon , sma , unc ) show diverse low-penetrance vulval variants and defects similar to the ones observed in MA lines . One consequence of the induction of deviation from an invariant pattern is an increase in the within-line component of variance . We previously demonstrated that the environmental ( within-line ) component of variance ( VE ) consistently increases with mutation accumulation for W , total lifetime fecundity , and body volume in these same lines [45] . We interpreted this result as evidence that spontaneous mutations de-canalize the phenotype , but could not completely rule out the possibility that that result was an artefact of the way in which these data were scaled . In contrast , the increase in vulval variants and defects with MA is most straightforwardly interpreted as an increase in the environmental component of variance , i . e . , de-canalization , and it cannot be attributed to scaling . Thus , mutation accumulation increases the sensitivity of the vulval developmental system to stochastic ( micro-environmental ) perturbations [46] . We calculated an estimate of the standing genetic variance ( VG ) for variant vulval phenotypes using data from 25 C . elegans and 10 C . briggsae wild isolates . At mutation-selection balance in a large population , the ratio of the mutational variance to the standing genetic variance provides an estimate of the strength of purifying selection of mutations affecting the trait , i . e . , S≈VM/VG , where S is the average selection coefficient against a new mutation . Using the point estimate of of the wild isolates as a surrogate for VG and the point estimate of ΔV as a surrogate for VM , the average selection coefficient against mutations affecting Class A variants inferred from the ratio VM/VG ( = S ) is on the order of 10% or larger ( for C . briggsae the point estimate of S = 0 . 30; for C . elegans S = 0 . 16 ) . Conversely , the ratio VG/VM can be interpreted as the “persistence time” of a new mutation , i . e . , the expected number of generations the mutation segregates before it is lost [47] . Thus , as expected , new mutations that cause Class A variants segregate for only a very few generations before they are removed by selection ( Class A variants in the system are clearly deleterious in laboratory conditions , because they prevent egg-laying and reduce progeny number [34] ) . By way of comparison to life history traits in the same species , selection coefficients inferred in this way for W , body volume , and lifespan are on the order of 1–5% [48] , [49] . This result confirms that vulval development is under strong purifying selection to maintain an invariant phenotypic output . The observed selection thus very likely corresponds to the type of stabilizing selection , as defined by Schmalhausen [50] , and canalizing selection [51] . Concerning other variant classes , comparison of the genetic variance among wild isolates and after spontaneous mutation accumulation with minimal selection provides indirect evidence of their elimination by selection in natural populations . Especially in class B , the frequency of developmental variants was very low in the four controls as well as in a large set of wild isolates of C . elegans and C . briggsae covering a much larger range of genetic variation than the MA lines [43] , [52] ( Table S6 ) . Averaged over variants and species , the ratio VM/VG ( = S ) of Class B variants is again on the order of 10% , very similar to Class A variants ( for C . briggsae the point estimate of S = 0 . 12 , for C . elegans S≈1 ) . Among the class B variants , variants with vulva centering shifts or missing Pn . p cells ( variants #6–9 ) form a complete vulva due to cell fate regulation among the vulva competence group ( cells that can adopt a vulval fate through expression of the lin-39/Hox gene [31] ) . Importantly , this result strongly argues for strong selection against class B variants in natural populations although these variants do not disrupt functionality of the vulval organ and show no fitness effects in the laboratory [34] . By contrast , selection against class C variants appears much weaker ( S on the order of 0 . 1% ) . Class C variants describe variation in non-vulval fates of P4 . p and P8 . p , which normally do not affect P ( 5–7 ) . p vulval fates . When adopting the variant pattern ( i . e . adoption of the 4° fate ) , P4 . p and P8 . p fuse to epidermal syncytium without division in the L2 stage [53] , so that the cells lose their competence to respond to late inductive vulval signalling . Nevertheless , these cells may still be able to respond to Wnt or EGF signalling earlier before hypodermal fusion , and thus to replace one of the P ( 5–7 ) . p cells in the case of co-occurrence of a class B variant . In contrast to classic mutagenesis screens selecting for developmental mutants with high penetrance phenotypes , the screening of the phenotypic spectrum of MA lines is largely unbiased and representative of the phenotypic spectrum induced by spontaneous random mutation . We found that MA induced certain phenotypic variants much more readily than others , demonstrating biases in the mutational accessibility of phenotypic variants . The vulval trait with the highest mutational variance is that already showing high variability in the ancestral controls ( P3 . p division frequency , variant #14 ) , followed by P4 . p and P8 . p division frequency ( variant #12 and #13; class C ) . Variants causing likely defects in vulval function ( class A ) were overall less frequent than variants in classes B or C . In addition , several of these variant patterns have not been found by mutagenesis in the laboratory , presumably because they were too subtle for efficient phenotypic scoring . On the other hand , we did not uncover all possible variant vulval patterns , which suggests that certain of these variants are either fully lethal and could not be propagated in MA lines , or their appearance through mutational effects is too improbable . Such variants include lateral inhibition defects with vulval cells showing adjacent 1° fates as seen in lin-12/Notch mutants [54] . Although a fully penetrant loss of lateral inhibition may be lethal , it is interesting that we did not find this variant at low penetrance like other fate pattern variants . This suggests that the mutational target size for this variant ( relying on Notch pathway regulation ) is small . Taken together , these observations provide clear examples of developmental bias [13]–[15] , [18] , [19] , with certain phenotypic variants being more easily induced by mutation than others . Several results show that biases in the production of vulval variants are genotype-dependent . First , overall rates of mutational decay differ among ancestral controls , most likely due to higher molecular mutation rates in the C . briggsae isolates compared to the C . elegans isolates [25] , [30] . The approximately two-fold greater change in the trait mean in C . briggsae was roughly consistent with previous results concerning other traits [28] , [29] . Second , we observed differences in the relative mutability of the same canonical pattern to different types of variant pattern . These differences in the mutationally inducible phenotypic spectra may be explained by one of two possible mechanisms . First , the mutation rate at specific loci may vary among wild isolates . For example , a microsatellite repeat present at these loci in some isolates and absent in others may dramatically change mutation rates at the locus [55] . Second , a distinct bias in the developmental system may occur if the internal system variables are slightly offset in some isolates towards the production of a given variant pattern . For example , C . elegans PB306 may mutate more frequently to genotypes producing hyperinduction defects if the Ras pathway involved in vulval induction is in average slightly more active in individuals of this isolate ( compared to other wild isolates ) . More mutations of small effect on the system may then tip the balance towards hyperinduction when acting on the C . elegans PB306 isolate , and remain silent in other isolates . In this case , the different relative mutability to the hyperinduced phenotype of different starting genotypes may thus depend on cryptic genetic variation causing variation in system parameters , also termed intermediate phenotypes [32] . Apparent cryptic variation in such a quantitative developmental parameter may be confirmed by introgression of mutations or by measurements of signalling pathway activity . A higher Ras pathway activity in the C . elegans PB306 isolate is indeed supported by the higher induction index of let-60 ( n1046gf/ras , lin-3 ( n378rf ) /egf mutants and of the ark-1 ( sy247lf ) ; gap-1 ( n1691lf ) double mutant [35] . Our present results using a reporter gene further confirm that the Ras pathway is significantly more active in C . elegans PB306 compared to C . elegans N2 ( Figure 4 ) . This result demonstrates the presence of intraspecific variation in the activity of vulval signalling pathways and agrees with the proposed second mechanism of evolution of the mutational variance through a bias in mutational effects . In the future , the determination of the molecular lesions and their introgression in different genetic backgrounds may definitively answer whether this difference accounts for the increased frequency of hyperinduced variants in PB306 . Mutational and environmental perturbations can both cause de-canalization of the phenotype [56] . Yet , there is limited experimental evidence whether these two sources of variation also affect the same elements of developmental systems . When comparing the phenotypic effects of mutational vs . environmental perturbation , analyses are often restricted to a single or few environmental conditions using a single or few genetic variants . MA lines provide a more extensive and unbiased sampling of genotypic space . Yet , unlike mutation , environments cannot be systematically sampled . We therefore limit our comparison to six environments examined in an earlier study [34] , showing that certain vulval variants are specifically generated in certain environments and genotypes . Several of these previously observed variant patterns were also frequently found after MA . Specifically , vulval centering shift variants on P7 . p were never found in C . elegans N2 MA lines , but occurred often in MA lines derived from the other three ancestral genotypes . Similarly , N2 never generated P7 . p centering shifts under starvation stress , while C . briggsae showed increased and increased frequency of this variant pattern . Mutational perturbations therefore may mirror environmental perturbations , so that both sources of variation reveal the genotype-dependence of developmental bias . Examination of different Caenorhabditis MA lines allows us to detect axes of high mutational variability in the vulval developmental system . Whether or not such high mutational variance translates into actual evolution then depends on selection . Some of these phenotypic axes of least resistance upon mutation may correspond to traits under purifying selection . In this case , the available mutational variance does not result in phenotypic evolution . For other variant types , however , the high mutational variance may correspond to phenotypic evolution observed in the species or among closely related species . In the Caenorhabditis genus , intra- and interspecific variation in vulval patterning traits is limited to the frequency of P3 . p adopting a 3° versus 4° fate , and to a lesser extent that of P4 . p [37] , [39] , [57] . For these two vulval phenotypes we also found the greatest mutational variance . The mutational bias and the evolutionary trend in the vulva system thus mainly affect the same trait . At a larger evolutionary scale , a similar match between mutational pattern and evolution is found in the Oscheius genus , but for vulva variants that concern the second round of 3° cell divisions ( variants #10–11 ) . In this case , the mutational variance in the occurrence of the second round of 3° cell divisions appears high in Oscheius tipulae CEW1 ( from EMS-induced mutant lines ) [58] and the same trait varies greatly within the Oscheius genus [24] , [37] , [39] . By contrast , we found very little mutational variation in the occurrence of a second division round for the 3° cells ( variants #10–11 ) , and these traits are invariant within the Caenorhabditis genus , presumably because of developmental constraints . Such studies of relative trait mutability are thus crucial to understand variation in evolutionary trends between taxa and thereby bridge the gap between micro- and macro-evolutionary variation . In conclusion , our results provide an empirical view on the developmental variation induced by spontaneous random mutation . In the case of the highly canalized vulval developmental system , this variation is generally very subtle and difficult to quantify . In addition , the induced phenotypic variation is very complex despite the seeming molecular and developmental simplicity of this process . Nonetheless , we could uncover a number of developmental and genetic biases in the introduction of phenotypic variation , supporting the notion that such asymmetries bias the range of phenotypes available for selection to act upon [11]–[15] , [18] , [19] . Many more studies characterizing biases in the production spontaneous phenotypic variation ( and its correspondence to evolutionary variation of the studied phenotypes ) are required to evaluate whether such asymmetries play important roles as direction-giving forces in the evolutionary process .
The main set of mutation accumulation ( MA ) lines in this study is that of Baer et al . [25] ( called CFB lines ) . The lines were originated from a single highly inbred individual from each of two isogenic wild isolates of C . elegans ( N2 and PB306 isolates ) and C . briggsae ( HK104 and PB800 isolates ) . Criteria for choice of these isolates are given in [25] . The mutation accumulation experiments began with 100 replicate MA lines per isolate . Details of the mutation accumulation protocols are given in the original paper . Briefly , highly inbred stocks of each isolate were replicated 100 times and perpetuated by single-hermaphrodite transfer for 250 generations . This protocol results in a genetic effective population size of Ne≈1 ( the approximation is the result of occasionally having to use backup stocks of worms when the original worm did not survive ) , thereby minimizing the efficiency of natural selection and ensuring that all but the most deleterious mutations behave according to neutral dynamics . Worm stocks , including G0 ancestral controls and ultimate generation MA lines , were cryopreserved using standard methods [59] . Wild isolates of C . elegans ( N = 25 ) and C . briggsae ( N = 10 ) used in this study are listed in Table S6 . Both species display a high selfing rate in natural populations [52] , [60] . The ( isogenic ) wild isolates were originally established by selfing populations derived from a single individual isolated from the wild . Worms were kept on Petri dishes ( 55 mm diameter ) filled with NGM ( Nematode Growth Medium ) agar , seeded with approximately 200 µl bacterial suspension of the E . coli strain OP50 . All experiments were carried out at 20°C . For each of three experimental blocks , a random set of MA lines and the four ancestral controls were thawed ( for samples size , see below ) . To eliminate potential genetic variation in the stock culture , a single individual from each line was selected to initiate the experimental populations . After population expansion , 20–30 adult hermaphrodites per line were hypochlorite treated to clear individuals form potential microbial contaminations . ( At this time , for each of the four ancestral controls , multiple replicates were established except for the first block ) . The resulting eggs were allowed to develop into adults at which stage 20 hermaphrodites ( from the same NGM plate ) were transferred to a new NGM plate . When the majority of the offspring had reached the L4 stage ( after approximately 2–5 days depending on the line ) , 50 offspring/line were randomly selected to score their vulval phenotype . The vulval cell phenotype was determined during the early to mid L4 stage using Nomarski microscopy on individuals anaesthetized with sodium azide [59] . We counted induced cells and determined the fates of the cells P3 . p to P8 . p as described previously [44] . MA and control lines underwent approximately 4–6 generations on NGM plates ( at low densities ) between thawing and scoring . We defined different types of vulval developmental variants ( shown in Figure 2 ) by taking into account developmental features of the system . Note that due to replacement regulation between vulval precursor cells [31] , the fate of each individual cell is not independent from that of the other cells . For example , when the anchor cell is positioned on P5 . p , the entire pattern is displaced anteriorly and four Pn . p cell fates are affected simultaneously; if P5 . p is missing , P4 . p adopts a 2° fate; if the anchor cell is missing , the fates of P ( 5–7 ) . p switch to a 3° fate , etc . Defining 14 distinct variant types allowed us to greatly lower the number of variant types compared to the combination of each fate for each cell ( 1°/2°/3°/4°/missing x 6 = 30 classes ) . Some of these variants correspond to changes due to independent developmental events as defined by mutational analysis [24] , [53] , [61] . For example , hypoinduction phenotypes through cell fate change from a vulval fate to a non-vulval fate ( trait #2 ) likely occur through low activities of Ras or possibly Wnt pathways ( Induction Vulvaless in [61] ) . In contrast , hypoinduction phenotypes arising by lack of Pn . p cells ( trait #3 ) occur because of cell death or earlier switch in cell fate ( Generation Vulvaless in [61] ) . The following number of MA and control lines were analyzed for each isolate: HK104 ( 44 MA lines , 17 control lines ) , PB800 ( 53 MA lines , 17 control lines ) , PB306 ( 51 MA lines , 17 control lines ) and N2 ( 52 MA lines , 17 control lines ) . For each MA and control line , 50 individuals were scored for their vulval phenotype . There are two fundamental observable quantities of interest in a MA experiment—the change in the trait mean and the change in the variance . In this study , vulval character state is a binary random variable X with state 0 = wild-type and state 1 = non-canonical” ( for traits 1–13 ) . The data are binomially-distributed with parameter p = Pr ( X = 1 ) . Within a genotype/treatment group ( “treatment” = MA or G0 ancestral control ) , each line provides a single independent estimate of p . If wild isolates are homozygous at all loci ( a plausible approximation for a highly-selfing species; see above ) , the standing genetic variance ( VG ) can be estimated from the among-line component of variance [65] . However , for 22/25 wild isolates of C . elegans , we only have a single estimate of the binomial parameter p and therefore cannot meaningfully partition the variance in p into within and among-isolate components . Instead , we use the variance in isolate means V L̂ as an upper bound on VG . Using ΔV and V L̂ to approximate the mutational variance VM and VG , respectively , the relationship VG≈VM/S provides an estimate of the strength of selection against new mutations ( S ) , provided the system is at mutation- ( purifying ) selection balance ( MSB ) [47] . For the isolates for which we have multiple independent estimates of p , we partitioned the variance into within- and among-isolate components using REML as implemented in the MIXED procedure of SAS v . 9 . 2 . We can then compare the variance components of these isolates to V L̂ to gain a rough idea of the relative fraction of the variance that is among isolates . To establish confidence intervals on ΔV and V L̂ we used a delete-one jackknife method [66] to estimate the standard error of the statistic , which was then used in the standard Student's-t calculation of the 95% confidence limits [67] , To estimate Ras pathway activity level in the C . elegans N2 and PB306 isolates , we used a previously generated transgenic strain containing an integrated transcriptional reporter construct for the LET-60/Ras pathway , egl-17::cfp-lacZ ( strain GS3582 ) [41] . This construct contains a nuclear localization sequences upstream of the CFP coding sequence and was generated using the isolate N2 [41] . We then generated the egl-17::cfp-lacZ strain JU480 from the strain GS3582 by genetically removing the transformation marker unc-4 ( e120 ) . Each integrated transgenic array generated in the N2 background was outcrossed ten times to PB306 , by crossing at each generation the male progeny to wild hermaphrodites . After ten backcrosses , the introgressed line was made isogenic by selfing for several generations , yielding strain JU488 . The CFP fluorescence quantification experiment was performed as described in [34] in standard conditions at 20°C , for JU480 and JU488 in parallel . For each individual/image , we quantified signal ( pixel ) intensity of P5 . p , P6 . p and P7 . p . For each examined developmental stage , we carried out an ANOVA ( JMP 7 . 0 for Mac ) testing for the fixed effects of isolate , individual ( nested in isolate ) , cell , and the interaction between isolate and cell type using mean signal intensity as a response variable . The inclusion of the effect individual ( isolate ) allowed us to control for the non-independence between measures of P5 . p , P6 . p , and P7 . p taken from a single individual . Post-hoc tests ( Tukey's HSD ) were then performed to determine differences in signal expression between isolates and cells ( P5 . p . P6 . p , P7 . p ) . | Random mutation does not generate random phenotypic variation because genetic and developmental architecture may constrain and bias the mutationally inducible phenotypic spectrum . Understanding such biases in the introduction of phenotypic variation is thus essential to reveal which phenotypes can ultimately be explored and selected through evolution . Here we used lines which had accumulated spontaneous random mutation over 250 generations starting from four distinct wild isolates of the nematode species C . briggsae and C . elegans , to study how a developmental system—vulval cell fate patterning—responds to mutational perturbations . We show that developmental defects and variants increase upon mutation accumulation in lines derived from all four isolates . However , some mutationally induced phenotypic variants occur more frequently than others , and the degree and spectrum of developmental variation further differed between isolates . These results illustrate how the phenotypic spectrum induced by random mutation can be biased due to both developmental system features and variation in the genetic background . Moreover , the mutationally most sensitive phenotypic characters are the ones that show most evolutionary variation among closely related species . These observations show how random mutation translates into a biased , limited range of phenotypes—a phenomenon likely impacting possible trajectories of phenotypic evolution . | [
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] | 2010 | Bias and Evolution of the Mutationally Accessible Phenotypic Space in a Developmental System |
Many pathogens associated with chronic infections evolve so rapidly that strains found late in an infection have little in common with the initial strain . This raises questions at different levels of analysis because rapid within-host evolution affects the course of an infection , but it can also affect the possibility for natural selection to act at the between-host level . We present a nested approach that incorporates within-host evolutionary dynamics of a rapidly mutating virus ( hepatitis C virus ) targeted by a cellular cross-reactive immune response , into an epidemiological perspective . The viral trait we follow is the replication rate of the strain initiating the infection . We find that , even for rapidly evolving viruses , the replication rate of the initial strain has a strong effect on the fitness of an infection . Moreover , infections caused by slowly replicating viruses have the highest infection fitness ( i . e . , lead to more secondary infections ) , but strains with higher replication rates tend to dominate within a host in the long-term . We also study the effect of cross-reactive immunity and viral mutation rate on infection life history traits . For instance , because of the stochastic nature of our approach , we can identify factors affecting the outcome of the infection ( acute or chronic infections ) . Finally , we show that anti-viral treatments modify the value of the optimal initial replication rate and that the timing of the treatment administration can have public health consequences due to within-host evolution . Our results support the idea that natural selection can act on the replication rate of rapidly evolving viruses at the between-host level . It also provides a mechanistic description of within-host constraints , such as cross-reactive immunity , and shows how these constraints affect the infection fitness . This model raises questions that can be tested experimentally and underlines the necessity to consider the evolution of quantitative traits to understand the outcome and the fitness of an infection .
Most theoretical and experimental studies focus on the dynamics of viral diversity during an infection ( see e . g . , [10]–[14] ) or on qualitative traits such as drug resistance [15]–[17] . Studies rarely consider quantitative traits , even though viral replication seems to provide an exception . Variable polymerase activities have been observed during HCV infections along with the individual viral sub-populations , both between infected individuals and within an individual [18] , which suggests that strain distribution in a given host is characterised by a range of replication rates . Given the high evolutionary rates of rapidly mutating viruses [19] , [20] and the extreme selection pressure exerted by the immune response [15] , [21] , one can expect such quantitative traits to evolve during an infection . Infections caused by a rapidly mutating virus exhibit high levels of genetic diversity . Classical theory predicts that , in such a case , faster replicating strains have the highest infection fitness because they gather more resources before the end of the infection [22] , [23] , which has been confirmed experimentally ( e . g . , [24] ) . This ‘short-sighted’ evolution , with the subsequent selection of faster replicating strains , has been proposed as a mechanism to explain HIV virulence [23] . Yet , not all strains have high replication rates and rapidly replicating strains also kill their host quickly , thus potentially decreasing their fitness at the between-host level [25]–[27] . Some within-host models study the evolutionary dynamics of the replication rate during the course of an infection . A common finding of such models is that the replication rate increases during the course of an infection , either because of resource competition or because of apparent competition through the immune system [28]–[30] . If within-host trade-offs are assumed , strains with intermediate or low replication rates can take over the infection [31]–[35] . Here , we focus on the fitness of a viral strategy at the between-host level ( i . e . , how many new infections an infection can cause ) . Bonhoeffer and Nowak [25] developed one of the few evolutionary epidemiology models that includes within-host evolution of the replication rate , however they simplified within-host processes to the extreme by assuming that , within a host , a more virulent mutant always instantaneously takes over the infection . Our model describes the within-host dynamics , which allows us to follow the evolution of the virus along with the changes in its environment ( i . e . , the immune response ) . The immune system is known to be a major constraint on viral evolution [21] , [36] . This underlines the importance of including immune dynamics in within-host models [37] , [38] . Following [7] , we assume that immune activation depends on the overall viral growth rate , i . e . , the product of the viral replication rate and density . The idea underlying this assumption is that increasing the growth rate can be detrimental for the virus because it increases immune pressure . An implication of such a trade-off is that a strain with a lower growth rate can have higher infection fitness because the infection lasts longer . This assumption echoes the immunogenicity criterion formalised by Pradeu and Carosella [39]: maintaining a continuity , in terms of antigens ( viral peptides recognised by the immune system ) present in the host , can be decisive for the pathogen to avoid triggering an immune response . How can the replication rate affect this continuity ? Infected cells process and present antigenic peptides , that are ‘pictures’ of the current intracellular state , to the initial T-cell repertoire . Yewdell [40] argues that there is a clear link between viral translation ( i . e . , the production of non-endogenous proteins ) and antigen presentation . The reason for this is that rapidly degraded polypeptides ( associated with rapid translation ) lead to a more efficient generation of major histocompatibility complex ( MHC ) Class-I peptides , which are then presented to cytotoxic T cells . This explains why infected cells can be identified so rapidly: viral peptides have a higher chance to be expressed than endogenous peptides because they are degraded more rapidly than endogenous gene products [40] . Rapidly replicating viruses could then be an easy target for the immune system if they generate a larger pool of rapidly degraded polypeptides . This is consistent with the fact that to copies of a protein are needed for successful processing and presentation of MHC Class I peptides [41] . A few experimental studies have also shown that increasing the viral replication rate provides a wider and more abundant antigen presentation [40] , [42] , [43] . One must admit that it is difficult to disentangle the roles of variations in replication rates and variations in viral density because the two are obviously linked [7] . Moreover , many other factors such as the dose , the localisation and the duration of antigen presence are also critical to immune activation [44] . Here , we develop a nested model that links within-host evolutionary dynamics to the epidemiology . By explicitly describing the immune dynamics , the model takes into account the fact that a viral strategy dominant early in an infection ( e . g . , replicating slowly to escape the immune system ) may be rare later in the infection .
As described in the Model section , in order to show that natural selection can act on the replication rate of the infecting strain at the epidemiological level , we need to show that this trait affects infection fitness and is heritable from one infection to the next . In this subsection , we focus on the default case ( in black in Figure 2 ) . We find that the initial replication rate ( ) has a strong effect on infection fitness , which is maximised for low replication rates ( Figure 2A ) . The fact that very low or high values of lead to low fitness values is likely to be due to short duration of the infections ( Figure 2B ) . This is also clear from the statistically significant correlations we observe between the infection fitness and the initial replication rate ( Table 2 ) . Note that the value of maximising the infection fitness depends on the base-line clearance term of infected cells ( , figure not shown ) . The correlation between and the average replication rate of transmitted strains ( Figure 2C ) suggests that the trait is heritable . For low values of , transmitted values can be higher than ( Figure 2C ) because the trait evolves for a longer time within the host ( Figure 2B ) . Also , viral diversity increases as the infection progresses ( Figure 1C ) , implying that the distribution of transmitted replication rates also changes over time ( see Figure S1 ) . According to these results , predictions are more complex to achieve for long-lasting infections and call for an explicit epidemiological model based on the distribution of transmitted strains resulting from the within-host dynamics . Figure 2D shows that viral diversity near the end of the infection is maximised for low replication rates , which is likely to be due to an increased duration of the infection ( Figure 2B ) . The density of infected cells near the end of the infection also has a maximum value for low replication rates ( figure not shown ) . Finally , higher initial replication rates always lead to higher final levels of immune cells densities ( Table 2 ) . These results suggest that for low replication rates , infections are long lasting ( most of the simulations end because the maximum time of days is reached ) whereas for other values the infection is often cleared by the immune response . Overall , the initial replication rate significantly influences the course of the infection . We model the variation in the breadth of the immune response by varying the width ( ) of the cross-reactive immunity function in equations 2a and 2b ( see the Model section ) . Increasing the width ( ) reduces infection fitness ( Figure 2A and Table 2 ) and decreases viral diversity near the end of the infection ( Figure 2D ) . The decrease in fitness seems to be due to a decrease in the duration of the infection ( Figure 2B ) . Increasing cross-reactive immunity ( ) also decreases the replication rate of transmitted strains ( Figure 2C ) . This result , which may seem counter-intuitive , is a consequence of the stochastic nature of our simulations . Increasing increases the rate of immune activation , which limits the opportunities for escape mutations and thus decreases the duration of the infection ( Figure 2B ) . In our model , however , the activation rate of immune cells depends on the replication rate of the virus . Therefore , an increase in immune pressure confers a strong selective advantage to slow-replicating strains . In the case of infections caused by a rapidly replicating strain ( high ) , there are two scenarios: rapid clearance or rapid evolution towards lower replication rates . The latter prolongs the infection and the number of transmission events , thus decreasing the average value of replication rates of transmitted strains . Unless cross-reactive immunity is very high , diversity near the end of the infection is maximised for low values of the initial replication rates ( Figure 2D ) . Overall , cross-reactive immunity significantly decreases infection fitness and within-host evolution , and alters the strain composition and therefore the transmission dynamics . We model viral mutation as a stochastic process ( see the Model section and Text S1 ) . A newly generated strain is identified by at least one new trait value ( its replication rate and/or its antigenic value ) . We study the effect of the mutation process on infection life-history traits . Overall , there is no substantial effect of the mutation rate ( ) on viral fitness , which has a maximum value for low values of the initial replication rate ( Figure 3A ) . However , increasing tends to be slightly deleterious in terms of infection fitness for strains with low because , with high , rapidly replicating mutants tend to appear earlier , which decreases the duration of the infection . For high values of , however , increasing the viral mutation rate leads to an increase in viral fitness because mutation rate increases the probability that an escape mutant is generated before the first infecting strain is cleared . The mutation rate also significantly correlates with the duration of the infection , the final level of viral diversity and the average replication rate of transmitted strains ( see Table 2 and Supporting Figure S2 ) . Increasing the width ( ) of the uniform distribution from which new mutant replication rates are chosen decreases infection fitness and increases both the duration of the infection and the initial replication rate that maximises fitness ( Figure 3B and Table 2 ) . Also , increasing leads to a significant increase in average replication rate of transmitted strains ( Figure S2 ) and to a decrease in final viral diversity ( Table 2 ) . Contrary to the mutation rate , increasing the width does not increase the probability that an escape mutation is generated by the initial strain . However , for high values of , rapidly-replicating mutants appear early in the infection . Since these mutants are more adapted to the apparent competition taking place at the within-host level ( see the Discussion ) , increasing decreases the infection diversity and fitness for low values of . From a biological point of view , increasing the size of the mutation step can be interpreted as the introduction of recombination between unrelated strains . This process is rare for HCV , but it has been shown to occur within some hosts [49] . Increasing the maximum T-cell activation rate ( ) improves the immune response , therefore the consequences of an increase in are not surprising: it significantly decreases infection fitness ( Figure 3C ) , duration of the infection , final diversity and final total viral load ( Table 2 ) . Also , the initial replication rate that maximises infection fitness increases with , which is consistent with earlier models without within-host evolution [5] . For large values of infections tend to be very short and there is no heritability of the trait ( see Figure S2 ) . Increasing the maximum T-cell killing rate ( ) also increases the efficiency of the immune response but the evolutionary response seems to differ with respect to variations of ( Figure 3C and D ) . Increasing significantly increases both the fitness of rapidly replicating strains and the duration of the infections these strains cause ( Table 2 ) . Indeed , the increase in leads to a decrease in the number of infected cells early in the infection . Since immune activation is proportional to the number of infected cells present in the system , the increase in killing capacity also has a drawback: it slows down the immune response . Therefore , both the duration of the infection and the viral density increase , thus improving the infection fitness . When the killing rate is very high , the virus is completely eliminated and no within-host evolution occurs . Overall , these results show that interfering with immune activation or immune killing can lead to different outcomes . Also , the result that increasing immune response does not always reduce viral fitness is in accordance to the experimental observation that HCV generates a highly cytotoxic environment and therefore it is the immune response that may jeopardise host survival . Our model thus provides a simple mechanism that explains how an increase in immune resources can limit the immune response . We also studied the effect of the initial population sizes of infected cells and of lymphocytes ( precursor frequency ) , the possibility of host death ( i . e . , virulence ) and of saturation in the lymphocyte proliferation function in equation 1 . These results are shown in Text S2 . In the absence of within-host evolution , Figure 2A would be sufficient to infer the evolutionary stable strategy ( ESS , [50] ) of the virus , i . e . , the replication rate that is selected on the long term . In a model with within-host evolution where all infections would produce the same number of secondary infections ( i . e . , have the same infection fitness ) , Figure 2C could be used to find the ESS . The latter , if it exists , is at the intersection between the curve and the line ( see the caption of Figure 4 for further details ) . Depending on the topology near the intersection , this singularity can be evolutionary stable ( i . e . , be an ESS ) or unstable: if the curve intersects the line from top to bottom ( i . e . that it is concave ) , there is an ESS . In our model , each one of the transmitted strains potentially has a different infection fitness based on its replication rate . We therefore combine differential fitness ( Figure 2A ) and within-host evolution ( Figure 2C ) to identify the ESS ( see Model Section ) . Figure 4 shows the average replication rate of transmitted strains weighted by their infection fitness for different values of . In the default case , the ESS is obtained for ( the black star in Figure 4 ) . This estimate is close to what would be obtained by ignoring the differences in infection fitness , which indicates that within-host evolution acts more strongly than between-host evolution . Increasing the level of cross-reactive immunity favours those strains with higher replication rates , which eventually dominate the population on the long term . For low values of , curves in Figure 4 strongly differ from those in Figure 2C . This shows that the control over trait evolution can shift from the within-host to the between-host level when parameter values change . We simulate two types of treatments: the first type improves the killing of infected cells and is modelled with an additional killing term of infected cells , the second type blocks viral replication and is modelled with a limitation term on viral replication . Current treatments of HCV use typically in combination with ribavirin . These treatments act on viral dynamics by blocking viral replication [51] , but also stimulate innate and cellular immunity [52] , [53] . In Figure 5 , we show the effect of a replication-blocking treatment administered with different intensities ( ) and started at different time points ( 0 , 30 or 180 days after infection ) . A treatment model based on increasing immune killing leads to similar results ( results not shown ) . This is because , in our model , there is only immune-mediated competition between strains and no resource competition . Overall , increasing decreases the infection fitness , the duration and the diversity of the infection ( Figure 5A , C and , E , and Table 2 ) . Note that the average infection fitness values we show in Figure 5 are obtained by including hosts who cleared the infection before the initiation of the treatment . Regardless of the starting time , increasing the treatment intensity increases the value of the initial replication rate that maximises infection fitness ( Figure 5A , C and E ) , as in models without within-host evolution [5] . The timing of the treatment also affects the course of an infection . When the treatment is initiated at the onset of an infection ( which would be the case if hosts are treated following exposure ) , we see a shift of the curves to the right ( Figure 5A , C and E ) . This means that the virus can compensate completely for the fitness decrease due to treatment by increasing its replication rate . The current practice in HCV infections is to treat the patient after several months to avoid treating cases that resolve naturally , and because treatments based on are toxic . We show that delaying the treatment also has an evolutionary advantage because strains that do well in a treated host are also the strains that tend to lead to acute infection ( Figure 2B ) . In order to investigate the effect of treatment at the host level rather than at the host population level , we focussed on the duration of the infection and removed from the analysis hosts who had cleared the infection before the beginning of the treatment ( Fig . 5B , D and F ) . We find similar effects as for the infection fitness: depending on the initial replication rate and on the treatment intensity , the duration of the infection can decrease ( e . g . , if is low and is high ) but it can also increase ( e . g . if is high ) . The only case where the treatment always decreases the duration of the infection is when treatment begins after 180 days ( Figure 5F ) . This is because in the other cases not all the acute infections have ended and treating at that time allows rapidly-replicating strains to persist in the host .
We find that infection fitness ( measured as the number of transmission events per infection ) is maximised for infections initiated by slow-replicating strains . This is partly due to the assumption we make on the immune proliferation function , which allows slow-replicating strains to have the most efficient resource exploitation ( by escaping from the immune response ) . Escaping from the immune response has a second advantage because it gives more opportunities to these strains to generate escape mutants . We are not aware of studies on the evolution of HCV replication rate at the between-host level . In the case of HIV , the subtype C of HIV-1 has been shown to be spreading more rapidly than other subtypes [17] . Interestingly , viruses of this subtype are out-competed when grown with viruses from other subtypes . Even if in vitro conditions have little in common with the within-host environmental conditions , these results suggest that slow replication could be optimal at a population level for rapidly evolving viruses ( see also [54] ) . In epidemiology , the idea that low replication rates can be optimal is not new; it is present in the transmission-virulence trade-off theory [55] , [56] , which states that pathogens should spare their host to maximise the duration of the infection . However , most models ignore within-host evolution of the replication rate ( but see [25] ) . Our nested model provides an explicit description of the within-host mechanisms that drive evolution toward low replication rates . Our model also illustrates the conflict between levels of selection [25]–[27] . At the between-host level , slow-replicating strains have an advantage because they tend to generate longer infections , thus leading to more transmission events . At the within-host level however , rapid replication is favoured through cross-reactive immunity . As discussed in [8] , rapid replication leads to higher activation of the immune response , which penalises slow-replicators ( rapid replication rate being a means to compensate for the immune killing ) . Note that here , contrary to other models , the virus cannot indefinitely evolve to higher replication rates within the host because the immune activation function depends on both viral density and viral replication rate [7] . This explains why , for default parameter values , strains with a replication rate close to 1 are likely to dominate the population over the long term even though the highest infection fitness is reached for replication rates close to 0 . 3 . Moreover , increasing cross-reactive immunity favours more slowly replicating strains in the long term at the between-host level . This effect is much clearer at the between-host level ( Figure 4 ) than at the within-host level ( Figure 2A ) , and also highlights the fact that changes in within-host parameter values can shift the overall selective pressure from the within-host to the between-host level . Our model allows us to study the evolutionary consequences of anti-viral treatments on infection life-history traits . Increasing the efficiency with which a treatment blocks viral replication decreases viral fitness , but also increases the evolutionarily optimal replication rate of the virus . This is consistent with theoretical [57] , [58] and experimental results [59]: treatments decrease viral fitness but they also select for more virulent parasites . Most epidemiological models find a host ‘selfish’ strategy , which consists in increasing its own protection at the expenses of the community [57] . Here , starting a treatment is not always the best option for the host because , in addition to the toxicity of the drugs , treatment can increase the duration of the infection . This is especially true for acute HCV infections , where hosts recover without intervention . Therefore , we stress the importance of a neglected factor in evolutionary epidemiology , i . e . , the timing of treatment administration . We show that administering treatment a few months after the infection begins , imposes a treatment-free period that selects against rapidly replicating strains . Of course , this procedure is acceptable only if the short duration of the infection comes from rapid host clearance and not from host death ( fortunately , acute HCV infection very rarely results in fulminant disease ) . These predictions could be tested empirically by comparing the course of the infection in patients infected by similar HCV genotypes for whom treatment started at different time points . Few studies have attempted to model HCV disease outcomes . Wodarz [60] studied the pathology of HCV with a multi-strain model that described both cellular and humoral immune responses . However , this model did not account for antigenic diversity nor for different viral traits . Population genetics and phylogenetic analysis applied to HCV provide evidence that HCV evolution within-host is under a strong immune pressure [61] and that disease outcome is statistically associated with the number of genetic sites selected [62] . A key feature of our model is that for the exact same parameter values , we can observe chronic or acute infections ( for a review on this topic , see [63] ) . In both cases there is a significant amount of viral diversity generated , which means that this result is not only due to the fact that fewer escape mutants are generated during acute infection outcomes . Finally , we find that there is a delay of a few weeks in launching a specific immune response following viral infection , which is consistent to reports in acute HCV infections . This also echoes a more general concern about most within-host models , in which the outcome of the infection tends to be an assumption rather than a result [38] . We show that replication rate of the initial strain and the parameters describing the immune response alter the probability that one of the two outcomes ( chronic or acute infection ) is reached . The few observations on the correlations between the outcome of HCV infections and the viral replication rate tend to support our results . Data on the growth of HCV in sera suggest that slow replicating viral populations are common in HCV cases with viral persistence [64]–[66] . A direct relationship between the initial viral replication kinetics and cellular responses has also been observed [46] . Moreover , a study based on a peculiar sample of seven people infected on the same day from the same source showed a correlation between viral load and the dominance of a few strains [67] . They also showed that subjects with lower viral loads had a higher diversity that increased over time , thus suggesting an evolutionary process driven by slow-replicating strains , which fits with our result that these strains have the highest infection fitness . The immune response against HCV is puzzling . The mechanisms responsible for the high rate of viral persistence are thought to be the result of complex early host-virus interactions that involve immune system heterogeneity , viral diversity and cross-reactive immunity [63] , [68] . In acute HCV infection there is a significantly broader cytotoxic T-cell response with wider variant cross-recognition capacity than in chronic cases [69] . A significant result of our work is that cross-reactive immunity decreases viral diversity , and infection fitness , thus supporting the idea that viruses face a trade-off: low replication maximises infection fitness but rapid replication helps to escape from cross-reactive immunity . However , one needs to be careful about drawing conclusions from our model regarding the optimal level of cross-reactivity of the immune response , as increasing the width of the cross-reactive immunity function confers a cost-free advantage to the immune system . Therefore , it should not come as a surprise that infection fitness decreases with cross-reactive immunity . A more detailed analysis should introduce a cost to the breadth of the immune response ( for instance in terms of efficiency of killing ) . The between-host dynamics of our model could be extended in several ways . We used an invasion fitness analysis ( with ) , which simplifies the analysis but can also be misleading [70] . Adding a detailed between-host framework would allow to remove the assumption that the size of the susceptible host population remains constant and would lead to more accurate epidemiological dynamics . Modelling the between-host dynamics more explicitly would allow one to take into account the age of the infection . Day [71] shows that variations in transmission rates during the infection ( which is likely to happen if parasite densities vary ) can affect the epidemiology of the disease . Here , as we show in Figures 1E and S2 , the trait of the transmitted strains varies over the duration of the infection . This could significantly impact on the evolution of the trait during an epidemic , for instance if rapidly replicating strains are transmitted later in the infection ( see [6] for a similar discussion in a case with only two strains per host ) . Another extension would be to include several transmission routes for the virus . The model we use here assumes that infections are initiated by a unique viral strain , which is known to be the case for sexual transmission of HIV [72] . However , transmission can also occur through needle sharing in which case the inoculum is more likely to be diverse . Allowing for multiple transmission routes would influence both the within- and between-host dynamics . Regarding the selective forces involved in the evolution of HCV at a population level , there is evidence that the major histocompatibility complex ( MHC ) allele diversity among the population is a major force driving the evolution of the virus [68] . Host heterogeneity could be introduced by assuming that each host type has lymphocytes with different antigenic values for their receptors . This would allow us to identify the optimal viral strategy at the between-host levels for different parameter sets . It would also allow us to further investigate the unresolved issue of why , during HCV infection , mutants that escape the immune responses do not always revert to wild type forms upon transmission . Here , we suggest that this evolutionary process may be influenced by both the antigenic value ( epitope escape ) and the viral replication rate; host diversity is likely to have a strong impact as well . Our work leads to predictions that can be tested experimentally: strains that dominate early in the infection and those that dominate later have different replication rates; and the replication rate of the initial strain and the mutation rate affect the duration and/or the fitness of an infection ( see Table 2 for a summary ) . Of course , some experimental difficulties need to be overcome . First , estimating the replication rate of a circulating strain may be challenging . Second , diversity in replication rates may be difficult to assess if some of the circulating strains are rare . Third , in the case of HCV , it is rare to detect the infection in the early acute phase ( as it is typically asymptomatic ) . Another interesting question raised by this model that calls for empirical support is the heritability of infection life-history traits in the case of rapidly evolving diseases . This question has been studied in the case of HIV virulence , but there is still no convincing evidence that this trait is heritable from one infection to the next [54] . This study stresses the key role played by the cross-reactive immune response in controlling the duration and the fitness of an infection . In addition to the theoretical challenge described above , an experimental validation of this study could be to measure ex vivo the strain-specific immune response and the degree of cross-reactive immunity within-host , and finally to investigate the role of these responses in the outcome of the infection . This model validation could be achieved by identifying strain specific and shared T-cell responses across multiple strains during an infection . Experimental assays such as the Elispot assay , in vitro stimulation and intracellular cytokine detection , and MHC-tetramer staining are established methods to measure the T-cell responses against specific T-cell epitopes [69] , [73] , [74] . However , these assays carry a substantial cost given the large uncertainty and variation in the distribution of MHC class I and II epitopes between infected individuals . Bioinformatics tools have been successfully used to predict pools of MHC class I epitopes ( which can be synthesised as peptides ) to be employed in immunological assays to measure T-cell specific responses [75]; these predictions minimise the experimental effort in identifying T-cell epitopes in terms of cost and timing [76] . One could validate the model predictions by measuring variations in viral replication rates with in vitro assays , and of strain specific immune responses using prediction tools and T-cell response read-out techniques , during the course of the infection . Overall , combining bioinformatics tools , immunological assays , and theoretical models on viral trait evolution forms a promising framework for detecting and measuring the presence of immune selection during the within-host evolution of rapidly mutating viruses .
We model viral dynamics with an immune control model based on a predator-prey-like interaction , where lymphocytes are predators . We assume a finite number of T-cell clones per host , , while the number of viral strains , , varies during the infection . We explicitly take into account the cross-reactivity of the immune response i . e . , the fact that a lymphocyte clone can be activated by and destroy more than one viral strain . Following previous models , we simplify the virus life cycle by focusing on infected cells only [37] , [38] . The reason for this is that we study a case where viral growth is limited by the immune response , not by resource availability ( indeed , only a fraction of the total amount of liver cells seem to be infected , [77] ) . Moreover , the half-life of free viruses has been shown to be low [51] , which supports the assumption that these dynamics can be considered to be at equilibrium . Mathematically , if we denote the number of cells infected by viral strain by and the number of T-cells of type by , the population dynamics are governed by the following system of equations: ( 1a ) ( 1b ) where is what we refer to as the replication rate of strain ( it corresponds to the rate at which viruses of strain i are produced and infect susceptible cells ) , is the base-line death rate of infected cells , is the killing rate of cells infected by viruses of strain by lymphocytes of clone , is the activation rate of lymphocytes of clone by cells infected by viral strain and is the lymphocyte death rate ( the notations used are summarised in Table 1 ) . As in Alizon [7] , the immune activation term does not only depend on the number of infected cells ( ) but on the overall viral growth rate ( , see Introduction ) . T-cells of clone are defined by one trait that does not vary over time: their receptor ( ) . Viruses of strain are defined by an antigen value ( ) . In reality , viruses are recognised by cellular immune responses via multiple antigenic peptides but the combination of peptides is unique and it is this combination that our antigen value reflects . For simplicity and to avoid boundary effects , the receptor and antigen values are chosen on a finite space , i . e . in . The intensity of the cross-reactivity between a lymphocyte clone and a viral strain depends on a measure of the genetic proximity , i . e . . The strength of the cross-immunity between an antigen and a receptor is then given by a function . More precisely , we have ( 2a ) ( 2b ) where is the width ( or breadth ) of the cross-reactive immunity and and are the maximum values for and , respectively . We assume that cross-reactivity affects both the T-cell proliferation and the killing of infected cells . Here , increasing results in an increase in the range of antigen values recognised by receptor . Note that the cross-immunity function has a maximum for . Note also that in the model as we define it in equation system 1 , a viral strain undergoes immune killing by all the lymphocyte clones ( but with variable efficiency ) , which means there is never complete immune escape . For further details on modelling cross-immunity as a function of the distance between antigens and receptors , see [8] and the discussion therein . Equation system 1 only describes within-host dynamics , but nothing is specified concerning evolutionary processes . Here , the number of viral strains varies due to stochastic mutations . Newly infected cells can mutate to a new viral strain with probability . Mutations are modelled as stochastic events following a Binomial distribution ( for details see Text S1 ) . In this model , viruses of strain are defined by two traits , their replication rate ( ) and their antigen ( ) . Each time a virus replicates within a cell , it produces ‘mutants’ , i . e . offspring with different genomes . Mutations in the genome sequence can be silent or affect the phenotype of the virus , and then can be deleterious or advantageous . Here , we consider mutations affecting both the antigen and the replication rate of a virus . Therefore , our mutation event is a composite event that accounts for mutations leading to a new replication rate and to a change in the antigenic value . We analyse the effect of escape mutants with a hybrid stochastic-deterministic approach ( see Text S1 ) , where the population dynamics of the strains are given by equation system 1 . A mutant strain is defined , as any other strain , by its antigen value and its replication rate . The antigen is drawn randomly among one of the possible values ( see above ) . By doing so , we allow for the mutant to ( partially ) evade the existing immune response while limiting the impact of the antigen value on viral evolution . The mutant replication rate depends on that of the original strain . There is little data on the shape of the distribution in which the trait value of a new mutant should be drawn , even if there are strong constraints on RNA genomes [78] . Without further information , we assume for simplicity that the replication rate of the new strain is drawn from a uniform distribution of width centred at the replication rate value of the original strain . We assume a finite number of replication rates that is fixed to 100 in the default case . We wish to stress that our model allows for backwards mutation . By this we mean that a mutant can be identical to another strain in the infection or to a strain that was present earlier in the infection . This approach provides a realistic representation of the quasi-species characteristics of HCV evolution and frees us from potential biases due to infinite allele model assumptions . The number of strains is not constant in these simulations . We follow the evolution of strain diversity by measuring the Shannon index , ( 3 ) where X ( t ) is the total density of infected cells at time t , i . e . . The higher the value of , the more diverse the infection is . We extended the model to study the effect of two types of treatments . The first type of treatment directly increases the killing of infected cells . It is obtained by adding a death term ( ) in equation 1a , where is the treatment efficiency rate . The second type of treatment blocks viral replication . This is modelled by multiplying the by in equations 1a and b , where is the treatment efficiency in reducing viral replication . Treatment efficiencies are assumed not to vary among strains . Further details concerning the model with treatments can be found in the Text S1 . We do not introduce host death in the default case of the model . The main justification for this assumption is that , from the point of view of the virus , host recovery or host death are very similar , the only difference lies at the epidemiological level that is outside the scope of this study . The effect of virulence is shown in Text S2 . We also investigated the effects of a saturation term in the lymphocyte proliferation rate due to resource competition . This term does not change qualitatively the results presented ( see Text S2 ) . Having a nested model requires a careful definition of the viral trait that evolves at the between-host level . Here , we follow the replication rate of the strain that causes an infection ( or ‘initial replication rate’ ) . Can this trait evolve under natural selection at the between-host level ? For this , three conditions must be fulfilled [79]: the trait value must be variable in the population , it must be heritable ( here from one infection to the next ) and it must have a fitness effect . The first condition is fulfilled because viral replication rates are known to vary among infections ( see the Background section ) . The validity of the last two conditions is unknown: so far , no data show that the initial replication rate is heritable from one infection to the next , nor that it has an effect on the infection fitness . Our model allows us to test if these two conditions are fulfilled . If so , we can identify trait values that are optimal at the between-host level . Now that we have specified our trait of interest , we need to introduce a fitness measure for the infection bearing the trait . The ‘fitness’ of an infection can be expressed through the basic reproduction ratio ( ) , which indicates the number of new infections caused by an infected host in a population of susceptible hosts [9] . This is a measure of the invasion fitness and , as discussed in [70] , it can be used to study disease evolution provided that the epidemiological dynamics are simple ( for instance there should not be frequency-dependent feedbacks ) . To estimate the infection fitness , we introduce a random transmission event in the simulation . At each time step , this event occurs with a probability proportional to the log of the total density of infected cells at this time , thus reflecting recent data showing that the transmission rate during primary infections with HIV increases linearly or more than linearly with the viral load [80] ( for further details , see Text S1 ) . The trait of the transmitted strain depends on the proportion of each type of strain at the time the transmission event takes place . We use the number of transmission events during an infection to estimate infection fitness . ( This number correlates with the total number of viruses produced during an infection . ) We also have access to the average replication rate of transmitted strains as a function of the initial replication rate , which allows us to assess the heritability , from one infection to the next , of the trait of interest . In addition to the infection fitness , we investigate the effect of parameter values and initial values on several life-history traits of the infection: the duration of the infection , the final viral diversity ( measured near the end of the infection ) , the final total immune cell density , and the final total viral load . The analysis is performed by varying one of the parameters at the time and running simulations for 22 different initial replication rates ( ranging from to ) . In order to keep the computational time within feasible time scales , we introduce a maximum duration of an infection of days ( 2 years circa ) , which is sufficiently long to represent a chronic infection . An additional reason not to prolong in silico infections beyond 800 days is that long-lasting infections are characterised by impaired immune responses , where the HCV directed immune responses undergo a functional change [21] , which would call for a different modelling approach . To test the robustness of our results , we run each simulation setting 200 times ( see also Text S1 for more details of the simulations ) . Finally , we introduce a weighted average replication rate of transmitted strains . The rationale for this is that the average replication rate of transmitted strains per se is not sufficient to estimate the long-term fitness . For instance , in a very extreme case where only two strains ( A and B ) would be transmitted from an infected host and where the infection fitness of strain A would be close to 0 , it is easy to see that in the long term strain B should be more frequent than strain A . In other words , if one of the transmitted strains generates an infection with negligible infection fitness , it will not contribute to shaping the virus population in the long term . If the infection fitness of an infection caused by a strain with replication rate is denoted , the weighted average replication rate is given by ( 4 ) where is the set containing all the strains transmitted from the host . Note that in the classical average , the weights are assumed to be equal to 1 . Similar methods , such as weighting the contribution of the transmitted viruses with their relative value of fitness at the next generation , are often adopted in evolutionary biology in kin selection models , where the fitness gain obtained by an individual through its offspring ( in terms of inclusive fitness ) has to be weighted by the reproductive value of the offspring [81] . | Rapidly mutating viruses , such as hepatitis C virus , can escape host immunity by generating new strains that avoid the immune system . Existing data support the idea that such within-host evolution affects the outcome of the infection . Few theoretical models address this question and most follow viral diversity or qualitative traits , such as drug resistance . Here , we study the evolution of two virus quantitative traits—the replication rate and the ability to be recognised by the immune response—during an infection . We develop an epidemiological framework where transmission events are driven by within-host dynamics . We find that the replication rate of the virus that initially infects the host has a strong influence on the epidemiological success of the disease . Furthermore , we show that the cross-reactive immune response is key to determining the outcome of the infection ( acute or chronic ) . Finally , we show that the timing of the start of an anti-viral treatment has a strong effect on viral evolution , which impacts the efficiency of the treatment . Our analysis suggests a new mechanism to explain infection outcomes and proposes testable predictions that can drive future experimental approaches . | [
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"co... | 2009 | The Evolutionary Dynamics of a Rapidly Mutating Virus within and between Hosts: The Case of Hepatitis C Virus |
Integrator is an RNA polymerase II ( RNAPII ) -associated complex that was recently identified to have a broad role in both RNA processing and transcription regulation . Importantly , its role in human development and disease is so far largely unexplored . Here , we provide evidence that biallelic Integrator Complex Subunit 1 ( INTS1 ) and Subunit 8 ( INTS8 ) gene mutations are associated with rare recessive human neurodevelopmental syndromes . Three unrelated individuals of Dutch ancestry showed the same homozygous truncating INTS1 mutation . Three siblings harboured compound heterozygous INTS8 mutations . Shared features by these six individuals are severe neurodevelopmental delay and a distinctive appearance . The INTS8 family in addition presented with neuronal migration defects ( periventricular nodular heterotopia ) . We show that the first INTS8 mutation , a nine base-pair deletion , leads to a protein that disrupts INT complex stability , while the second missense mutation introduces an alternative splice site leading to an unstable messenger . Cells from patients with INTS8 mutations show increased levels of unprocessed UsnRNA , compatible with the INT function in the 3’-end maturation of UsnRNA , and display significant disruptions in gene expression and RNA processing . Finally , the introduction of the INTS8 deletion mutation in P19 cells using genome editing alters gene expression throughout the course of retinoic acid-induced neural differentiation . Altogether , our results confirm the essential role of Integrator to transcriptome integrity and point to the requirement of the Integrator complex in human brain development .
Malformations of cortical development ( MCD ) are a group of neurodevelopmental disorders characterized by structural brain abnormalities involving the human cerebral cortex . They form a common cause of developmental delay and epilepsy , accounting for 3% of intellectual disability , 25% of pediatric partial seizures , 5–15% of adult epilepsy , and 20–40% of therapy-resistant epilepsy [1–5] . MCD are divided into three main groups , reflecting failure of either the neurodevelopmental process of cell proliferation , neuronal migration , or post migrational cortical organization [6] . It was anticipated that mutations in genes unique to each disorder and to specific developmental stages would be identified . Recently , this classification has been challenged by the discovery of mutations in genes like WDR62 and TUBA1A [1 , 7 , 8] that lead to multiple malformations originating in different stages of brain development . Among MCDs , periventricular nodular heterotopia is considered a developmental defect of neuronal progenitors that fail to migrate from the ventricular and subventricular zone toward the upper cortical layers during early embryogenesis or undergo premature differentiation in apparent neuronal lineage . The most common genetic cause of periventricular nodular heterotopia is a mutation in the FLNA gene , which is associated with normal cognitive development . Rare gene mutations including ARFGEF2 and several rare chromosomal aberrations explain a small proportion of syndromic periventricular nodular heterotopia , associated with other malformations and/or developmental delay ( for review see [6 , 9] ) . It is rarely associated with cerebellar hypoplasia and microcephaly . Moreover , many cases of periventricular heterotopia remain without molecular etiology [10] . The Integrator complex ( INT ) consists of at least 14 subunits and is phylogenetically conserved among metazoans [11 , 12] . Integrator associates with the C-terminal domain of the largest subunit of the RNA polymerase II ( RNAPII ) and has a role in the regulation of gene expression and RNA processing . It was first discovered to mediate the co-transcriptional 3’-end processing of the U-rich small nuclear RNAs ( UsnRNAs ) , the RNA components of the spliceosome [11 , 13] . Recently , the scope of Integrator complex function has broadened through the discoveries of a general role in RNAPII promoter proximal pause-release and in the processing of enhancer RNAs [14–18] . Most Integrator complex subunits bear no homology to any RNA processing machinery or transcriptional regulators that would allow a predictable function within the complex , with the exception of Integrator complex subunits INTS9 and INTS11 [19] . These subunits are paralogues of the cleavage and polyadenylation specificity factor subunits CPSF100 and CPSF73 respectively , which form the endonuclease factor responsible for cleavage of pre-mRNA at the polyA site [20–22] . Although animal studies suggest an evolutionary conserved requirement of Integrator complex subunits for normal embryonic development [23–28] , human germline mutations have not yet been linked to disease . Interestingly , mutations in UsnRNA have been previously linked to splicing alterations leading to brain disorders . For example , mutations in the mouse Rnu2-8 , coding for U2 snRNA , result in cerebellar degeneration [29] , RNU4atac mutations in humans result in extreme microcephaly [30] and mutations in RNU12 have been found associated with early-onset cerebellar ataxia[31] . Recently , mutations in the non-canonical deadenylase TOE1 that trims the 3’ ends of pre-UsnRNA , have been linked to pontocerebellar hypoplasia[32] . Moreover , promoter-proximal pausing , affecting up to 40% of the genes , is particularly important during development [33 , 34] . Paused RNAPII is preferentially encountered at developmentally-regulated genes where it can orchestrate the synchronous gene activation necessary for pattern formation [35–38] . This process is particularly important during neuronal development , synapse plasticity and maturation [39–41] . Altogether , these data indicate that , through UsnRNA processing and RNAPII promoter-proximal pausing , reduced Integrator activity could have an impact on normal human neurodevelopment . Here , we describe the first report of INT mutations that are associated with severe neurodevelopmental defects . We identified mutations in two distinct INT subunits , INTS1 and INTS8 , which are present in six patients from four unrelated families . We determined that patients with INTS8 mutations carry two distinct alleles where one leads to the production of an unstable transcript while the other deletes three conserved amino acids near the C-terminus that disrupts integrity of the whole complex . We detect significant splicing and transcriptional defects in patients cells and demonstrate that replacement of intact INTS8 genes with the deletion mutant using genome editing is sufficient to disrupt retinoic-acid induced neuronal differentiation in P19 cells .
In our research cohort of patients with brain abnormalities we identified six individuals from four families with a distinct and recognizable neurodevelopmental syndrome . Clinical details are presented in Table 1 . In summary , all patients shared profound intellectual disability , lack of speech development , motor impairment , seizures and similar dysmorphic features of the face and limbs . In addition , the three individuals from the same sibship had severe spastic tetraplegia , borderline microcephaly , and an abnormality at brain MRI: cerebellar hypoplasia , reduced volume of pons and brainstem and periventricular nodular heterotopia ( PNH ) , a migration defect of the cortical neurons ( Fig 1A–1F ) . Two of the other unrelated individuals of Dutch ancestry sharing a similar phenotype underwent exome sequencing for clinical diagnostic purposes . In both patients the same homozygous nonsense mutation in INTS1 was found c . 5351C>A , p . ( Ser1784* ) , ( NM_001080453 ) , whereas all the parents were heterozygote for the mutation . The last individual of Dutch ancestry was diagnosed because of recognizable dysmorphic features and was found by targeted analysis to be homozygote for the p . ( Ser1784* ) mutation , while his parents were heterozygotes . Additional genomic analysis , using Illumina Infinium_CytoSNP_850K v1 . 1 genotyping array , was performed which showed in all three individuals a shared region of homozygosity ( according to UCSC Genome Browser Mar . 2009 ( NCBI37/hg19 ) ) , suggesting this mutation was derived from a common ancestor . This mutation is not reported in ExAC ( http://exac . broadinstitute . org ) , nor in gnomAD databases ( http://gnomad . broadinstitute . org/ ) . Expression of mRNA bearing the p . Ser1784* mutation in skin fibroblasts derived from two patients was significantly reduced when tested by qRT-PCR ( S1 Fig ) , compatible with a loss of function effect . Whole genome sequencing ( WGS ) using DNA from the three affected siblings and three unaffected family members identified biallelic mutations in the Integrator Complex subunit 8 gene ( INTS8 ) in the affected individuals , following an autosomal recessive inheritance pattern ( Fig 1G , S1 and S2 Tables ) . The first mutation is a predicted missense mutation ( c . 893A>G , p . Asp298Gly ) , and the second is an in-frame nine-base-pair deletion leading to the deletion of three amino acids ( c . 2917_2925del , p . Glu972_Leu974del; simplified as INTS8ΔEVL in the rest of the text ) ( Fig 1H ) . Both mutations are not reported in either the ExAC or the gnomAD databases . The mutations and their segregation in the family were confirmed by Sanger sequencing ( Fig 1I–1L ) . Interestingly , the c . 893A>G mutation arose de novo in the father . INTS8 is a 995-amino-acid protein , containing four predicted tetratricopeptide ( TPR ) motifs , which are versatile protein-protein interaction domains . Both mutations reside in conserved regions of the protein and INTS8ΔEVL is located at the C-terminus of the TPR4 domain in a predicted alpha helix ( Fig 1H ) . In spite of sequencing INTS8 in 25 PNH patients and 266 other patients with brain malformations , we did not observe additional biallelic INTS8 mutations , which suggests that INTS8 mutational rate is very low . This is supported by the high combined annotation dependent depletion ( CADD ) score of the mutations ( S3 Table ) [42] and , with a score of 0 . 99 , being classified as an extremely “loss-of-function intolerant” gene in the ExAC database ( http://exac . broadinstitute . org ) . To study whether and how these mutations affect INTS8 expression we cultured cell lines from primary fibroblasts from the three affected siblings and their unaffected brother , who is heterozygous only for the INTSΔEVL mutation . qRT-PCR analysis indicates that INTS8 expression is reduced in patient cells ( ~50% reduction ) suggesting that one of the mutant INTS8 alleles is not expressed ( Fig 2A ) . To test this possibility , we designed a different set of primers incapable of amplifying the INTS8ΔEVL allele and found that the expression of the INTS8 c . 893A>G allele is almost undetectable in patients ( Fig 2A ) . Close analysis of known INTS8 transcripts in the NCBI RefSeq database indicates that the INTS8 c . 893A>G mutation is located at position +1 of the exon 8 of an annotated alternative 3′ splice site used in isoform NR_073445 . 1 ( variant 3 , Fig 2B ) . This variant is predicted to generate a premature stop codon within exon 8 and to be subjected to rapid degradation through nonsense mediated decay ( NMD ) . To test the impact of c . 893A>G on exon 8 splicing , we designed an INTS8-GFP minigene-reporter construct ( Fig 2C ) . Wild-type-INTS8 minigenes transfected into HeLa or HEK293T cells generated two distinct exon inclusion products ( Fig 2D , lane 2 and 5 ) , confirmed by Sanger sequencing to be variants 1 and 3 ( S2A and S2B Fig ) . Strikingly , introduction of the single base c . 893A>G mutation into the INTS8 reporter was sufficient to change the splicing of exon 8 so that the distal 3′ splice site used in the INTS8 variant 3 was now exclusively utilized ( Fig 2D , lane 3 and 6 ) . Cloning and sequencing of this single spliced product confirmed the predicted exon junction ( S2C Fig ) . Both endogenous isoforms can be detected in HeLa cells but variant 3 constitutes only 6% of total cellular INTS8 mRNA ( S2D and S2E Fig ) . Treatment of HeLa or HEK293T cells with puromycin or cycloheximide increased variant 3 RNA levels to 43% and 34% , respectively ( S2E Fig ) , which indicates that variant 3 is subject to rapid degradation , probably through NMD [43] . Similarly , baseline levels of INTS8 variant 3 in patient cells are also increased after mRNA stabilization by puromycin or cycloheximide treatment ( S2F Fig ) . Collectively , these results show that the c . 893A>G mutation dramatically alters the splicing of INTS8 exon 8 , producing a premature stop codon and an unstable transcript . As the c . 893A>G allele produces an unstable mRNA , we deduced that the mutant INTS8ΔEVL is the preponderant protein expressed as suggested by the levels of mRNA expression in patient cells ( Fig 2A , middle panel ) . Therefore , we investigated whether the INTS8ΔEVL protein could impact INTS8 interaction with the other subunits in the complex . To that end , we established HEK293T cells stably expressing wild-type ( WT ) or mutant ( ΔEVL ) INTS8 bearing an N-terminal 3XFLAG tag and purified the associated complex using anti-FLAG affinity resin . WT and mutant INTS8-associated peptides showed a very similar pattern by SDS-PAGE ( S3 Fig ) . Probing for the presence of specific Integrator complex subunits by western blot revealed however differential association with the two proteins . The FLAG-INTS8-ΔEVL eluates contained nearly undetectable amounts of INTS1 , -12 and RPB1 , reduced levels of associated INTS4 , -9 and -11 , but similar levels of INTS5 and INTS3 , compared to FLAG-INTS8–WT ( Fig 2E ) . Thus , the ΔEVL mutation appears to impact the ability of INTS8 to associate with selected members of the Integrator complex . In patient cells , we found slightly reduced levels of several INT subunits including INTS3 , INTS5 , INTS11 and INTS12 ( Fig 2F ) . We also observed significantly reduced levels of INTS4 and INTS9 ( Fig 2F ) . The reduction in quantity of several INT subunits could be due to reduced mRNA expression or protein accumulation suggesting that the reduced association of INTS8 with certain members of Integrator complex could lead to an overall loss in the protein complex integrity and affect in return the stability of some of its subunits . Consistent with this observation , we measured slight but significantly elevated amounts of misprocessed UsnRNA in patient cells ( Fig 2G ) , while total UsnRNA levels did not significantly differ between patient and control cells ( S4 Fig ) . Taken together , these results indicate that the INTS8 mutations lead to both a reduction in integrity and function of the Integrator complex . To investigate the extent of INTS8 dysfunction on transcription and splicing , we conducted both exon array analysis and RNA-seq on patient cells relative to controls . Differential gene expression ( DGE ) data from exon arrays performed on fibroblasts showed a large number of significantly dysregulated genes , including INTS8 , in the patient vs . control cells ( n = 682; p<0 . 02 , S4 Table ) . To confirm both the results and the reproducibility attained by exon array we selected four of the highly dysregulated genes that are also known to be expressed in brain and important in CNS development and tested their expression using qRT-PCR . Importantly , we were able to successfully confirm the results using two independent qRT-PCR experiments comparing the three patients with two new and independent age-matched fibroblast control cell lines ( S5 Fig ) . To further explore a genome-wide effect on splicing we subjected poly ( A ) mRNA fractions from patient III-2 and III-4 and 2 age-matched controls to high depth RNA-seq analysis ( ~65-fold average coverage , S5 Table ) . We found an even larger number of genes ( N = 3 , 002; p<0 . 02 ) that were significantly up- or downregulated in patient cells vs . control cells ( S6 Table ) . We validated 17 of these potential target genes by qRT-PCR ( illustrative examples are in Fig 3A and 3B , S6 Fig ) . Comparison of DGE data between exon arrays and RNA-seq showed a correlation coefficient of 0 . 66 between the two data sets ( Fig 3C ) and , interestingly , that a majority of the genes that are significantly dysregulated in both datasets ( N = 82 ) are expressed in the CNS ( S7 Table ) . In addition to transcriptional deregulation , we also detected significant splicing changes in 215 genes ( p<0 . 01 , 292 total events , S8 Table ) and confirmed the INTS8 alternative splicing induced by the c . 893A>G mutation in patient cells . The vast majority of affected splicing events are skipped exons ( 65% ) and mutually exclusive exons ( 19% , Fig 3D ) . We selected four alternatively spliced genes and confirmed the corresponding splicing changes using RT-PCR assays with primers flanking differentially spliced regions ( Fig 3E and 3F , S7 Fig ) . Altogether , these multiple analyses demonstrate broad changes in both splicing and transcript levels in patient cells containing disrupted INTS8 expression . In situ hybridization ( ISH ) data in mouse embryonic brain show a high expression of INTS8 in the CNS at E14 . 5 , especially in the brain cortex ventricular zone ( VZ ) and hindbrain ( S8 Fig ) [44] . In humans , heat maps extracted from expression array data of several brain areas show high expression of INTS8 in the ventricular and subventricular zones , caudal and lateral ganglionic eminences and cerebellar primordium at 16–21 postconceptional week ( pcw ) ( S8 Fig ) . In the first and second trimester , from the ganglionic eminences , GABAergic interneuron progenitors migrate tangentially in the subventricular and marginal zone of the telencephalon in order to organize cortical development; meanwhile the subventricular zone is also an area of active proliferation of glutamatergic neural progenitors [45] . Analysis with the R software of raw RNAseq data of INTS8 expression at several developmental stages ( range 8 pcw—40 years ) across different human brain areas and multiple individuals , obtained from BrainSpan , shows that INTS8 expression peaks during early embryonic development , and decreases to retain a stable level during postnatal life ( S9 Fig ) . The spatiotemporal expression pattern of INTS8 suggests co-localization with both pyramidal and interneuron progenitors regulating neuronal migration and is in accordance with the PNH phenotype . To investigate whether the INTS8ΔEVL mutation could have an impact on neuron differentiation we used CRISPR/Cas9-mediated genome editing to introduce a homozygous EVL deletion in mouse P19 pluripotent embryonic carcinoma cells ( Fig 4A ) . After recombination , clonal selection and screening , we retained two lines bearing homozygous INTS8ΔEVL mutations ( ΔEVLA and ΔEVLB ) . Similarly to what we observed in fibroblast cells from patients , western blot analysis of Integrator complex subunits shows that expression levels of several subunits are reduced in the mutant cell lines compared to the parental line ( Fig 4B ) , indicating that introduction of the ΔEVL mutation into INTS8 is sufficient to disrupt accumulation of other members of INT . Additionally , we could also detect significant levels of U11 and U12 misprocessed snRNA in mutant cells indicating that not only is the integrity of INT disrupted in these cells but also its function ( S10 Fig ) . We then used these cell lines as an in vitro model of retinoic acid ( RA ) -induced neuron differentiation . After RA treatment , we followed by qRT-PCR the expression of different markers for pluripotency , retinoic acid response , neuronal precursor and neuronal differentiation . While the timing of the appearance or the morphology of differentiated neurons did not significantly differ between the two ΔEVL mutants and the parental P19 control , we observed marked differences in gene expression over the course of the process ( Fig 4C ) . In response to RA , the induction of hoxa1 ( direct transcriptional target of RA ) is markedly reduced in the mutant lines even though rapid downregulation of pluripotency genes like Oct4 is not altered . Later on , although not altered in its intensity , the induction of the neuronal stem cell marker nestin ( nes ) is temporally distinct between the control and the mutants , where its expression is delayed at first and later on more rapidly induced than in the control line . Finally , the induction of several neuronal markers such as tubulin beta-3 ( tubb3 ) or reelin is reduced while others remain similar to the control ( synapsin-1 , syn1 ) . These experiments confirm that the INTS8ΔEVL mutant protein can have a profound effect on gene expression during a neuronal differentiation process .
The present study provides a rare insight into the effect of an Integrator complex deficiency in humans . We identified biallelic mutations in the Integrator complex subunit INTS8 in three siblings and in Integrator complex subunit INTS1 in three unrelated individuals with a rare and severe developmental brain disorder and similar phenotypic abnormalities . The INTS1 mutation leads to strong reduction of its mRNA expression in skin fibroblasts . In the case of INTS8 , we show that one INTS8 mutant allele leads to rapid mRNA decay while the other translates into a protein lacking three residues in the C-terminus that impacts the overall stability of the Integrator complex . Our results indicate that Integrator-deficient patient cells contain global transcriptome perturbations manifesting as both altered splicing patterns and differential gene expression . Finally , the introduction of the INTS8ΔEVL mutation in P19 embryonic carcinoma cells also alters the pattern of differentiation marker expression during RA-induced neuronal differentiation . The phenotype among the individuals with INTS8 and INTS1 mutations is similar , combining profound intellectual disability , epilepsy , lack of speech , facial and limb dysmorphism , altogether forming a recognizable syndrome . In patients with INTS8 mutations , it is also associated with a rare combination of structural brain malformations including PNH and cerebellar hypoplasia . This association of PNH , a neuron migration disorder , with an Integrator subunit mutation is particularly interesting in view of the recent observation of a neuronal migration defect in mouse embryonic brain after Ints1 and ints11 knock-down and could implicate the Integrator complex function in the etiology of the disorder [46] . In spite of extensive search , we did not observe additional INTS8 mutations in other individuals with similar combination of PNH and cerebellar hypoplasia , which suggests that either this association of brain malformations is not constant in this disorder or this is an ultra-rare disorder ( i . e . caused by mutation in a gene with very low mutational rate ) [47] . The fact that the other phenotypic features are shared with the INTS1 mutations supports the former , while the high CADD score of the mutations and the very few INTS8 deleterious variants reported in control populations support the latter ( S2 Table ) . This also suggests that complete loss of INTS8 could be incompatible with human life . The same may be true for most Integrator complex subunits as loss of any Integrator complex component tested to date has proven lethal in various animal models at early developmental stages [23–28] . Yet , it is intriguing that although INTS8 is ubiquitously expressed , the brain is disproportionally affected by its disruption . Considering that INTS8 expression peaks in the developing fetal brain , these observations point to a specific role for INTS8 and more generally for the Integrator complex during brain development . Using in-depth analysis of genome-wide alternative exon usage we show that in patient cells the splicing pattern of up to 215 individual genes is affected . This finding could be compatible with a functional defect of the spliceosome and correlates with the increased level of misprocessed UsnRNAs that we observe in patient cells . Several of these alternatively spliced genes can be individually linked to brain-related phenotypes and could therefore individually or collectively contribute to the phenotype even though no gene known to be directly involved in cortical malformation ( heterotopia , with/without cerebellar hypoplasia and microcephaly ) show abnormal splicing in patient fibroblasts [6] . However , among the alternatively spliced genes that we identified SPTAN1 is of particular interest ( S7 Fig ) . Indeed , our RNA-seq analysis indicates that exon 37 of the gene is almost completely skipped in patient cells . Patients with SPTAN1 mutations present severe intellectual disability , no visual tracking , epilepsy and spastic tetraplegia . Brain imaging shows cerebellar hypoplasia , acquired microcephaly and hypomyelination [48] . Hence , patients with SPTAN1 mutations share many rare features with the patients described in this study . Also RPGRIP1L , mutated in Joubert syndrome with cerebellar hypoplasia , retinal dystrophy and variable cortical malformation , has disrupted alternative splicing in patients with INTS8 mutations . Although we find a broad disturbance of splicing patterns , it is only speculative to relate the results of individual genes in fibroblast cell lines to brain development . Numerous studies have however demonstrated that the brain relies heavily on alternative splicing to regulate neuronal development [49–54] . Moreover , spatiotemporal control of alternative splicing is crucial in the generation and differentiation of neuronal progenitors [55] . This is supported by the finding that mutations in the splicing factor RNA-binding motif RBM10 cause abnormal mRNA splicing , microcephaly , PNH , and cerebellar hypoplasia [56] . In addition , alterations in UsnRNAs have severe consequences on brain development in both mice and humans . A mutation in the mouse Rnu2-8 gene , coding for U2 snRNA , results in abnormal pre-mRNA splicing of specific transcripts and in cerebellar degeneration [29] . Similarly , mutations in the minor spliceosome U4atac snRNA gene in humans result in splicing defects and the rare disorder microcephalic osteodysplastic primordial dwarfism type I ( MOPD1 ) [30 , 57] . Mutations in another minor spliceosome snRNA , RNU12 , result in U12-type exon retention and are associated with cerebellar ataxia[31] . Therefore , a toxic effect of the accumulation of misprocessed UsnRNAs on the spliceosome in response to INTS8 mutations is possible and could result in the differential splicing patterns that we observed in patient versus control cells . Our results show a great number of differentially expressed genes in the patient cells . Significantly and reproducibly the top dysregulated genes in exon arrays are NOG ( OMIM 602991 , encoding noggin ) and TUBA1B that both have a primary role in brain and development ( S7 Fig ) . Noggin inhibits BMP4 , one of the major bone morphogenic proteins required for growth and patterning of neural tube while TUBA1B ( OMIM 602530 , coding for tubulin-beta ) shows its highest expression in brain and is one of the several tubulin genes essential for normal cortical development in human . Tubulin-beta forms a dimer with tubulin-alpha , encoded by TUBA1A , a gene mutated in syndromic cortical malformation with microcephaly , cerebral and cerebellar dysgyria [7] . On a mechanistic level , the effects of INTS8 mutation on gene expression could be linked to the newly identified function of Integrator complex in the regulation of RNAPII-dependent transcriptional initiation , pause-release and elongation of protein-coding genes [14 , 16 , 17] . This finding greatly expanded the scope of Integrator complex function as well as its potential impact on transcription , particularly on genes known to be regulated by promoter-proximal pausing such as immediate early genes ( IEGs ) [14] . Moreover , at the level of the neuron itself , promoter-proximal pausing and IEGs play an important role in neuronal development , synapse plasticity and maturation through neuronal activity-dependent transcription activation [39–41] . It is therefore possible that part of the disease pathogenesis associated with INTS8 mutations also results from widespread transcription deregulation , apart from abnormal splicing . A definitive answer will require a detailed analysis of INTS8 role in neuronal differentiation and in brain development . Our results in P19 differentiation by retinoic acid indicates that INTS8ΔEVL mutation can indeed cause misprocessing of snRNA and can affect expression of many differentiation-regulated genes and could therefore have consequences on neuronal and brain development . The recent progress in genetic engineering ushered by the development of CRISPR/Cas9-based genome editing tools will enable the development of animal models tailored in the future to address these questions . Likewise , progress in cell reprogramming and induced pluripotent cell production should allow for the direct use of patient cells to study the impact of INTS8 mutations on neuron differentiation in their original genetic background . Our study provides the first evidence for a crucial role of the Integrator complex during human brain development . INTS8 is essential for the structural and functional integrity of Integrator complex , and mutated INTS8 causes increased UsnRNA misprocessing , increased AS events and altered gene expression , confirming a central role for Integrator complex in transcriptional regulation and , together with INTS1 mutations , an unexpected role in human brain development .
All study participants or their legal caretakers gave written informed consent to participate in this study , and for publication of images , according to Erasmus MC institutional review board requirements ( protocol METC-2012387 ) . Details of the Whole-genome sequencing ( WGS ) , whole-exome sequencing ( WES ) and Sanger sequencing are provided in Supplemental methods ( S1 Text ) . Data are deposited internally at the Erasmus MC in respect to the privacy of the families . Quantitative qRT-PCR of RNA extracted from cultured fibroblasts of the affected siblings , their unaffected brother , and two control cell lines was carried out using a KAPA SYBR FAST qPCR Kit ( Kapa Biosystems ) in the CFX96 Real-Time system ( BioRad ) . Details are provided in S1 Text . qRT-PCR was performed using RNA from cultured fibroblasts on a Stratagene Mx3000P real-time PCR system ( Agilent ) using the KAPA SYBR FAST qPCR Kit ( Kapa Biosystems ) according to manufacturer’s instructions . Details are provided in S1 Text . Exon 8 and flanking intronic sequences of human INTS8 gene were amplified from HEK293T genomic DNA by PCR ( primers in S1 Text ) . The amplicon was cloned into the pGint vector [58] using BamHI and SalI restriction sites . The A893G mutation was introduced by site directed mutagenesis ( primers in S1 Text ) . The resulting constructs were transfected in HEK293T and HeLa cells using Lipofectamine2000 ( Thermo Fisher ) . After 48h , total RNA was extracted , purified and used to generate cDNA using M-MLV reverse transcriptase ( Thermo Fisher ) using manufacturer specifications . PCR amplification of the corresponding splicing product was performed . For detection and quantification , oligonucleotides were 5’ radiolabeled using 32P-γATP and T4 Polynucleotide Kinase ( Thermo Fisher ) and added in a 1/10 ratio with unlabeled oligonucleotides . The corresponding PCR reactions were resolved onto a 6% non-denaturing acrylamide gel , fixed and dried . The gels were scanned using a storage phosphor screen and a Storm scanner ( GE Healthcare ) and quantified using ImageQuant software ( GE Healthcare ) . The human INTS8 cDNA was amplified by PCR from HeLa cell cDNA and the corresponding PCR product was cloned into a modified pCDNA6 plasmid containing an N-terminal 3XFlag tag ( See S1 Text ) . The EVL deletion was introduced by site-directed mutagenesis . HEK293T cells were transfected with either construct . Flag-affinity purification was performed as in [11] using nuclear extracts from approximately 109 cells . Integrator subunits in the eluate were detected by Western blot using the following antibodies: anti-FLAG M2 ( Sigma ) , INTS1 ( Bethyl , A300-361A ) , INTS3 ( Bethyl , A302-050A ) , INTS4 ( Bethyl , A301-296A ) , INTS5 ( Abcam , ab74405 ) , INTS9 ( Bethyl , A300-422A ) , INTS11 ( Bethyl , A301-274A ) and INTS12 ( Proteintech , 16455-1-AP ) . Oligonucleotide sequences are listed in S1 Text . Cultured fibroblasts were used for western blots of Integrator complex components . Approximately 32 μg of the clarified whole cell extract were separated on an 8% acrylamide SDS-PAGE gel . After transfer to a PVDF membrane , the presence of the different Integrator complex subunits was assessed by Western blot using the antibodies listed above . Six Affymetrix GeneChip Human Exon 1 . 0 arrays of RNA extracted from cultured fibroblasts of all three affected siblings and three age and sex-matched controls were robust multi-array ( rma ) normalized on transcript level using the R package ( http://www . r-project . org ) . Top down- and upregulated genes with p values <0 . 02 were analyzed for enriched gene ontology terms ( goterms_BP_all ) using DAVID ( medium stringency ) to identify clusters with significant enrichment ( enriched score ≥ 1 . 3 ) . CEL file microarray data are available under GEO accession number GSE48849 ( http://www . ncbi . nlm . nih . gov/geo/ ) . Poly ( A ) mRNA fractions isolated from cultured fibroblasts from patient III-2 , III-4 , and two controls were subjected to RNA-Seq analysis . In order to detect even slight changes in splicing , we utilized high depth RNA sequencing ( S4 Table ) . The resulting data was analysed using a recently developed pipeline specifically designed to monitor splicing efficiency [59] . In addition , we analysed patient and control RNA-Seq data for differences in gene expression at steady state levels using EdgeR . Data are deposited in Gene Expression Omnibus ( GSE76878 ) . Differentiation of P19 cells was conducted according to [60] . Briefly , 107 exponentially growing cells were seeded on agarose-coated plate in a culture medium without serum containing 1μM all-trans Retinoic Acid ( sigma ) and N-2 supplement ( Thermo Fischer ) . After 48h , embryonic bodies were collected and seeded on tissue culture plates in medium with serum . After 48h , medium was replaced with culture medium without serum containing N-2 supplement . Half of the culture medium was replaced every 48H with fresh medium . RNA was extracted at day0 , day2 , day4 and day8 using Trizol ( Thermo Fisher ) . The mRNA expression levels of neuronal differentiation markers were monitored by qRT-PCR ( see S1 Text for details ) . Genome editing was conducted as in [61] with minor variations . See S1 Text for details . | Neurodevelopmental disorders often have a genetic cause , however the genes and the underlying mechanisms that are involved are increasingly diverse , pointing to the complexity of brain development . For normal cell function and in general for normal development , mechanisms that regulate gene transcription into mRNA are of outermost importance as proper spatial and temporal expression of key developmentally regulated transcripts is essential . The Integrator complex was recently identified to have a broad role in both RNA processing and transcription regulation . This complex is assembled from at least 14 different subunits and several animal studies have pointed to an important role in development . Nevertheless , studies directly demonstrating the relevance of this complex in human health and development have been lacking until now . We show here that mutations in the Integrator Complex Subunit 1 gene ( INTS1 ) and Subunit 8 gene ( INTS8 ) cause a severe neurodevelopmental syndrome , characterized by profound intellectual disability , epilepsy , spasticity , facial and limb dysmorphism and subtle structural brain abnormalities . While the role of the Integrator complex in neuronal migration has recently been established , we provide evidence that INTS8 mutations lead in vitro to instability of the complex and impaired function . In patients cultured fibroblasts we found evidence for abnormalities in mRNA transcription and processing . In addition , introduction of INTS8 mutations in an in vitro model of retinoic acid-induced neuronal differentiation results also in transcription alterations . Altogether our results suggest an evolutionary conserved requirement of INTS1 and INTS8 in brain development . | [
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"d... | 2017 | Human mutations in integrator complex subunits link transcriptome integrity to brain development |
Dengue is the most important vector-borne viral disease worldwide and a major cause of childhood fever burden in Sri Lanka , which has experienced a number of large epidemics in the past decade . Despite this , data on the burden and transmission of dengue virus in the Indian Subcontinent are lacking . As part of a longitudinal fever surveillance study , we conducted a dengue seroprevalence survey among children aged <12 years in Colombo , Sri Lanka . We used a catalytic model to estimate the risk of primary infection among seronegative children . Over 50% of children had IgG antibodies to dengue virus and seroprevalence increased with age . The risk of primary infection was 14 . 1% per year ( 95% CI: 12 . 7%–15 . 6% ) , indicating that among initially seronegative children , approximately 1 in 7 experience their first infection within 12 months . There was weak evidence to suggest that the force of primary infection could be lower for children aged 6 years and above . We estimate that there are approximately 30 primary dengue infections among children <12 years in the community for every case notified to national surveillance , although this ratio is closer to 100∶1 among infants . Dengue represents a considerable infection burden among children in urban Sri Lanka , with levels of transmission comparable to those in the more established epidemics of Southeast Asia .
Dengue is considered to be the most important mosquito-borne viral disease affecting humans today [1] . Between 50–100 million cases occur worldwide each year , resulting in an estimated 500 , 000 hospitalizations and 20 , 000 deaths; approximately two-thirds of the world's population lives in areas colonized by Aedes mosquitos , the principal vector for dengue viruses [2] . Dengue viruses thrive in urban areas that support large Aedes populations and close contact between infectious vectors and susceptible human hosts [1] , [3] . Dengue was first serologically confirmed in Sri Lanka in 1962 , with the first island-wide outbreak being reported in 1965 [4] . Although Sri Lanka has had a history of over 40 years of dengue , since the early 2000s , progressively large epidemics have occurred at regular intervals . Dengue transmission in Sri Lanka is endemic , but unusually large epidemics were experienced in 2004 and 2009 with the peak transmission occurring in June , following the southwesterly monsoon . Dengue is now considered to be hyperendemic in Sri Lanka , involving co-circulation of multiple serotypes [5] , [6] . In 2012 , 44 , 456 dengue cases were notified , corresponding to a rate of 220 per 100 , 000 population; approximately a quarter of notified cases occur in children under 15 years . Despite this , little is known about the epidemiology of dengue and the transmission of dengue viruses among children in Sri Lanka , in whom the risk of severe forms of the disease , including dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) , is considerably higher . In this paper , we estimate the risk of dengue primary infection among dengue-naive individuals using data from a seroprevalence survey in the paediatric population of Colombo , Sri Lanka .
Ethical approval for the study was obtained from the Ethical Review Committee of the Faculty of Medicine , University of Colombo . Permission to conduct the study was obtained from the Special Commissioner of the Colombo Municipality and the Chief Medical Officer of Health , Municipal Council Colombo . Ethical approval was also obtained from the following institutions: The Human Subjects Protection Committee of the Pediatric Dengue Vaccine Initiative , International Vaccine Institute , Korea; The Research Committees of the Lady Ridgeway Children's Hospital and Medical Research Institute , Sri Lanka; The Advisory Committee on Communicable Diseases , Ministry of Health , Sri Lanka; The Ethical Review Committee of the University of North Carolina , Chapel Hill , USA; The Ethical Review Committee of the London School of Hygiene & Tropical Medicine , UK . History of vaccination against Japanese Encephalitis ( JE ) virus was ascertained by parental consent and verified from vaccination records where available . Signed , informed consent to take part in the study was obtained from the child's parent or guardian . A census of Municipal Council Ward 33 ( MCW33 ) , Colombo , was carried out between September and October 2008 to enumerate all households and children <12 years in the area . MCW33 covers an area of 37 . 3 km2 and comprises a relatively homogeneous population of low-income families . The population of children <12 years at the last census in 2001 was 4737 . Prior to the census , a digital map of the area was constructed and the area divided into census blocks . In each block , a designated field worker visited every residence and obtained information on all children <12 years living in the household and socioeconomic characteristics of the household . Following the census , 802 children from 504 randomly selected households were recruited into the study . Selection of households within blocks was proportional to population size . Recruitment took place between November 2008 and January 2009 . Children were excluded if they were at increased risk of harm following a blood draw , had hemophilia , leukemia or thrombocytopenic purpura . At enrolment , a baseline dengue seroprevalence survey was conducted . A fingerprick blood sample was taken from every child on filter paper strips for antibody testing ( Dried Blood Spot Saver Cards manufactured by ID Biological Systems , Greenville , South Carolina , USA: Ref IDBS1003 ) . Each sample was labeled , air dried and transported in a special container to the study laboratory at the end of the day . History of vaccination against Japanese Encephalitis ( JE ) virus was ascertained from parents or legal guardians and verified from vaccination records where available . The data used in this analysis come from this baseline seroprevalence survey . The dried blood spot samples were stored with desiccants at −20°C until testing . Dengue virus seropositivity was determined by dengue IgG ELISA as previously described [7] and OD values ≥0 . 3 were considered flavivirus antibody positive . This analysis includes data from 797 children for whom age on the date of the baseline sample could be confirmed . We used a catalytic model to estimate the dengue force of infection among dengue naive children from age-specific seroprevalence data [8] . In the context of dengue , this approach has a specific interpretation . Dengue can result from infection with any one of four viral serotypes . Infection with one serotype provides long-term protection to that serotype , but not to others . Unlike for other common childhood immunizing infections , therefore dengue seropositive individuals could still be susceptible to secondary , heterotypic infections . However , ELISA tests cannot distinguish between dengue serotypes and current dengue diagnostics cannot reliably determine how many infections an individual has experienced . For the purposes of this analysis , we use the term “force of infection” specifically to mean the annual risk of infection with any serotype among dengue-naive ( seronegative ) individuals . This is equivalent to the rate of seroconversion , and we use the two terms interchangeably . The catalytic model predicts that the proportion of seronegative individuals declines with increasing age at a constant rate , λ , according to the relationship:where pa is the proportion seronegative by age a years . Assuming that the risk of infection is constant over time , the force of infection parameter , λ , defines the rate at which seroconversion ( first infection ) increases with age . The above model can be expressed within a generalized linear modelling framework , such that:where λ can be estimated by maximum likelihood regression methods . The model can be generalized to allow the force of infection to vary with age . We fitted two different models to our seroprevalence data , Model 1 assuming a constant force of infection , and Model 2 the force of infection to vary with age: ( Model 1 ) ( Model 2 ) In Model 2 , a1 and a2 are linear terms of age for two different age groups , 0 . 5 to <6 years and 6 to <12 years respectively; λ1 and λ2 are estimates of the force of infection in each age group . a and a1 are constrained to be ≥0 . 5 years , as seropositivity below this age is likely to represent presence of maternal antibody . We estimated the force of infection parameters , with corresponding 95% confidence intervals ( 95% CIs ) using logistic models with a log link . Although our survey sample included children from the same household , accounting for household level clustering made a negligible difference to our estimates . We therefore ignored the clustering , and used the likelihood ratio ( LR ) test to determine the level of statistical support for the model with age-varying force of infection over the model assuming a constant force of infection . We investigated the sensitivity of Model 2 to different age groupings by varying the age breakpoint by one year above and below the cut-off of 6 years . We used AIC to compare model fit , favouring the model with the lowest AIC value . We also investigated the effect of JE vaccination history , to determine whether there was evidence that JE seropositivity interfered with the flavivirus ELISA . We hypothesized two possible scenarios: 1 ) that a substantial fraction of flavivirus positive ELISA results were due to infection with JE virus rather than dengue virus; 2 ) that the results of the flavivirus ELISA were influenced by the presence of vaccine-derived antibodies to JE virus . In either scenario , we expected that the force of infection estimates would be different between JE vaccinees and non-vaccinees , which would be manifested in a statistical interaction between JE vaccination history and age . Conversely , if the flavivirus ELISA results are not influenced by JE seropositivity , no such interaction should be present , and the force of infection estimates should be similar in both JE vaccinees and non-vaccinees . We evaluated the association between JE vaccination and flavivirus seropositivity using the χ2 test , and compared the median age of JE-vaccinated and non-vaccinated children using the Kruskall-Wallis test . We then fit logistic models as above , but additionally including terms for the interaction between age and JE vaccination status , to determine whether force of infection estimates differed between JE vaccinees and non-vaccinees . We compared the age-specific force of infection parameters for vaccinees and non-vaccinees , and used the LR test to assess evidence for the interaction term . We also fitted models including JE vaccination as a covariate , to assess potential confounding of the force of infection parameter by JE vaccination status . We compared the force of infection parameters from models with and without the JE vaccination variable to assess evidence of confounding , and used the Wald test to assess evidence of an effect of JE vaccination history on serological status . The number of dengue infections far exceeds the number of clinical and notified cases , because many infections are asymptomatic and only a fraction of clinical dengue cases seek medical care and are notified to the authorities . We estimated , for the Colombo Municipal Council ( CMC ) , the ratio of infections to notified cases for the year 2009 . We obtained the expected size of the paediatric population , by single year of age , by applying an annual population growth factor of 0 . 7% to population estimates from the 2001 census ( Department of the Census , Sri Lanka , personal communication ) . To estimate the number of dengue primary infections , we applied our age-specific estimates of force of infection to the age distribution of the paediatric population in CMC , discounting from each single-year age group the number of seroconversions expected to have occurred in the previous year . We divided the age-specific number of primary infections by the number of DF/DHF cases notified to the Ministry of Health in 2009 to obtain the ratio of infections to notified cases . Because of difficulties in differentiating between infection and maternal antibody , we excluded infants <6 months old from the analysis .
Force of infection estimates from Models 1 and 2 are shown in Table 2 . Assuming a constant force of infection ( Model 1 ) , dengue-naïve children seroconvert at a rate of 14 . 1% per year ( 95% CI: 12 . 7%–15 . 6% ) . Under Model 2 , the annual rate of seroconversion was 15 . 4% ( 95% CI: 13 . 2%–17 . 7% ) among children aged 6 months to <6 years , and 8 . 7% ( 95% CI: 2%–15 . 4% ) among children aged 6 to <12 years . The LR test showed only weak evidence to favour Model 2 ( p = 0 . 111 ) . Similarly , a z-test comparing the λ1 and λ2 parameters from Model 2 showed only weak evidence that these were significantly different ( p = 0 . 109 ) . The model predictions are plotted together with the observed age-specific seroprevalences ( and 95% CIs ) in Figure 1 . Despite weak evidence in to support Model 2 , the predictions from this model appear to provide a slightly better fit to the observed seroprevalence values , particularly for children above 6 years . For Model 2 , varying the age breakpoint to 5 and 7 years changed the λ1 estimate between 15 . 5% and 15 . 1% , but the value of λ2 was quite sensitive to changes in the age breakpoint , ranging from 10 . 4% to 6 . 7% . Overall , 514 ( 64 . 5% ) children had received JE vaccination . JE vaccination status was missing for 23 ( 2 . 9% ) children . Among children aged 1 year , 35% of children had received JE vaccine , while above the age of 3 years , between 73% and 81% of children were vaccinated . Children vaccinated against JE were more likely to be seropositive ( 56 . 8% vs 41 . 9% , OR = 1 . 83 , 95% CI: 1 . 34–2 . 50 ) . However , this association was markedly reduced after adjusting for differences in the age distribution of vaccinees and non-vaccinees ( age-adjusted OR = 1 . 22 , 95% CI: 0 . 87–1 . 73 ) . There was no evidence that JE vaccination history had any influence on dengue force of infection estimates ( Figure 2 ) . The force of infection parameters for JE vaccinees and non-vaccinees were similar for Model 1 assuming a constant force of infection . For Model 2 , the force of infection was somewhat lower among JE vaccinees aged 6 to <12 years , but there was little statistical support for the interaction parameter ( LR test p-value = 0 . 861 ) . Adjusting for JE vaccination status had little influence on the force of infection estimates . We estimated that approximately 27 , 000 primary dengue infections occurred in children aged six months to 11 years in CMC in 2009 . The estimates were similar for Model 1 ( 27 , 913 ) and Model 2 ( 26 , 185 ) , although the latter estimated a higher number of infections in children <6 years and fewer infections among those aged 6 years and above compared with Model 1 . In the same year , 878 cases of DF/DHF were notified to the Ministry of Health . Thus , for every case of DF/DHF notified , an additional 30 primary dengue infections occur in the community . There was evidence that this ratio was age dependent , being much higher in the first two years of life than at older ages ( Figure 3 ) . Based on Model 1 , we estimated an overall primary infection rate in children <12 years of 68 per 1000 children per year ( 95% CI: 65–70 ) , as compared with a rate of 2 . 1 notified cases per 100 , 000 children per year . Primary infection rates based on Model 2 were similar ( 64 per 1000 children per year )
To our knowledge , this is the first study from the Asian subcontinent to present data on dengue seroconversion rates among children . Our study population also includes pre-school age children , which have not always been included in similar studies in other settings . Our findings indicate that transmission of dengue among the paediatric population of Colombo is high; among dengue naïve children , approximately 14% experience their first dengue infection ( seroconvert ) within 12 months . There was some suggestion in our data that this risk could be age-dependent; from our Model 2 estimates , the rate of seroconversion among children aged 6 to <12 years was lower at 8% . Although there was only weak statistical support for this age dependence , our sample size was modest and our study may have lacked power to detect age-dependent effects . Nevertheless , Model 2 predictions did provide a slightly better visual fit to the data at older ages , while the marked change in the force of infection parameters with age suggest that a lower rate of seroconversion among those aged 6 years and above could be plausible . If true , this would indicate a lower infection pressure at older ages . A possible reason for this could be changes in behaviour with age; school-age children spend more time outside the home , which may reduce exposure to bites by the primarily domestic , indoor-biting Aedes aegypti mosquito , the main dengue vector in Sri Lanka . Studies in other countries have indicated that dengue transmission clusters primarily around the home [9] , [10] , although house-to-house movements appear to be important for maintaining transmission in an urban environment [11] . Despite this , the two models provide similar estimates of the expected number of primary infections by age 12 . A reduction in the rate of seroconversion above 6 years of age has a small impact , because most children experience their first infection at a younger age; the median age at first infection in this population was less than 5 years . Our findings are comparable to those of other studies in Southeast Asia and Brazil . The median age at first infection is less than 5 years in Colombo . A recent seroprevalence survey in Recife , Brazil , indicated that over 50% of children in areas of intermediate and high deprivation are seropositive before the age of 10 years [12] , while a recent study in Vietnam found that seroprevalence was approximately 50% by the time children enter primary school [13] . Seroprevalences of 23% , 9% and 17% at 9 , 12 and 18 months of age were reported in a recent study in Bangkok , similar , though somewhat lower , than those found in our study [14] . Our force of infection estimates are also similar to those in other studies . Using similar methods with seroprevalence data , Braga et al . estimated the force of infection across all ages in a deprived area of Recife at 17% , with lower forces of infection in wealthier areas of the city [12] . Other studies have used longitudinal approaches to measure seroconversion rates empirically . Using repeat serological surveys two years apart , Teixeira et al . recently reported a seroconversion rate equivalent to 17% per year among children aged <3 years in Salvador , Brazil . In Vietnam , cohort studies have reported one-year seroconversion rates of 11% among children aged 2 to 15 years in the Mekong Delta [15] , and 17% among primary school children aged 7 years and above in the southeastern coastal area . [13] . Among elementary schoolchildren in Kamphaeng Phet , Thailand , dengue infection incidence varied between 7 . 9% and 2 . 1% per year between 1998 and 2000 , although this latter study did not report results separately for dengue-naïve and previously infected children [16] . We estimate that there are approximately 30 primary dengue infections in the community for every DF/DHF case notified to national surveillance . This ratio was much higher among children less than two years of age than in older age groups . This is partly because older children are more likely to experience secondary infections , which we were unable to estimate from our data . Our figures thus underestimate of the true ratio of infections to notifications at older ages However , the ratio was four times higher among children <2 years compared with those aged 6 years and above , which suggests a real differences even accounting for additional secondary infections at older ages . Such a difference is likely to result from a higher ratio of asymptomatic to clinical dengue among very young children; older age has been found to carry a greater risk of symptomatic dengue given infection , both in Latin America [17] and Southeast Asia [18] . The high burden of subclinical dengue , particularly at younger ages , represents a large pool of undetected infection . The importance of this pool of asymptomatic infection for transmission of dengue viruses remains to be determined . Our study is subject to a number of limitations . Firstly , our serology results were from a flavivirus rather than dengue-specific ELISA . This could result in inaccurate force of infection estimates if antibodies to flaviviruses other than dengue were being detected . The most important of these is JE virus , which is endemic in Sri Lanka . If substantial levels of seropositivity against JE virus were being detected , we would expect that the force of infection estimates would differ between those with and without evidence of JE antibodies . We investigated this using JE vaccination history as a proxy for JE virus seropositivity and we found no evidence for such an effect , indicating that primarily dengue infections were being detected by our ELISA . In addition , high levels of JE virus circulation in our study are unlikely , as this virus is largely confined to rural areas of the country . An analogous finding was reported from a study in the Amazonian basin , in which dengue virus seropositivity was not affected by prior yellow fever vaccination [19] . In addition , we assumed that maternal antibody wanes by 6 months of age . The large drop in seroprevalence between 0 and 6 months suggests that this is a reasonable assumption . However , studies have shown persistence of maternal antibody in a minority of children beyond 12 months [14] , [20] , [21] . It is therefore possible that our models overestimate seroprevalence at these ages , which would have resulted in an underestimate of the rates of seroconversion . Unlike in other childhood immunizing infections , a first infection with dengue does not provide long-term protection from re-infection , as individuals are still susceptible to infection with other dengue serotypes . Diagnosis of past dengue infection is complicated by the fact that , beyond the first infection , no method can reliably determine the serotypes to which an individual has been exposed . We were thus unable to estimate serotype-specific forces of infection; our estimates are better interpreted as the average force of first infection with any serotype . For this reason , we are also unable to comment on the risk of infection among seroconverters , which would require longitudinal approaches and/or diagnostics that are better able to differentiate between primary and secondary infections . Ferguson et al . previously estimated strain-specific forces of infection in Thai children under 10 years by applying a mathematical model to data from cross-sectional serological samples tested by plaque reduction neutralization tests , which can differentiate between strain-specific monotypic infections and multitypic infections [22] . Their serotype-specific force of infection estimates ranged from 0 . 01 to 0 . 1 , and were highest for the DEN-2 serotype . Strain-specific forces of infection will depend on the relative frequency of circulation of each serotype , as well as any interactions between strains . Our model assumes that the force of infection is constant over time . This may not be true in highly epidemic years , or if changes to the force of infection occur because of shifts in the predominant circulating strains . Such effects would likely result in cohort effects in the data , such as spikes in the prevalence of seropositivity in children of a specific age . Sri Lanka experienced a number of highly epidemic years prior to the study , most notably in 2004 , but there is little evidence in our data of such cohort effects , suggesting that any temporal changes in the force of infection are minimal . In estimating the number of dengue infections , we have extrapolated results from MC Ward 33 to the entire CMC region . It is possible that the force of infection in other areas of Colombo differs from that in our study area , because of differences in demographic or socioeconomic characteristics , or population and/or vector densities , although we have little reason to believe that our results would not be applicable to other areas of CMC . Our study has important implications , both for understanding dengue transmission and developing control strategies . Our findings confirm that there is a high infection pressure at young ages , as more than half of children experience their first infection before the age of 5 years . This age group is also at highest risk of mortality . The high level of transmission at very young ages , suggests that control strategies and future vaccination schedules that focus these age groups are likely to provide the most benefit . It also suggests that transmission in Colombo primarily occurs in and around the home , as more than half of children seroconvert before they reach school age . Finally , a number of studies have indicated that transitions in dengue epidemiology , characterized by shifts in the average age of cases , are related to the force of infection [23] [24] . Brazil , where historically adults contribute the majority of cases , is currently seeing a trend towards infection and more severe disease at younger ages [24] , [25] . Conversely , secular decreases in birth rates in Thailand are thought to be linked with declines in the force of infection and a resulting increase in the average age of dengue infection [23] . In Sri Lanka , high forces of infection , the predominance of cases in childhood , and a recent trend towards a greater contribution to the caseload among adults all indicate that the epidemic is at an equivalent stage to the well-established epidemics in Southeast Asia . | Dengue is an increasing problem in the Asian subcontinent , but little research exists on dengue burden and transmission in this region . Dengue ranges from mild fever to pronounced circulatory shock and potentially death . However , clinical disease gives an incomplete picture of how much dengue is circulating , because many infections are asymptomatic . Presence of antibodies to dengue virus provides evidence of past infection . By studying how antibody prevalence changes with age , the force of infection can be estimated , a key measure of population transmission that quantifies the risk of a first infection among dengue-naive ( seronegative ) individuals . We estimated the force of dengue primary infection by applying a catalytic model to data from a serological study of children in Colombo , Sri Lanka . Over 70% of children experienced at least one infection by the age of 12 years , and the median age at infection was 4 . 7 years . Among dengue-naive children 14% can be expected to experience a dengue infection within 12 months . The high force of infection at young ages indicates a very high level of dengue virus transmission in this urban setting that is comparable with levels seen in other regions with well-established epidemics , including Southeast Asia and Latin America . | [
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"... | 2013 | Estimates of Dengue Force of Infection in Children in Colombo, Sri Lanka |
Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions . This transmission of information is subjected to molecular noise what can decrease its fidelity . Here , we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies . The result is due to the contribution of noise to the generation of autonomous responses by each copy , which are altogether associated with a common decision . Moreover , factors that correlate the responses of the redundant units ( extrinsic noise or regulatory cross-talk ) contribute to reduce fidelity , while those that further uncouple them ( heterogeneity within the copies ) can lead to stronger information gain . Overall , our study emphasizes how the interplay of signal thresholding , redundancy , and noise influences the accuracy of cellular decision making . Understanding this interplay provides a basis to explain collective cell signaling mechanisms , and to engineer robust decisions with noisy genetic circuits .
The biochemistry of cells determines the operation of biological circuits . This biochemistry is inevitably noisy [1–3] what immediately suggests a limitation to the reliable function of these circuits , and thus many early studies examined how the problem of achieving correct operation could nevertheless be solved . Mechanisms such as kinetic proofreading [4] , or integral feedback control [5] emerged then as some fundamental solutions . One might ask , on the other hand , to what extent noise could indirectly represent an advantage . An example is found when cell populations , in which noise leads to phenotypic variability , display heterogeneity in stress responses that represent a crucial element for survival [6] . In a more direct situation , noise can turn into a necessary ingredient to facilitate new classes of behaviors not achievable otherwise [7–11] . These valuable behaviors are typically related to cellular decisions , which essentially involve changes in the expression phenotype . Specific biological circuits were therefore shown to employ noise to induce the expression of transient phenotypes [8] , or to switch among distinct stable states [9] . That many of these probabilistic dynamics relate to systems whose actions are susceptible to limiting signal values [12] emphasizes the connection between noise , cellular decisions , and threshold response circuits . The beneficial aspect of noise also forces us to revisit some of the original arguments on the relationship between stochasticity and the structure of biological systems [13 , 14] . In particular , the existence of genetic redundancies was frequently interpreted as a mean to enhance reliability of operation ( i . e . , noise as a disruptive element ) . This role appeared in consequence as a plausible rationale for the evolutionary maintenance of several copies of a gene or circuit [15] . Instead , we focus here on redundancy as a genetic architecture that , when coupled to the effect of noise in threshold response circuits , enables unique information-processing functions . We examined this issue within the precise framework of information theory . Biological circuits are in this way interpreted as communication channels , in which an input signal ( x ) originates –as a result of a cellular decision– an expression output ( y ) , with a given probability ( Fig 1A ) . The uncertainty on the input signal is then reduced by the decision process , whose set of outcomes tells us about the input distribution [16 , 17] . An association that is properly quantified by the mutual information ( MI ) , an information-theoretic measure describing the dependence between the input signal and the output phenotype ( no matter how they could correlate [18] , Fig 1A ) . Notably , this framework was recently exploited to quantify the functionality of transcriptional regulatory elements [19–21] , the accuracy of cell location during developmental processes [22] , and the maximal information transmission capacity of noisy signaling pathways [23 , 24] . The relevance of redundancies was already manifested in some of these results . Here , we first illustrate how the stochasticity of biochemical reactions ( intrinsic noise ) can help to gain information . We then show that information transfer can be amplified , if the combined response of multiple genetic units is considered . The reported amplification is shown to rely on the presence of different factors that contribute to generate variability in the individual response of each unit , like intrinsic noise or genetic heterogeneity ( i . e . , differences in the biochemical properties ) . This variability helps to enlarge the capacity of the global output to represent the input distribution . In contrast , we also discuss how factors reducing variability in the responses , like a noise source common to all units ( extrinsic noise ) or regulatory cross-talk , eventually mitigate the gain .
We first analyzed a minimal regulatory circuit implemented by a gene ( whose expression we denote as y ) autoactivating transcriptionally its own production [25] ( Materials and Methods ) . This is a genetic implementation of a threshold device that , by acting deterministically , becomes activated only if the input signal x crosses a particular limit ( Fig 1B ) . When the signal is stochastic , the response depends of course on the relationship between this threshold and the mean ( and variance ) of the underlying distribution P ( x ) ( considered for simplicity as a uniform distribution; Fig 1B ) . A symmetric distribution centered on the threshold would thus originate equally likely the two output values ( OFF/ON ) ( i . e . , one bit of information ) ; while the same distribution centered above/below the threshold would produce biased responses ( i . e . , less than one bit of information ) . However , the previous behavior can be affected by the extensive noise sources acting on biological circuits [1–3] . One could ask then to what extent the circuit is reliably representing the signal . With this goal , we computed the response ( black dots in subpanels of Fig 1C ) to a number of signals drawn from a fixed distribution ( red distribution in Fig 1B ) and strength of intrinsic noise . To quantify how much information the response conveys about the input , we made use of MI [16–18] ( Fig 1A ) ( Materials and Methods ) . Values of MI change with noise [main plot in Fig 1C; the mean of P ( x ) is above the threshold] . For weak noise levels , the circuit works essentially as a deterministic switch , it is always y = ON as x > threshold . For strong noise levels , the device cannot distinguish signal fluctuations , then its behavior is essentially random . In both cases , the gene is processing a limited amount of information ( subpanels of Fig 1C , red curves denote the averaged stimulus-response profiles ) . But the transmission of information presents a maximum for an intermediate noise level . In this regime , the circuit can express its two possible states due to noise ( i . e . , low values of x can cross the threshold ) [26] , what precisely contributes to a better representation of the input signal ( see also S1 Fig ) ; a characteristic behavior of noisy nonlinear systems known as stochastic resonance ( SR ) [27 , 28] . Moreover , SR disappears when the mean of P ( x ) is close to the threshold , as stochasticity is now not required to reach the two possible states . Noise always reduces information transfer ( Fig 1D , curves for N = 1 , N denoting the number of circuits involved ) . Note here how MI does exhibit an upper limit of one bit when the mean of P ( x ) exactly matches the threshold , and the circuit is noiseless . MI decreases with noise because signal values above/below the threshold originate in some cases stochastic crossings ( e . g . , y = ON when x < threshold ) , and the information content in absence of noise is already high ( note in contrast that , in the scenario of SR , MI was very low in absence of noise ) . Additionally , Fig 1D displays a situation in which a maximum in MI is nevertheless observed ( curves for N = 2 ) . This is obtained by increasing the number of devices processing the same input , with y representing the sum of all individual outputs; a phenomenon called suprathreshold SR [29] ( see S1 Text , and S2 Fig , for a brief account of suprathreshold SR ) . Note how the maximum is observed in the noise regime where the information processing of individual units ( N = 1 ) qualitatively declines ( from here the presence of several units begins to lose effectiveness ) . What is apparent is that redundancy boosts information transfer , given a fixed noise level . The addition of extra copies of the threshold device , i . e . , genetic redundancy , appears then as a potential mechanism to increase the transmission of information in the presence of intrinsic noise . Consider , for instance , a situation in which two devices read in parallel the same input signal , assuming again two possible values of gene expression for each unit . The overall output alphabet [30] consists of three letters: {0 ( both copies OFF ) , 1 ( one OFF the other ON ) , 2 ( both ON ) } . The new alphabet is linked , of course , to the action of independent ( intrinsic ) noise sources acting on the two genes , which allows each device to produce an autonomous response ( with noise-induced threshold crossings , S2 Fig ) . The sum of individual responses would give , accordingly , a global output distribution P ( y ) constituted by three peaks . The extended alphabet helps therefore to enlarge the capacity of the output to represent the input variability; in other words , it contributes to linearize the averaged stimulus-response profile ( Fig 2A , see also S3 Fig ) . Both the number of units and the type of nonlinearity influence the increment of information transfer . In Fig 2B , we introduced three different threshold devices [25] to show how MI increases with redundancy . For each type , MI relative to the case of no redundancy ( i . e . , a single unit ) was plotted . Specifically , we examined a simple regulated unit , a bistable expression system implemented through a positive feedback , and an excitable device constituted by interlinked positive and negative feedbacks ( implemented as the one linked to transient differentiation in Bacillus subtilis [8] ) ( Materials and Methods ) . The output of all these devices is given by a continuous variable representing gene expression ( note that the response of the bistable unit was regarded as OFF/ON in the previous section , Materials and Methods ) . This allowed identifying discrepancies in terms of MI among different gene regulatory circuits . In particular , the largest amplification of information content corresponds to those devices whose actions ultimately rely on discontinuous transitions ( i . e . , the bistable and excitable systems ) . Out of these two systems , the excitable one presents comparatively larger amplification , although only observed for relatively large arrays . In this system , the response is entirely binary even in presence of noise: either the signal triggers a response or not ( S4 Fig ) . Moreover , the gain in information transfer is much lower for the simple regulated system in which the response profile is continuous ( what entails that one unit already has the capacity to reach a relatively large output alphabet ) . The contribution of redundancy is therefore always much higher in analog-to-digital than in analog-to-analog signaling circuits , and provided they are noisy . We next studied how the specific distribution of the signal impinging on the genetic circuits ( that can encode distinct environmental or genetic conditions [31] ) can further modulate the enhancement of information transfer . Knowing this distribution is typically difficult in cellular systems [24] . We examined then to what extent information transfer would be influenced by the shape of P ( x ) . We considered three different signals acting on the array of threshold devices ( Materials and Methods ) . MI increases more strongly with a normally distributed signal ( Fig 3A ) . For this distribution , the mass of x values is closer to the threshold what allows noise to alter more frequently the expected deterministic response of the device . We then analyzed the effect of the relationship between the threshold and the signal mean . When the mean of P ( x ) is equal to the threshold , a higher increase of MI with genetic redundancy is observed ( Fig 3B ) . Arguably , if the mass of x values is equally distributed above/below the threshold , there exists again more chances for noise-induced threshold crossings . Fine-tuning of the parameters characterizing P ( x ) contributes thus to a better representation of the input signal by the global output response . The most important constraint for the gain in information associated to the previous redundant systems is the independence between the noise sources . When these are correlated , P ( y ) becomes more sharply peaked around a small subset of possible responses ( i . e . , the output alphabet is more limited; Fig 4A ) . This applies to biological circuits that , in addition to intrinsic noise , also integrate the effect of extrinsic fluctuations [2 , 32] . Extrinsic noise affects all genetic devices in the same manner what eventually correlates individual outputs . For instance , Fig 4B shows how ( relative ) MI decreases with the strength of extrinsic noise in an array of five bistable units ( Materials and Methods ) . Note however that this redundant architecture still exhibits , for different extrinsic noise levels , a larger MI with respect to the nonredundant case ( inset of Fig 4B ) . Despite the independence of the noise sources , cross-talks between devices can similarly lead to correlations in the individual responses . In a genetic context , one could imagine two independent transcription factors sharing recognition domains [33] . One could also conceive a second unit recently emerged by duplication , and that no process of neofunctionalization yet occurred [34] . Fig 4C indeed shows a decay in ( relative ) MI for a system of two bistable units when cross-talk between them increases ( simulations done without accounting for extrinsic noise , Materials and Methods , see also S5 Fig for a study of asymmetric cross-talk ) . In this case , the activation of one unit drags the activation of the other , biasing again the output alphabet ( inset of Fig 4C ) . Of note , the decay profile in MI is qualitatively different in the two scenarios . Addition of extrinsic noise contributes to limit information transfer in a progressive manner since it increasingly coordinates responses . In the second situation , outputs are correlated once a certain cross-talk range is reached what is reflected in a sharper decay ( similar patterns are expected to be observed when considering other constituent units ) . We proceeded by examining a complementary source of individuality in information processing , which is linked to the heterogeneity within the collection of threshold devices . In the context of genetic circuits , variability in promoter strengths , ribosome-binding sites , proteins half-lives , or protein-DNA binding affinities are all factors that in effect modify threshold values or output responses . Adjusting for each device the values of the biochemical parameters of the model can capture this variation [35] . We specifically explored the implication of threshold heterogeneity in the array of five bistable units in which the threshold values are drawn from a Gaussian distribution ( and heterogeneity equates to the associated standard deviation , Materials and Methods ) . Notably , we observed again a resonance in information transfer , but this time as a function of the degree of heterogeneity ( Fig 5 ) . While moderate levels of heterogeneity allows regulatory circuits to encode complementary aspects of the input signal , hence enhancing information transfer , greater variation becomes detrimental by originating noise-induced threshold crossings over the whole input range . Moreover , since both intrinsic noise and heterogeneity contribute to increase the transmission of information ( certainly by enlarging the alphabet of the global output ) , we also explored to what extent these two sources of individuality act independently [36] . We found that intrinsic noise mitigates the increase in MI due to threshold variability ( inset of Fig 5 ) . Intuitively , higher noise levels make indistinguishable those variations associated to the control of gene expression in the threshold unit . Several analyses support the gain in information transfer established by our theoretical framework . For instance , by explicitly quantifying information transduction , new experimental reports showed how multiple copies of a biochemical system accessing the same signal leads to an increase in MI [23 , 24] . This was coupled to multiple gene copies ( comparing 1× and 2× diploids [24] ) or to a general redundant architecture ( with a bush network topology ) that could be implemented in an intra- or intercellular manner [23] . Moreover , recent work examined how the output of a LuxR-inducible promoter in Escherichia coli would change with copy number ranging from 1 copy per cell ( genome copy ) to hundreds ( via multicopy plasmids ) [37] . We used the available data to confirm the relative MI gain as a function of genetic redundancy ( Fig 6A , Materials and Methods ) . Because the system does not rely on a discontinuous transition ( e . g . , it is not bistable ) , the gain saturates with few copies ( see the curve labelled as “1” in Fig 2B ) . We contrasted this analysis with the response to progesterone of a single bistable unit governing the oocyte maturation in Xenopus [38] . The noise-driven differential concentration from oocyte to oocyte ( for a particular progesterone level ) of the controlling protein makes unreliable the maturation process ( i . e . , subthreshold levels of progesterone can induce maturation in some oocytes ) what limits the amount of information transmission one could achieve in a noiseless situation ( Fig 6B ) . Arguably , the presence of additional copies of the precursor gene would help to reduce such variability and then mitigate the loss of information ( see Fig 1D ) . Finally , Fig 6C illustrates other biological contexts in which the signaling architectures , by exhibiting redundancies , could be more effective in terms of information transfer following our predictions [39–41] .
Binary decisions implemented by means of threshold devices appear in many engineering and physical systems , and have been extensively studied in relation to the detection and transmission of signals . While noise was commonly considered harmful in many of these settings , some work alternatively identified circumstances in which its presence improves performance [27 , 28 , 42] . In Biology , both the stochastic nature of biochemical reactions and the typical occurrence of thresholds –linked , for instance , to cell fate determination– also anticipates the possibility of beneficial effects . This specifically applies to the case of gene regulatory circuits , in which molecular stochasticity acts in many cases as a core determinant of function [43] . In this work we discussed in detail the benefits of intrinsic molecular noise when multiple threshold regulatory circuits process a common signal . This system exhibits a resonance phenomenon known as suprathreshold SR [29] ( S1 Text ) . The effect establishes the value of the noise-induced uncoupling of the action of each unit . This advantage is manifested as well in a more linear relation between stimulus and response , a type of dose-response alignment that could be important in how precise extracellular conditions determine cell responses , and that was previously associated to negative feedbacks [21] . Our functional analysis therefore reveals redundancies not only as a genetic architecture contributing to robustness [44 , 45] , or to the adaptation to novel environments through the increase of gene expression levels [46 , 47] , but also as a mechanism increasing the capacity to transmit reliable information ( Fig 7 ) . We suggest that this aspect could contribute to the evolutionary maintenance of genetic redundancy ( tradeoffs with the associated genetic load of redundancy could matter , see S6 Fig ) . That multiple signaling pathways in Saccharomyces cerevisiae overlap supports this hypothesis [48] . The balance of intrinsic/extrinsic noise also plays an important part to condition the amount of information transferred ( Fig 7 ) . Cells implementing regulatory circuits with few representative molecules or living in rich environments would shift this balance towards intrinsic noise [49] . Beyond this genetic/environmental tuning , cellular systems could avoid the loss of information , due to extrinsic noise , when the signal operates dynamically rather than statically [50] . Note that here we considered a static operation . Our results further emphasize how heterogeneity and cross-talk among redundant copies play opposite roles in the maintenance of information content ( Fig 7 ) . One could thus interpret the action of several parallel signaling pathways , each conveying approximately one bit of information , as heterogeneous copies of an effective threshold device what enhances information transmission , e . g . , this was observed in pathways for the growth factor-mediated gene expression [51] . That a global response –the sum of individual responses , in this case– implemented by parallel processing units could lead to better performance than that of the individual components was proposed in early models of computing , and can indeed be observed at different levels of biological organization: from genes ( this work ) , to living cells [23] , to social organisms [52] . In addition , ideas on redundancy and heterogeneity when mounting unreliable components were already present in the initial development of fault-tolerant computation and communication [53 , 54] , and also permeate to many biological scenarios . Our work substantiates the implications of these notions in cellular decision making by natural [48] and synthetic [55] molecular circuits , and contributes to exemplify how the application of concepts from information theory could lead to a more precise and quantitative understanding of cellular systems .
As a general regulatory model we examined a redundant system consisting of N different units , each of them activated by the input signal ( x ) . These units correspond to three types , defined by three specific sets of ordinary differential equations that were extended to account for stochastic effects using the Langevin approach [56] . We introduced an additional stochastic process ξex , to account for extrinsic noise common to all units . The correlation time of extrinsic noise is of the order of the cell cycle ( the mean is also 0 ) . For simplicity , we assumed systems implemented with short-lived proteins , so that ξex is constant within the time window required for the dynamical unit to reach steady state after reading the signal x ( this feature also reduces potential expression dependences on growth rates , e . g . , [59] ) . To examine cross-talk we applied a perturbative approach , with a perturbative parameter ε quantifying the degree of cross-talk . Finally , to study heterogeneity we specifically considered variability in the threshold values of the different units . We modified these values by introducing a Gaussian random number ω ( of mean 1 ) , with its standard deviation corresponding to the degree of heterogeneity . Note that only intrinsic noise was considered when accounting for cross-talk or heterogeneity . See full details of these methods in the S1 Text . We considered that the threshold regulatory system is initially in a steady state ( x = 0 ) before becoming activated ( x ≠ 0 at time t = 0 ) . The signal represents a continuous stimulus with fixed amplitude ( x is a step function at t = 0 ) , for the simple and bistable units , or a pulse ( for one unit of normalized time ) for the excitable one . The amplitude of the signal is given by x = 〈x〉10u , where u corresponds to a random number uniformly distributed in [−1 , +1] , unless otherwise specified . Signal stochasticity illustrates fluctuations due to upstream processes , environmental changes or molecular noise . We considered logx as input variable to compute information transfer . Each threshold unit is able to sense the signal what could alter its expression level as Δyi = yi ( x ) − yi ( x = 0 ) . The output was calculated at steady state , and signal fluctuations occur at a frequency that allows the genetic circuit to respond against the current signal value . In addition , the total differential gene expression of a redundant system can be written as Δ y = ∑ i = 1 N Δ y i . Since the response of the excitable system is transient , we implemented a Boolean function operating on yi , setting 1 if the unit was excited or 0 if not . For all main figures , we always treated the threshold units as dynamical systems , i . e . , modeled by differential equations . However , in the first section of the paper ( Fig 1 ) , the gene expression level ( yi ) was treated as a Boolean variable ( OFF/ON ) after resolving the corresponding differential equation . Expression was treated as a continuous variable in the subsequent sections ( Figs 2–5 ) . We mainly included a uniform distribution P ( x ) covering two orders of magnitude throughout the manuscript ( as described above ) . However , in Figs 1 and 3B , we analyzed the effect of the mean 〈x〉 of the distribution , with values 0 . 001 ( equal to the threshold value ) , 0 . 005 and 0 . 01 . In Fig 2 , the mean was fixed to the threshold value , i . e . , 〈x〉 = 1 in the simple regulated unit , 〈x〉 = 0 . 001 in the bistable system , and 〈x〉 = 0 . 9 in the excitable system . In Figs 4 and 5 , concerning to the bistable system , 〈x〉 = 0 . 005 . Moreover , in Fig 3A , we studied the effects of a normal or a beta distribution in log scale , with the mean equal to the threshold value . We used mutual information ( I ) as a quantitative metric to describe how the global output response of a single cell is sensitive to different concentrations of the input signal [18] . This adds to the quantification by the averaged stimulus-response profile . To calculate I , we performed 104 realizations of the pair ( x , y ) and solved numerically the following integral I = - ∫ - ∞ + ∞ P Δ y ( s ) log 2 P Δ y ( s ) d s + ∫ - ∞ + ∞ P log x ( r ) × ∫ - ∞ + ∞ P Δ y | log x ( s ) log 2 P Δ y | log x ( s ) d s d r , ( 4 ) where we considered logx as input and Δy as output variables . By using the Fokker-Planck equation , we calculated the probability that a unit has a given gene expression level ( see more details in S1 Text ) . We considered the dose-response data of a synthetic system composed by a red fluorescent protein ( RFP ) controlled by the transcription factor LuxR , which is activated by N-acyl homoserine lactone ( AHL , the signal ) [37] . Indeed , this is a simple regulated unit , which was implemented with different gene copy numbers . Mutual information was calculated between the RFP expression at the population level and the concentration of AHL in log scale ( an estimation of the actual values ) . In addition , we considered the dose-response data of a natural system governing the oocyte maturation in Xenopus [38] . Here , the glycogen synthase kinase 3β ( GSK3β ) controls the meiotic entry of progesterone ( the signal ) in the oocytes . This system is bistable and is implemented by an effective positive feedback loop ( through two negative regulations ) . Mutual information was calculated between the phosphorylation state of GSK3β of individual oocytes ( considered as a Boolean variable ) and the concentration of progesterone in log scale . As a reference , we considered a deterministic scenario with a distribution of progesterone centered in the threshold of the system . | There is increasing evidence that the presence of molecular noise greatly influences function in biological systems . This could imply , for instance , that genetic circuits adopt particular architectures in order to reduce noise . On the other hand , noise can be beneficial . Here , we show that this could be the case for the functioning of analog to digital genetic devices , which are commonly found in cellular decision making situations . We use the framework of information theory to illustrate first how noise can enhance information transfer in these devices . In those regimes in which noise is detrimental , we discuss how genetic redundancies allow information to be maximized , and how this effect depends on the specifics of the devices , and the interdependence among them . These results provide overall an additional rationale for genetic redundancies in genomic systems . | [
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"re... | 2016 | Genetic Redundancies Enhance Information Transfer in Noisy Regulatory Circuits |
The multifunctional Mre11-Rad50-Nbs1 ( MRN ) protein complex recruits ATM/Tel1 checkpoint kinase and CtIP/Ctp1 homologous recombination ( HR ) repair factor to double-strand breaks ( DSBs ) . HR repair commences with the 5′-to-3′ resection of DNA ends , generating 3′ single-strand DNA ( ssDNA ) overhangs that bind Replication Protein A ( RPA ) complex , followed by Rad51 recombinase . In Saccharomyces cerevisiae , the Mre11-Rad50-Xrs2 ( MRX ) complex is critical for DSB resection , although the enigmatic ssDNA endonuclease activity of Mre11 and the DNA-end processing factor Sae2 ( CtIP/Ctp1 ortholog ) are largely unnecessary unless the resection activities of Exo1 and Sgs1-Dna2 are also eliminated . Mre11 nuclease activity and Ctp1/CtIP are essential for DSB repair in Schizosaccharomyces pombe and mammals . To investigate DNA end resection in Schizo . pombe , we adapted an assay that directly measures ssDNA formation at a defined DSB . We found that Mre11 and Ctp1 are essential for the efficient initiation of resection , consistent with their equally crucial roles in DSB repair . Exo1 is largely responsible for extended resection up to 3 . 1 kb from a DSB , with an activity dependent on Rqh1 ( Sgs1 ) DNA helicase having a minor role . Despite its critical function in DSB repair , Mre11 nuclease activity is not required for resection in fission yeast . However , Mre11 nuclease and Ctp1 are required to disassociate the MRN complex and the Ku70-Ku80 nonhomologous end-joining ( NHEJ ) complex from DSBs , which is required for efficient RPA localization . Eliminating Ku makes Mre11 nuclease activity dispensable for MRN disassociation and RPA localization , while improving repair of a one-ended DSB formed by replication fork collapse . From these data we propose that release of the MRN complex and Ku from DNA ends by Mre11 nuclease activity and Ctp1 is a critical step required to expose ssDNA for RPA localization and ensuing HR repair .
Genome integrity is constantly threatened by DNA damage resulting from internal and external insults . One of the most harmful forms of DNA damage is the double-strand break ( DSB ) . When left unrepaired , DSBs can cause a plethora of chromosomal aberrations that often result in cell death or mutations that can lead to cancer phenotypes [1] . There are two major DSB repair pathways: nonhomologous end-joining ( NHEJ ) and homologous recombination ( HR ) . NHEJ is an error-prone pathway that repairs DSBs by directly ligating DNA ends with little or no end processing . Key NHEJ proteins that are conserved from yeast to humans include the Ku70-Ku80 heterodimer , which binds DNA ends with high affinity , as well as XLF/Cernunnos and DNA ligase IV [2]–[5] . HR typically uses the intact sister chromatid as template for repair in mitotic cells , hence it is most critical during S and G2 phases of the cell cycle [6] , [7] . HR is also essential for recombination between homologous chromosomes in meiosis , which is required for proper chromosome segregation and generation of genetic diversity . A multitude of human disease syndromes have been traced to NHEJ and HR defects , including those characterized by neurological , immunological and developmental disorders as well as radiation sensitivity , premature aging diseases and cancer , pointing to the critical roles of NHEJ and HR in maintaining genome stability [8]–[13] . A major prerequisite for HR is 5′ to 3′ resection of the DNA flanking each side of the break [14] , [15] . The eukaryotic heterotrimeric Replication Protein A ( RPA ) binds the single-stranded DNA ( ssDNA ) . The recruitment of the ATR ( ataxia-telangiectasia mutated- and Rad3-related ) -ATRIP ( ATR-interacting protein ) protein kinase complex to RPA-coated ssDNA initiates checkpoint signaling [16] . RPA is exchanged for Rad51 , a protein involved in the search for homology to allow subsequent repair [15] . Research in multiple model organisms has shed light on the proteins that catalyze the initiating events of HR , with most of the groundbreaking studies performed in Saccharomyces cerevisiae . Current models propose that the Mre11-Rad50-Nbs1 ( MRN ) protein complex initially recognizes the break , whereupon it initiates HR repair and a checkpoint response through ATM checkpoint kinase . Several years ago , we described a protein in Schizosaccharomyces pombe known as Ctp1 ( or Nip1 ) that collaborates with the MRN complex in HR [17]–[19] . Ctp1 shares a conserved C-terminal domain with S . cerevisiae Sae2 and mammalian CtIP , the latter of which associates with the MRN complex and the tumor suppressor BRCA1 [20]–[23] . Biochemical studies of Sae2 ( also known as Com1 ) identified a nuclease activity [24] , and more recent studies in budding yeast implicated both Sae2 and the Mre11 complex in the initiation of resection required for HR [25]–[31] . Further resection , extending several kilo-base pairs or beyond , is performed by the exonuclease Exo1 , or the helicase Sgs1 ( Rqh1 in Schizo . pombe and BLM in mammals ) together with the exonuclease Dna2 . Recently , DNA end resection was reconstituted in vitro using the budding yeast Mre11 complex , Sgs1-Top3-Rmi1 , Dna2 and RPA or the Mre11 complex , Sae2 and Exo1 respectively [32]–[34] . So far , quantitative measurements of the formation of ssDNA by resection of DNA ends have only been performed in S . cerevisiae . Studies on the in vivo function of orthologous proteins in other organisms have mainly relied upon the indirect detection of ssDNA through the recruitment of RPA to sites of DNA damage . The observation that siRNA depletion of Exo1 , BLM and CtIP reduces RPA interactions with damaged DNA in human cells implies that the function of these proteins in resection is conserved from yeast to human [23] , [35] , [36] . Whilst budding yeast Sae2 can act with the Mre11 complex to initiate resection of a DSB [27]–[29] , and it is essential for processing meiotic DSBs created by Spo11 [37]–[40] , Sae2 is not generally required for DSB repair . Indeed , sae2Δ mutants have a normal growth rate and are only weakly radiosensitive [39] , [41] . Schizo . pombe ctp1Δ mutants are also unable to process meiotic DSBs created by the Spo11 ortholog Rec12 [42]–[44] , but in addition they are acutely sensitive to IR and other DNA break inducing agents [19] . This damage sensitivity is reflected in vertebrate cells with the use of CtIP siRNA knockdown [23] , [45] . DNA damage sensitivity is also observed in mutants for the conserved ssDNA endonuclease and 3′ to 5′ dsDNA exonuclease activities of Mre11 ( also known as Rad32 in fission yeast ) . The mre11-H134S mutation ( corresponding to the Pyrococcus furiosus mre11-H85S mutation [46] that ablates both nucleolytic activities ) causes severe radiosensitivity in fission yeast , whereas a mutation that specifically impairs exonuclease activity has little effect , indicating that the endonuclease activity is critical for DSB repair in Schizo . pombe [46] . Mutations equivalent to mre11-H134S in budding yeast are mildly IR sensitive but do not impair DSB resection [47] , [48] , whilst a corresponding mutation of mouse Mre11 causes early embryonic lethality and strong cellular radiosensitivity [49] . Thus , while Mre11 nuclease activity and Ctp1/CtIP are critical for mitotic DSB repair in fission yeast and mice , the underlying molecular defects are largely unknown . To uncover the critical functions of Mre11 nuclease activity and Ctp1 in Schizo . pombe and study the interplay between proteins involved in resection and the localization of RPA to ssDNA overhangs , we have adapted a qPCR-based assay that directly measures the formation of ssDNA at a site-specific DSB . In agreement with studies of Mre11 in budding yeast [27] , [28] , [50] , this assay reveals that Mre11 , but not its nuclease activity per se , is critical for resection in fission yeast . Ctp1 and Mre11 are both critical for resection in fission yeast , unlike the relationship between Sae2 and Mre11 in budding yeast [25]–[31] . In agreement with studies of Exo1 and Sgs1 in budding yeast [27] , [28] , Exo1 and Rqh1 comprise independent activities that can catalyze extended resection in fission yeast , although our studies show that Exo1 plays a more dominant role in fission yeast . Also consistent with studies in budding yeast [26] , chromatin immunoprecipitation studies indicate that Ctp1 and Mre11 nuclease activity promote the release Ku from DNA ends in fission yeast . The inability to release Ku stabilizes binding of MRN to the break . Intriguingly , despite normal levels of ssDNA , we show that RPA localization in the Mre11 nuclease mutant is reduced compared to wild type by chromatin immunoprecipitation at a site-specific DSB . The observed reduction in RPA localization appears to be a general defect of mre11-H134S cells , which is independent of the DSB inducing agent , as IR treatment of these mutant cells results in reduced phosphorylation of the checkpoint kinase Chk1 as compared to wild type . Deletion of Ku in mre11-H134S cells reduces MRN retention at the DSB , increases the localization of RPA , improves checkpoint signaling in response to IR , and suppresses sensitivity to DNA damage . From these data we propose that Mre11 nuclease activity and Ctp1 are required to release both the MRN and Ku complexes from DNA ends , which is critical for correct assembly of RPA on the resected DNA ends and the repair of DSBs .
Repair of DSBs by homologous recombination requires 5′-to-3′ resection of DNA ends at each side of the DSB . In budding yeast , DNA end resection assays have been established that quantitatively measure the formation of ssDNA at particular distances from a site-specific DSB . To extend quantitative DSB resection studies to another model organism , in this case Schizo . pombe , we adapted a qPCR-based resection assay that measures the formation of ssDNA directly [51] . In this assay , a site-specific DSB is created by the S . cerevisiae HO endonuclease , which is expressed from the thiamine-repressible nmt41 promoter in Schizo . pombe [52] , [53] . The HO recognition site is integrated at unique sequences at the arg3 locus , hence these DSBs are irreparable when both sister chromatids are cleaved at this site . The nmt41 promoter system allows essentially complete repression of the HO expression in the presence of thiamine , which for this assay was a critical attribute of nmt41 in comparison to other promoters used for regulating gene expression in fission yeast . A disadvantage of the nmt41 promoter is that full derepression requires ∼16 hrs growth in thiamine-free media , making it impractical to use cell cycle synchronization protocols . However , as Schizo . pombe has a very short G1 phase and a long G2 phase [54] , and HO-induced DSBs elicit a robust checkpoint response that arrests division in late G2 phase [52] , [53] , the large majority of cells ( ∼80% ) in wild type cultures are expected to suffer the HO-induced DSB in G2 phase and remain arrested in this phase of the cell cycle . DSBs do not delay progression through G1 or S phase in fission yeast [55] , thus the small fraction of cells that suffer a DSB outside of G2 phase will replicate the DSB once and then arrest in G2 . However , as discussed below , resection is required for robust checkpoint signaling . We use qPCR to measure the formation of the break through the corresponding disappearance of the DNA product formed by PCR across the DSB , and calculate the percentage of uncut DNA by comparing these values to the formation of a DNA product elsewhere in the genome . These data are used to correct for timing variations in break formation between strains and experiments . Data were generated from a minimum of 3 independent experiments . Resection initiation was measured 35 bp from the break , whereas extended resection was measured 3 . 1 kb from the DSB ( described in Materials and Methods , see Figure 1A , Figure S1A and S1B ) . As expected , formation of the break was followed by resection initiation measured at 35 bp , which preceded the appearance of the long-range resection product at 3 . 1 kb ( Figure 1B ) . By measuring the formation of ssDNA at several sites from 35 bp to 9 . 4 kb from the break , we estimated that the resection rate was fairly constant in this region , with an average rate of ∼4 kb/hr in wild type ( see Figure 1C ) . Remarkably similar rates ( ∼3 . 5–4 . 5 kb/hr ) were measured using a Southern hybridization-based assay in budding yeast [28] . In S . cerevisiae , the Mre11-Rad50-Xrs2Nbs1 ( MRX ) subunits are essential for radioresistance , whereas a role for Sae2 is revealed only at very high doses of IR , and even then the sae2Δ phenotype is mild compared to mrxΔ mutants [6] , [39] , [41] . These differences are reflected in resection assays: resection initiation at an HO-induced DSB is delayed but nearly 100% efficient in sae2Δ cells , as compared to ∼40% resection initiation deficiency in mre11Δ or rad50Δ mutants [27] , [28] . A requirement for Sae2 is only revealed when Exo1 and Sgs1-dependent resection activities are also eliminated . As Ctp1 and the MRN subunits are both essential for radioresistance in Schizo . pombe [19] , we tested whether Ctp1 is required for resection initiation and whether its activity is as important as Mre11 . We first examined mre11Δ cells , wherein we found a substantial resection defect , with the formation of ssDNA at 35 bp from the DSB being ∼45% of the level measured in wild type ( Figure 2A ) . The resection initiation defects of Schizo . pombe and S . cerevisiae mre11Δ mutants appear to be quite similar , which is consistent with their acute radiosensitive phenotypes . Schizo . pombe ctp1Δ cells displayed a strong resection initiation defect that was very similar to mre11Δ cells ( Figure 2A ) . They also displayed similar defects when resection was measured at 3 . 1 kb from the DSB ( Figure 2B ) . Thus , Ctp1 and Mre11 are equally critical for resection in fission yeast , which is consistent with their essential roles in IR survival . Activation of the checkpoint kinase Chk1 is partially impaired in mre11Δ and ctp1Δ cells exposed to IR [56] , [57] , which is consistent with a resection defect . However , the IR-induced G2 checkpoint delay is lengthened in these cells , which is likely caused by their inability to complete DSB repair and relieve the checkpoint signal [56] , [57] . These mutants also arrested division in response to the HO-induced DSB , although the HO induction kinetics prevented quantitative assessment of the duration of the checkpoint arrest . Therefore , as seen in budding yeast [25] , [28] , in our experiments more cells may have leaked through the checkpoint arrest in the resection-defective mutants . In budding yeast , replication increases resection at a DSB , but it is unknown which activities are responsible for this effect [51] . In S . cerevisiae , Exo1 and an Sgs1/Dna2-dependent activity catalyze extended resection [27] , [28] . The Sgs1-dependent activity is critical for very long-range resection ( ∼10 kb and longer ) , but either activity is sufficient for resection to ∼3 kb from a DSB . We examined the roles of Exo1 and Rqh1 ( Sgs1 ) in resection at 35 bp and 3 . 1 kb from the DSB in fission yeast . Elimination of Exo1 or Rqh1 , either alone or in combination , did not decrease ssDNA formation at 35 bp by a statistically significant amount ( Figure 2C ) . In comparison , a small but statistically significant defect in resection initiation was observed in S . cerevisiae exo1Δ sgs1Δ cells [28] . When we measured resection at 3 . 1 kb from the DSB , we found that elimination of Exo1 caused a very strong defect , reducing resection efficiency to about 30% compared to wild type , whereas there was no measurable resection deficiency in rqh1Δ cells ( Figure 2D ) . Analysis of the exo1Δ rqh1Δ strain revealed that essentially all of the remaining long-range resection activity in exo1Δ cells was attributable to Rqh1 ( Figure 2D ) . This result suggests that Rqh1 can contribute to long-range resection in fission yeast , but its activity is relatively inefficient . Thus , while Exo1 is not required for efficient resection initiation , it is responsible for the majority of the long-range resection in fission yeast . This situation contrasts somewhat with budding yeast , in which either Exo1 or the Sgs1-dependent activity is sufficient for extended resection . Intriguingly , deleting Ku subunits substantially suppress mrnΔ and ctp1Δ phenotypes in Schizo . pombe [19] , [46] , [58] . While Exo1 is not required for survival of IR or CPT exposure , it is crucial for resistance to DSB inducing agents in a ctp1Δ pku80Δ background [19] . One possible mechanism to explain this genetic result is that eliminating Ku allows Exo1 to substitute for Ctp1 to initiate resection . To test whether Ku inhibits Exo1-dependent initiation of resection , we performed resection assays in pku80Δ backgrounds . Unexpectedly , we detected a small but statistically significant decrease in the formation of ssDNA at 35 bp in the pku80Δ strain ( Figure 2E ) . The physiological significance of this defect is unclear , as pku80Δ mutants appear to be fully proficient at HR repair of DSBs [5] . Although deletion of Ctp1 or Mre11 caused a large decrease in close-in resection in a pku80+ background ( Figure 2A ) , deleting these genes did not have statistically significant effects in the pku80Δ background ( Figure 2E ) . These results suggested that another activity could substitute for Ctp1 and Mre11 in a pku80Δ background . In support of this model , deleting Ku significantly increased resection at 35 bp in the ctp1Δ and mre11Δ backgrounds ( Figure 2E , p-values≤0 . 05 ) . Importantly , this effect entirely depended on Exo1 , i . e . , deleting Ku did not cause a statistically significant increase in resection in the ctp1Δ exo1Δ background ( Figure 2F ) . From these results we conclude that Ku inhibits Exo1 from initiating resection in ctp1Δ cells . Analogous results have been reported in budding yeast , where it was found that deleting Ku suppressed the resection delay in sae2Δ cells and improved resection in an Exo1-dependent manner in a rad50Δ mutant [6] , [26] . These data further indicated that there is at least one additional resection activity in ctp1Δ exo1Δ double mutant cells , with the most likely candidate being an Rqh1-dependent activity . We attempted to answer this question by creating a ctp1Δ exo1Δ rqh1Δ strain , but tetrad analysis revealed that the triple mutant is not viable ( Figure S2 ) . The same lethality occurs when exo1Δ rqh1Δ is combined with either mre11Δ or the mre11-H134S nuclease defective allele ( Figure S2 ) . This genetic interaction was also observed in S . cerevisiae , although in some strain backgrounds rad50Δ or sae2Δ are not lethal when combined with exo1Δ sgs1Δ [27] , [28] . Even though we were able to construct ctp1Δ rqh1Δ and mre11Δ rqh1Δ strains , these double mutants were extremely sick and therefore unsuitable for resection assays . We have previously established that the localization of RPA to ssDNA is reduced in the absence of Ctp1 or Mre11 [19] . As Ku deletion improves the growth and DNA damage survival of ctp1Δ mutants [19] , [46] , and significantly increases resection initiation performed by Exo1 in the absence of Ctp1 ( Figure 2F ) , we investigated whether Ku deletion also enhances RPA localization at a DSB in ctp1Δ cells . We used a previously established chromatin immunoprecipitation ( ChIP ) protocol to measure RPA enrichment at the HO-induced DSB . The ChIP procedure detects RPA associated with a 200 bp region that overlaps ∼85% with the region used to measure resection at 35 bp from the break , which allows us to directly compare the initiation of resection with protein enrichment . Using RPA ChIP , we confirmed our earlier studies showing a severe reduction in the localization of RPA to the DSB in ctp1Δ cells [19] , and furthermore found that RPA localization was significantly increased in ctp1Δ pku80Δ cells compared to the ctp1Δ control , although not to the wild type level ( Figure 3 ) . Thus , deleting Ku does not completely restore RPA localization at a DSB in ctp1Δ cells , which is consistent with the failure of pku80Δ to fully suppress ctp1Δ at higher doses of DNA damage [19] , [46] . Very similar genetic interactions and effects on RPA localization were observed when Ku is deleted in mre11Δ cells ( data not shown ) . Having found that pku80Δ suppression of ctp1Δ DNA damage sensitivity correlates with increased DSB resection and RPA localization at a DSB , we investigated whether other mutations that impair NHEJ would suppress ctp1Δ . We found that deleting Lig4 or Xlf1 did not rescue the slow growth and genotoxin sensitivity of ctp1Δ cells , indicating that it is the DSB-binding capacity of Ku , as opposed to NHEJ per se , that blocks Exo1-dependent repair of DSBs in ctp1Δ cells ( Figure 4A ) . From these data we hypothesized that Ctp1 might be required to release Ku or to prevent it from binding DNA ends . To test this idea , we carried out ChIP studies to measure HA-tagged Ku70 association with an HO-induced DSB . In wild type cells we are unable to detect Ku enrichment near a DSB , indicating that any association this high-affinity DNA binding factor might have with the DSB must be extremely transient . In contrast , ChIP analysis detected a statistically significant Ku70 enrichment near the DSB in mre11Δ cells ( Figure 4B ) , consistent with recent results obtained in S . cerevisiae [26] , [59] , [60] . Interestingly , we also observed a statistically significant increase in Ku enrichment in ctp1Δ cells , although not as high as in mre11Δ cells ( Figure 4B ) . No enrichment of Ku at the DSB was observed in exo1Δ cells ( Figure 4B ) , indicating that extended resection is not required to prevent Ku from associating with DNA ends . Combined with the genetic suppression of mrnΔ and ctp1Δ phenotypes by deleting Ku , these results suggest that MRN complex and Ctp1 are required to release the Ku heterodimer from DNA ends , and likely can prevent it from rebinding by creating resected DNA ends that are poor binding substrates for Ku . As seen for ctp1Δ mutants in fission yeast , Ku also accumulates at DSBs in sae2Δ mutants in S . cerevisiae , although the effect of a sae2Δ mutation is negligible in comparison to mrxΔ mutations [26] , [59] , [60] . These results suggested an epistatic relationship between Ctp1 and Mre11 . Indeed , an IR survival assay confirmed that the ctp1Δ mre11Δ double mutant was no more sensitive than either single mutant ( Figure S3A ) . As seen for the ctp1Δ and mre11Δ single mutants , deleting Ku made ctp1Δ mre11Δ cells more resistant to IR , and this suppression depended entirely on Exo1 ( Figure S3A ) . In Schizo . pombe , the mre11-H134S mutation that ablates Mre11 nuclease activity causes acute sensitivity to ionizing radiation , although not quite to the level of mre11Δ single mutants [46] . Mre11 nuclease activity is similarly critical for IR resistance in mammalian cells [49] . Mre11 nuclease activity appears to be much less important for IR resistance in S . cerevisiae , although defects are detected at high doses of IR [61] . Presented with these facts , we suspected that Mre11 nuclease activity might be important for resection in Schizo . pombe , and this might explain why the nuclease activity is required for radioresistance . Surprisingly , we found that the mre11-H134S nuclease-defective strain was not significantly defective in our resection assays ( Figure 5A ) . Thus , Mre11 nuclease activity is not required for DSB resection in budding yeast or fission yeast . Some other DSB repair defect must therefore underlie the IR sensitivity of the Schizo . pombe mre11-H134S mutant . When we measured the effect of deleting either Ctp1 or Mre11 on the enrichment of Ku at the DSB , we observed a higher enrichment of Ku in the mre11Δ than the ctp1Δ strain ( Figure 4B , p-value≤0 . 05 ) . As resection in these two strains is similarly affected , we hypothesized that Mre11 has an additional function that prevents Ku from binding or remaining attached to a DSB , compared to Ctp1 . To test whether the additional function of Mre11 depends on its nuclease activity , we investigated Ku binding to the HO-induced DSB in mre11-H134S cells . We detected significant Ku enrichment , at a level similar to ctp1Δ cells ( Figure 5B ) . These data are consistent with the suppression of mre11-H134S growth defects and DNA damage sensitive phenotypes by Ku deletion [46] . Thus , Ku accumulates at DSBs in mre11-H134S cells even though resection is proficient . These data appear to contrast with studies in S . cerevisiae that showed a negligible role for the Mre11 nuclease activity in regulating Ku accumulation at DNA ends [26] , [59] . As discussed below , our data suggest that Mre11 nuclease activity and Ctp1 are both required to release Ku from DSBs . In agreement with this possibility , we found that a ctp1Δ mre11-H134S double mutant was no more sensitive to IR than either single mutant ( Figure S3B ) . Resection appears to be proficient in mre11-H134S cells ( Figure 5A ) , and yet they are sensitive to IR and other DNA damaging agents . The IR sensitivity of mre11-H134S cells is suppressed by deleting Ku [46] , and Ku accumulation at DSBs is elevated in mre11-H134S cells ( Figure 5B ) . In an attempt to explain these results , we analyzed RPA localization at the HO break in mre11-H134S cells . Despite efficient resection , we found there was a significant defect in RPA enrichment in mre11-H134S cells ( Figure 5C ) . Interestingly , just as seen for ctp1Δ and mre11Δ mutants , deletion of Ku increased RPA localization at the DSB in mre11-H134S cells . In fact , RPA enrichment at the DSB in mre11-H134S pku80Δ cells was not significantly different from wild type ( Figure 5C ) . A similar relationship was observed when RPA enrichment was measured 2 kb from the DSB , although in this case deleting Ku in mre11-H134S cells did not fully restore the RPA signal to the wild type level ( Figure S4A ) . In view of the unexpected relationship between resection and RPA enrichment in mre11-H134S cells , we decided to measure a separate RPA-dependent response in these cells . The RPA-ssDNA filament recruits the heterodimeric Rad3-Rad26 checkpoint kinase that is orthologous to mammalian ATR-ATRIP complex [16] . Rad3 phosphorylates the checkpoint effector kinase Chk1 [62] . If RPA localization is reduced in mre11-H134S cells , Chk1 phosphorylation should be impaired as well . We used a well-established immunoblot assay to measure Rad3-dependent Chk1 phosphorylation in mre11-H134S and mre11-H134S pku80Δ cells [19] , [57] , [63] . Rather than creating an irreparable break with the HO endonuclease , we used IR to create DSBs that can be repaired by HR . Our analysis revealed a strong reduction in Chk1 phosphorylation in mre11-H134S cells compared to wild type 30 minutes after irradiation with 90 Gy ( Figure 5D ) . This result is consistent with the reduction in RPA localization at the HO-induced DSB in these cells ( Figure 5C ) . Elimination of Ku in mre11-H134S cells significantly increased Chk1 phosphorylation , although not to the level of wild type or pku80Δ cells ( Figure 5D ) . The failure to fully restore Chk1 phosphorylation in mre11-H134S pku80Δ cells may reflect an inability to completely suppress defects in RPA localization , as suggested by ChIP assays performed at 2 kb from an HO-induced DSB ( Figure S4A ) , or perhaps Mre11 nuclease activity has an additional role in promoting Chk1 activation at IR-induced DSBs . If resection is proficient in mre11-H134S cells , why are RPA enrichment and checkpoint signaling reduced ? We considered whether the presence of other proteins on the DNA might interfere with RPA localization . Ku was a candidate because structural studies showed that the Ku complex can translocate along duplex DNA to form a beads-on-a-string configuration [64] , [65] . However , in mre11-H134S cells we could not detect Ku70 at 2 kb from the HO break ( data not shown ) , even though RPA enrichment was reduced at this site ( Figure S4A ) . Another candidate was the MRN complex . Our previous ChIP studies revealed that Mre11 , Nbs1 and Ctp1 localize at the HO break in mre11-H134S mutant cells [46] . In fact , the ChIP signals appeared to be enhanced relative to wild type . Indeed , quantitative ChIP analysis revealed very strong enrichment of Mre11-H134S at the DSB ( Figure 6 ) . Crucially , this enrichment was largely ablated by deleting Ku ( Figure 6 ) . The same effect was observed at 2 kb from break ( Figure S4B ) . Ku therefore appears to stabilize Mre11-H134S complex binding at DSBs , which likely interferes with efficient assembly of the RPA-ssDNA filament . Our data suggest that Mre11 endonuclease activity and Ctp1 are required to effectively release Ku and MRN complex from DSBs . As genetic deletion of Ku substantially alleviates the poor growth and genotoxin sensitivity of ctp1Δ and mre11-H134S mutants , not only to IR and CPT , but also to compounds such as hydroxyurea ( HU ) and methyl methanesulfonate ( MMS ) that indirectly cause DSBs ( Figure 7A ) , our data suggest that Ku release is a critical function of Mre11 endonuclease activity and Ctp1 . But why is the release of Ku crucial if NHEJ is an alternative mechanism for the repair of DSBs ? This question is particularly relevant to mammalian cells in which NHEJ appears to be responsible for the bulk of DSB repair . An answer to this question may lie with the repair of broken replication forks , which are likely the most abundant form of spontaneous DSBs . Unlike the two-ended DSBs made by ionizing radiation or by the HO endonuclease , broken replication forks create one-ended DSBs that cannot be repaired by NHEJ [66] . Mre11 and Ctp1 may therefore function to protect cells from the detrimental effects of Ku DNA end-binding on the repair of one-ended DSBs . To test this hypothesis , we made use of the mating type locus in which a stalled replication fork creates a single one-ended rather than two-ended DSB ( Figure 7B ) [67]–[69] . Using an altered version of the mating type locus , which lacks homologous donor sequences for repair by gene conversion ( mat1-P ( 2 , 3Δ ) or “donorless” strain ) , we previously determined that Rad50 , one of the members of the MRN complex , is required for survival of this strain [70] . Similarly , we found that the ctp1Δ mat1-P ( 2 , 3Δ ) cells grew extremely poorly ( Figure 7C ) . The defect was similar but slightly weaker in mre11-H134S mat1-P ( 2 , 3Δ ) cells ( Figure 7C ) . Importantly , deletion of Ku substantially enhanced growth in these genetic backgrounds ( Figure 7C ) . From these results we conclude that Ctp1 and Mre11 nuclease activity are required to overcome the inhibitory effects of Ku on HR repair of one-ended DSBs formed by replication fork collapse in S-phase .
Ctp1 and Mre11 are critical to efficiently initiate resection of DSBs ( Figure 2A ) . Extended resection measured at 3 . 1 kb from the DSB is mainly achieved through the activity of Exo1 ( Figure 2D ) . Only in the absence of Exo1 do we uncover a modest contribution of Rqh1 ( Figure 2D ) . These data are generally consistent with studies in budding yeast using Southern blot-based assay systems . The role of Sae2 was initially detected in an exo1Δ sgs1Δ background , indicating that the initial ∼100 bp cleavage is made by Mre11 complex and Sae2 [27] , [28] . Ensuing resection up to ∼5 kb from the DSB is slowed in the absence of Exo1 or Sgs1 , although it is necessary to eliminate both Exo1 and Sgs1 to effectively block long-range resection [27] , [28] . However , resection in the absence of Sae2 is only mildly affected , suggesting that Exo1 or Sgs1-dependent activities can effectively initiate resection , and this likely explains why sae2Δ mutants are only mildly radiosensitive compared to MRX mutants [6] , [26] , [39] , [41] . In an effort to completely eliminate resection , we tried to generate ctp1Δ exo1Δ rqh1Δ triple mutants . We found that the triple mutant is unviable ( Figure S2 ) . We were also unable to analyze resection in ctp1Δ rqh1Δ mutants , as the break induction kinetics in this extremely sick strain is slower than that of all other strains , making comparisons between strains impossible . Similar synthetic growth defects and lethality have been observed in budding yeast [27] . Deleting Ku substantially rescues the genotoxin sensitivity of mre11Δ , mre11-H134S , rad50Δ and ctp1Δ mutants [19] , [46] , [58] , [71] . This rescue requires Exo1 , indicating that Ku prevents Exo1 from substituting for the MRN complex and Ctp1 . Indeed , deleting Ku in ctp1Δ cells enhances Exo1-dependent initiation of resection ( Figure 2F ) . Similar results were observed in budding yeast , where deleting Ku suppressed the initial resection delay in sae2Δ cells and improved resection in an Exo1-dependent manner in a rad50Δ mutant [26] , [72] . Additionally , Exo1 was shown to be involved in both the initiation and extension of resection at a VMA1 derived homing endonuclease ( VDE ) -induced DSB during meiosis in budding yeast [73] . We observed no defect in ssDNA formation at 35 bp or 3 . 1 kb from the DSB in the mre11-H134S mutant , consistent with observations in S . cerevisiae [50] . However , if Mre11 nuclease activity is not required for resection , then why is Exo1 required for robust growth and radioresistance in mre11-H134S cells , both in the presence or absence of Pku80 ? These data suggest that Exo1 might be important for resection in the absence of Mre11 nuclease activity . In budding yeast cells lacking Mre11 nuclease activity , Exo1 is not required for resection , but resection initiation is substantially impaired in the absence of Sgs1 and completely abrogated in when both Sgs1 and Exo1 are eliminated [26] , [72] . These observations suggest that resection activities that are normally involved in extended resection can be essential for resection initiation in the absence of Mre11 nuclease activity . Ku is enriched at DNA ends in the absence of Mre11 nuclease activity or Ctp1 ( Figure 4B and Figure 5B ) . While Ku's affinity for blunt DSBs can explain this effect in resection-defective ctp1Δ cells , the same explanation cannot easily account for Ku enrichment in resection-proficient mre11-H134S cells ( Figure 2A and Figure 5A ) . The requirement for both Mre11 nuclease activity and Ctp1 to release Ku may have some analogy to the release of Rec12Spo11 that is covalently bound to DSBs in meiosis , although removal of Rec12Spo11 is required for efficient resection [44] . Mre11 nuclease activity and Ctp1 are required to release Rec12 bound to oligonucleotides that range roughly from 13 to 29 nucleotides [42] , [43] . Our resection assay shows that while Mre11-H134S and Ku still bind DNA in mre11-H134S cells , resection at 35 bp is comparable to wild type . As the Ku heterodimer can bind to dsDNA regions as short as 25 bp [74] , one possibility is that there is a region of dsDNA adjacent to the HO break , which is smaller than 35 bp , that retains Ku . We are currently investigating this idea . The necessity to release Ku from the DNA to allow HR repair can explain the critical requirement for Ctp1 and Mre11 nuclease activity in HR in fission yeast [19] , [46] . In contrast , removing Sae2 or the Mre11 nuclease function in budding yeast has little effect on resection or the release of Ku [6] , [26]–[28] , [59] , which explains why they are largely dispensable for HR in budding yeast [39] , [41] , [47] , [48] . We observe a stronger enrichment of Ku at the DNA end in the absence of Mre11 than in the absence of either Ctp1 or Mre11 nuclease activity alone ( Figure 4B ) . This increased enrichment of Ku in mre11Δ cells can be explained either by decreased competition between MRN and Ku for DNA ends , or by the combined lack of Mre11 nuclease activity and Ctp1 , as Ctp1 depends on Mre11 for recruitment to DSBs [19] . RPA localization to naked ssDNA is near diffusion limited when assayed in vitro with purified components [75] , but our data suggest that in vivo , localization of RPA to ssDNA can be influenced by other factors . Interestingly , the reduced localization of RPA observed in mre11-H134S cells is reflected in human and murine cells expressing nuclease dead Mre11 , as RPA foci formation following IR was strongly reduced in these mutant cells compared to wild type [49] , [76] . This suggests that the function of Mre11 nuclease activity to enable RPA localization is conserved in these species . Reduced RPA localization at a DSB in Schizo . pombe mre11-H134S cells depends on Ku ( Figure 5C ) . As we have shown , both Ku and Mre11-H134S are enriched at the DNA end in mre11-H134S cells ( Figure 5B and Figure 6 ) . Other proteins occupying the ssDNA , thereby preventing RPA from binding , might therefore explain the reduced RPA localization observed in mre11-H134S cells . One candidate is the MRN complex itself , as the Mre11-H134S ChIP signal at a DSB is high in an mre11-H134S background , but is reduced when Ku is deleted ( Figure 6 ) . It is of interest to note that this observation strongly suggests that Ku and MRN can bind to the same DNA end . Our data suggest that Ku either traps MRN to the DNA , or that it inhibits the action of an unidentified nuclease that enables the release of MRN from the DNA . A possible candidate is Exo1 , as rescue of the poor growth and genotoxin sensitivity of mre11-H134S mutants by deletion of Pku80 is dependent on Exo1 [46] . However , the most likely candidate is Ctp1 , as deletion of Ctp1 increases and prolongs MRN binding to DNA at a HO-induced DSB in wild type cells [57] . Similar effects on MRX foci formation were observed in the absence of Sae2 at a HO-induced DSB in budding yeast [77] . RPA localization in ctp1Δ pku80Δ and mre11Δ pku80Δ mutant cells is significantly lower than that observed in the pku80Δ mutant ( Figure 3 , data not shown for mre11Δ pku80Δ ) , while resection in ctp1Δ pku80Δ , mre11Δ pku80Δ and pku80Δ mutants are indistinguishable ( Figure 2E ) . This suggests that other proteins facilitate RPA localization at ssDNA in vivo . One possibility is Mre11 , as several studies in mammalian cells have shown an interaction between Mre11 and RPA [78]–[80] . We are currently investigating this interaction in Schizo . pombe . Our findings provide evidence for some of the underlying causes that result in the observed slow growth and DNA damage sensitivity phenotypes of mre11-H134S and ctp1Δ mutants in Schizo . pombe . DSBs can be recognized by MRN alone , Ku alone or both MRN and Ku . DSBs recognized by MRN alone do not provide difficulties for repair by homologous recombination . On the other hand , when the DSBs are recognized by Ku alone , the breaks may be repaired by NHEJ , or alternatively , repair is prevented by inhibiting the initiation of resection . Our observations that both Ku and MRN can be enriched at DNA ends in mre11-H134S and ctp1Δ cells ( Figure 4B , Figure 5B , Figure 6 , and Figure S4B and [57] ) , and that stabilization of MRN binding depends on the enrichment of Ku ( Figure 6 and Figure S4B ) suggest that there are a significant number of DNA ends that are recognized by both protein complexes at the same time . Based on our results , we propose that MRN recruits Ctp1 to these DSBs , after which the combined activities of Ctp1 and Mre11 nuclease ensure the release of Ku from the DNA end . The same activities are required for the release of the MRN complex from the DSB . Resection can be initiated by Ctp1 and extended by Exo1 , which can function independently of Ctp1 . Extension of resection by Exo1 is however likely not absolutely required , as exo1Δ cells are not sensitive to DSB inducing agents [19] . RPA bound to the generated ssDNA overhangs will recruit Rad3-Rad26 , resulting in activation the checkpoint pathway by phosphorylation of Chk1 ( see Figure 8 ) . In the absence of Ctp1 however , Mre11 nuclease activity is not efficient in releasing Ku and MRN from DNA ends and resection is often not initiated . Release of Ku from some DNA ends may allow Exo1 to substitute for Ctp1 in the initiation of resection . The strongly decreased RPA binding observed in ctp1Δ mutant cells suggests that this step is highly inefficient ( see Figure 8 ) . This is supported by our observation that a single collapsed replication fork is lethal in a ctp1Δ mutant ( Figure 7 ) , which likely is the result of a combination of reduced resection and defects in release of Ku and the MRN complex . On the other hand , when Mre11 nuclease activity is absent , Ctp1 can still be recruited [46] and initiate resection at all DNA ends . However , Ctp1 cannot release Ku from all DNA ends in the absence of Mre11 nuclease activity , which will create a situation where some DNA ends are bound by Ku and MRN , while other DNA ends are free from protein complexes ( exemplified in Figure 8 ) . We envision that Ctp1 can initiate resection up to several hundred base pairs , allowing Exo1 to extend resection on the 5′ recessed DNA ends despite the binding of Ku to the DNA end . This explains why resection is unaffected in mre11-H134S cells . The defective release of the MRN complex in mre11-H134S cells in the presence of Ku results in reduced RPA localization , less efficient checkpoint activation , and inefficient repair of the DSB ( see Figure 8 ) . On the other hand , RPA is able to bind to the free ssDNA overhangs resulting in repair of the DSB by homologous recombination ( see Figure 8 ) , which provides an explanation for the observation that a single collapsed replication fork decreases cell viability , but is not lethal in mre11-H134S cells ( Figure 7 ) . The model in Figure 8 does not propose an explanation for the observed small decrease in resection initiation in the absence of Ku . This observation may reflect a MRN-recruiting function of Ku . However , the physiological significance of this defect is unclear , as pku80Δ mutants appear to be fully proficient at RPA binding and HR repair of DSBs ( Figure 3 and [5] ) . An interaction between Mre11 and Pku80 has been detected in S . cerevisiae [81] and we are currently investigating this interaction in Schizo . pombe . Our data clearly show that DNA end resection is insufficient to initiate HR repair of DSBs — in addition to generation of ssDNA overhangs through resection initiated by Ctp1 and the MRN complex , Ctp1 and Mre11 nuclease activity must release MRN and Ku complexes to allow efficient RPA localization at resected DSBs . Therefore , our data indicate that while RPA localization does not always reflect the resection status of DSBs , it always reflects the ability to repair: inefficient RPA localization despite proficient resection leads to inefficient repair and therefore sensitivity to DSB inducing agents . We hypothesize that the relationships between MRN , Ctp1 and Ku are conserved in mammals , and this may explain why Mre11 endonuclease activity and CtIP are critical for cellular viability in mice [49] , [82] . Interestingly , our data describing the functions of Mre11 , Ctp1 and Exo1 in resection of DSBs in mitotic cells are reminiscent of a study showing the requirements of the same proteins for repair of an I-Sce-induced DSB during meiosis [83] . This study showed that Ctp1 is required for repair in the presence of the MRN complex , while Exo1 is required when MRN is absent [83] . Our resection data provide a possible explanation for this observation: in the presence of MRN , Ctp1 is recruited to DSBs and initiates resection . In the absence of MRN , Ctp1 is no longer recruited and Exo1 will initiate resection . There are however also clear differences: in meiotic cells MRN appears to inhibit Exo1 , while in mitotic cells it is Ku that inhibits Exo1 activity . The lack of an inhibitory effect of Ku on repair of DSBs in meiotic cells would bypass the need for Mre11 nuclease-dependent release of Ku from DNA ends . Indeed , Mre11 nuclease activity is not required for repair of an I-Sce-induced DSB during meiosis in fission yeast [83] , while it is required for repair of IR-induced DSBs in mitotic cells [46] . Curiously , in budding yeast Mre11 nuclease activity is required for resection of a VDE-induced DSB during meiosis [73] . The reasons for the different requirements for Mre11 nuclease activity between fission and budding yeast , and mitosis and meiosis remain elusive . While our studies provide important insights into the interplay of NHEJ and HR factors at DSBs , and point out some key similarities and differences between Schizo . pombe and S . cerevisiae , they also raise new questions . On the face of it , the relationship between Ctp1 , Mre11 and Ku is counter-intuitive , as NHEJ would be expected to be critical for survival of DSBs in these HR-defective strains . However , we and others found that deleting two other essential NHEJ factors , Xlf1/Cernunnos or Ligase IV , does not suppress mre11 or ctp1Δ mutants [58] , and thus inactivating NHEJ is insufficient for suppression . Instead , the high-affinity binding of Ku to DNA is sufficient to inhibit HR in these mutants , even if later steps of NHEJ are blocked . These data point to a competition between the Ku heterodimer and the MRN-Ctp1 unit to determine whether NHEJ or HR repairs DSBs . Ku might bind DNA ends first and then is subsequently released from the DNA through the DNA end processing activities provided by the MRN complex and Ctp1 . Ku has a very high affinity for DNA ends , and yet clearly NHEJ cannot efficiently repair DSBs in mre11-H134S or ctp1Δ mutants . Why this is so remains a mystery . It is possible that the MRN complex promotes NHEJ repair of chromosomal DSBs in fission yeast , as the MRX complex does in budding yeast [84] , [85] , but the failure to release the MRN complex in mre11-H134S and ctp1Δ mutants likely interferes with the completion of NHEJ . The requirement for the active release of Ku , a protein complex showing very high affinity for and slow dissociation from DNA ends [60] , [86] , becomes more evident when NHEJ cannot be considered as an alternative pathway for repair , such as is the case with one-ended DSBs that can arise from stalled or broken replication forks .
All strains used in this study harbor a single HO cleavage site at the arg3 locus and carry a gene coding for a thiamine ( B1 ) -repressible HO endonuclease as previously described [53] . Strains are listed in Table S1 . Cells were grown in YES unless stated otherwise . Plate survival assays were performed using 5× serial dilutions . For IR survival cells were exposed to 90 Gy IR using a 137Cs source . Plates were analyzed after an incubation period of three days at 30°C . For HO endonuclease induction pre-cultures were grown up to 28 hours , washed three times and subsequently cultured in medium without B1 . Samples were taken hourly , starting after 12 hours . Immunoblotting was performed as previously described [19] . Immunoblots were quantified with ImageJ software ( http://rsb . info . nih . gov/ij/ ) . Error bars represent the standard deviation of three independent experiments . 10 ml of cells from a saturated culture were resuspended in 200 µl of genomic DNA extraction buffer ( 2% Triton X100 , 1% SDS , 100 mM NaCl , 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA ) . After addition of 200 µl Phenol∶Chloroform∶Isoamylalcohol ( Sigma Life Sciences ) and glass beads , cells were lysed by vortexing 5 minutes at 4°C . DNA was then prepared by standard phenol/chloroform extraction . RNA was removed by addition of RNase A ( 1 hour at 37°C ) . 35 ng of DNA was used for each quantitative real-time PCR reaction , and all reactions were performed in triplicate . We used the iQ SYBR Green Supermix with the Chromo4 Real-Time PCR Detection System ( Bio-Rad Laboratories ) . The following program was used: 95°C for 3 min , 37× ( 95°C 15 s , 60°C 15 s , 72°C 30 s ) . Primer sequences are listed in Table S2 . To determine the efficiency of the HO break induction , we performed standard curves with primer pairs 1 and 2 . We then calculated the fraction of uncut DNA by measuring the total amount of DNA ( primer pair 1 ) and the amount of uncut DNA ( primer pair 2 ) ( see Figure S1A ) . To calculate the percentage of ssDNA at each time point we used the method described by [51] with the following exception: digestion of the DNA was performed with ApoI . Resection was measured at two distances from the break based on the location of the ApoI restriction site: 35 bp ( primer pair 3 ) and 3 . 1 kb ( primer pair 4 ) respectively ( see Figure S1B ) . To determine the overall rate of resection in a wild type strain , we measured resection at three addition distances from the break: 270 bp ( primer pair 5 ) , 5 . 4 kb ( primer pair 6 ) and 9 . 4 kb ( primer pair 7 ) . The HO endonuclease is induced at different times after removal of B1 depending on the strain used . To directly compare the resection between the different strains , we overlay the graphs showing the percentage of uncut DNA ( see Figure S5 ) . Kinetics of resection can be found in Figure S6 . Table S3 shows the range of the percentage of uncut DNA for each strain at the “−B1” point as used in Figure 2 . ChIP assays were performed as previously described [53] with the following modifications: IgG Sepharose 6 Fast-Flow beads ( GE Healthcare ) were used to retrieve TAP-tagged Rad11 and mouse anti-HA antibody ( Roche Applied Science ) conjugated to anti-mouse magnetic beads ( Dynal/Invitrogen ) were used to pull down Pku70-HA , while mouse anti-myc antibody ( Roche Applied Science ) conjugated to anti-mouse magnetic beads ( Dynal/Invitrogen ) were used to pull down Mre11-H134S-myc . Primers used to measure protein enrichment at 0 . 2 kb from the break are listed in [53] . Purelink PCR Purification Kit ( Invitrogen ) was used to recover DNA . In all ChIP assays cells were checked for HO break induction by measuring the percentage of uncut DNA as described above . The induced timepoint shown in all the graphs depicting ChIP data is the timepoint at which we observe maximal enrichment of the protein of interest in the strain with the highest enrichment: for RPA: wild type , for Mre11-H134S: mre11-H134S mutant and for Ku: mre11Δ mutant . | A double-strand break ( DSB ) is a devastating form of DNA damage . Fortunately , cells are equipped with two DSB repair pathways: homologous recombination ( HR ) and nonhomologous end-joining ( NHEJ ) . The Mre11-Rad50-Nbs1 ( MRN ) protein complex recognizes DSBs and initiates HR repair . The Mre11 subunit harbors a nuclease domain that is essential for repair in fission yeast and mammals , although the function is unknown . Here we show that Mre11 nuclease activity is required to release the Ku complex from DNA ends in fission yeast . While the initiation of repair , i . e . the generation of single-stranded DNA ( ssDNA ) overhangs in Mre11-nuclease dead mutants , is unaffected , we find that an essential downstream step involving the localization of Replication Protein A ( RPA ) to ssDNA is substantially decreased due to the inability to release Ku and MRN from the DNA end . In contrast , a DNA processing factor called Ctp1 , which binds to Nbs1 , is essential for the initiation of repair as well as the release of Ku and MRN from DNA ends . Importantly , we find that efficient localization of RPA , which is essential for efficient DSB repair by HR , requires the release of Ku and MRN from the DNA by the combined action of Mre11 nuclease and Ctp1 . | [
"Abstract",
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"biology"
] | 2011 | Release of Ku and MRN from DNA Ends by Mre11 Nuclease Activity and Ctp1 Is Required for Homologous Recombination Repair of Double-Strand Breaks |
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